Patent Publication Number: US-2021177359-A1

Title: System and method for determining blood disorders for a blood type using ppg technology

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority under 35 U.S.C. § 120 as a continuation application to U.S. patent application Ser. No. 16/019,518 entitled SYSTEM AND METHOD FOR BLOOD TYPING USING PPG TECHNOLOGY filed Jun. 26, 2018, which is a divisional application to U.S. patent application Ser. No. 15/867,632 entitled “SYSTEM AND METHOD FOR BLOOD TYPING USING PPG TECHNOLOGY,” filed Jan. 10, 2018, now U.S. Pat. No. 10,039,500 issued Aug. 7, 2018, and both of which are hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 15/867,632 claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/613,388 entitled “SYSTEM AND METHOD FOR INFECTION DISCRIMINATION USING PPG TECHNOLOGY,” filed Jan. 3, 2018, and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part to U.S. patent application Ser. No. 15/859,147 entitled “VEHICULAR HEALTH MONITORING SYSTEM AND METHOD,” filed Dec. 29, 2017, now U.S. Pat. No. 10,194,871 issued Feb. 5, 2019 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/811,479 entitled “SYSTEM AND METHOD FOR A BIOSENSOR INTEGRATED IN A VEHICLE,” filed Nov. 13, 2017, now U.S. Pat. No. 10,238,346 issued Mar. 26, 2019, and hereby expressly incorporated by reference herein, which claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/490,813 entitled “SYSTEM AND METHOD FOR HEALTH MONITORING USING A NON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Apr. 18, 2017, now U.S. Pat. No. 9,980,676 issued May 29, 2018 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 15/811,479 entitled “SYSTEM AND METHOD FOR A BIOSENSOR INTEGRATED IN A VEHICLE,” filed Nov. 13, 2017, now U.S. Pat. No. 10,238,346 issued Mar. 26, 2019 further claims priority as a continuation in part application to U.S. patent application Ser. No. 15/489,391 entitled “SYSTEM AND METHOD FOR A BIOSENSOR MONITORING AND TRACKING BAND,” filed Apr. 17, 2017, now U.S. Pat. No. 9,974,451 issued May 22, 2018, and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/485,816 entitled “SYSTEM AND METHOD FOR A DRUG DELIVERY AND BIOSENSOR PATCH,” filed Apr. 12, 2017, now U.S. Pat. No. 10,155,087 issued Dec. 18, 2018 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/718,721 entitled “SYSTEM AND METHOD FOR MONITORING NITRIC OXIDE LEVELS USING A NON-INVASIVE, MULTI-BAND BIOSENSOR,” filed Sep. 28, 2017, now U.S. Pat. No. 10,517,515 issued Dec. 31, 2019 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/804,581 entitled “SYSTEM AND METHOD FOR HEALTH MONITORING INCLUDING A USER DEVICE AND BIOSENSOR,” filed Nov. 6, 2017, now U.S. Pat. No. 10,231,674 issued Mar. 19, 2019 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/462,700 entitled “SYSTEM AND METHOD FOR ATOMIZING AND MONITORING A DRUG CARTRIDGE DURING INHALATION TREATMENTS filed Mar. 17, 2017, now U.S. Pat. No. 10,500,354 issued Dec. 10, 2019, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/457,138 entitled “SYSTEM AND METHOD FOR ATOMIZING AND MONITORING A DRUG CARTRIDGE DURING INHALATION TREATMENTS,” filed Feb. 9, 2017, and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. Utility application Ser. No. 15/958,620 entitled “SYSTEM AND METHOD FOR DETECTING A SEPSIS CONDITION,” filed Apr. 20, 2018, now U.S. Pat. No. 10,524,720 issued Jan. 7, 2020 which is hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part application to U.S. patent application Ser. No. 15/400,916 entitled “SYSTEM AND METHOD FOR HEALTH MONITORING INCLUDING A REMOTE DEVICE,” filed Jan. 6, 2017, now U.S. Pat. No. 10,750,981 issued Aug. 25, 2020 and hereby expressly incorporated by reference herein. 
     U.S. patent application Ser. No. 16/019,518 claims priority under 35 U.S.C. § 120 as a continuation in part to U.S. patent application Ser. No. 14/866,500 entitled “SYSTEM AND METHOD FOR GLUCOSE MONITORING,” filed Sep. 25, 2015, now U.S. Pat. No. 10,321,860 issued Jun. 18, 2019 and hereby expressly incorporated by reference herein. 
    
    
     FIELD 
     This application relates to systems and methods of non-invasive health monitoring, and in particular to systems and methods for non-invasively detecting a blood disorder using PPG technology. 
     BACKGROUND 
     A person&#39;s vitals, such as temperature, blood oxygen levels, respiration rate, relative blood pressure, etc., may need to be monitored periodically typically using one or more instruments. For example, instruments for obtaining vitals of a user include blood pressure cuffs, thermometers, SpO 2  measurement devices, glucose level meters, etc. Often, multiple instruments must be used to obtain vitals of a person. This monitoring process is time consuming, inconvenient and is not always continuous. 
     In addition, detection of substances and measurement of concentration level or indicators of various substances in a user&#39;s blood stream is important in health monitoring. Currently, detection of concentration levels of blood substances is performed by drawing blood from a blood vessel using a needle and syringe. The blood sample is then transported to a lab for analysis. This type of monitoring is invasive, non-continuous and time consuming. 
     Furthermore, detection of a patient&#39;s blood type (also known as blood group) is vital prior to a safe blood transfusion. Determination of blood groups is a vital factor for overall healthcare needs. The human race by nature has one of a plurality of blood groups, e.g. such as A, B, AB and O. During blood transfusion any mismatch can lead to great harm or possible the death of a person. Hence it is very important to identify the blood group of a person or animal. 
     The blood type notations (e.g., A, B, AB, O) indicate the antigens present on the surface of red blood cells. For example, the ABO and Rh factor indicate different types of antigens on the surface of red blood cells. Current blood typing procedures include drawing a blood sample and testing the blood sample using different reagents. For example, three separate tests are performed using different reagents with either A, B or Rh antibodies. The reagents attach to the antigens on the patient&#39;s red blood cells. The blood will agglutinate when the antigens in the patient&#39;s blood match the antibodies in the test tube. The blood type may thus be discerned. However, these known blood typing procedures require drawing blood from a blood vessel using a needle and syringe. The blood sample must then be transported to a lab for the analysis. So, this type of monitoring is invasive and time consuming, especially in an emergency situation when no lab is present. 
     One current non-invasive method is known for measuring the oxygen saturation of blood using pulse oximeters. Pulse oximeters detect oxygen saturation of hemoglobin by using, e.g., spectrophotometry to determine spectral absorbencies and determining concentration levels of oxygen based on Beer-Lambert law principles. In addition, pulse oximetry may use photoplethysmography (PPG) methods for the assessment of oxygen saturation in pulsatile blood flow. The subject&#39;s skin at a ‘measurement location’ is illuminated with two distinct wavelengths of light and the relative absorbance at each of the wavelengths is determined. For example, a wavelength in the visible red spectrum (for example, at 660 nm) has an extinction coefficient of hemoglobin that exceeds the extinction coefficient of oxihemoglobin. At a wavelength in the near infrared spectrum (for example, at 940 nm), the extinction coefficient of oxihemoglobin exceeds the extinction coefficient of hemoglobin. 
     The pulse oximeter filters the absorbance of the pulsatile fraction of the blood, i.e. that due to arterial blood (AC components), from the constant absorbance by nonpulsatile venous or capillary blood and other tissue pigments (DC components), to eliminate the effect of tissue absorbance to measure the oxygen saturation of arterial blood. Such PPG techniques are heretofore been limited to determining oxygen saturation. 
     As such, there is a need for a continuous and non-invasive health monitoring system and method that measures user vitals and monitors concentration levels or indicators of one or more substances in blood flow as well as determines blood type of a patient. 
     SUMMARY 
     According to a first aspect, A device includes at least one memory device that stores at least a first photoplethysmography (PPG) signal obtained from a first wavelength of light that is reflected from or transmitted through skin tissue of a user and a second PPG signal obtained at a second wavelength of light that is reflected from or transmitted through the skin tissue of the user. The device also includes at least one processing circuit configured to determine a first R value of the user using the first PPG signal and the second PPG signal, wherein the first R value is obtained using a first alternating current (AC) signal component in the first PPG signal and a second AC component in the second PPG signal; compare the first R value of the user to a normal range of R values for a blood type of the user; and determine an abnormal condition of the user in response to the comparison. 
     According to second aspect, a device includes at least one memory device that stores at least a first photoplethysmography (PPG) signal obtained from a first wavelength of light reflected from or transmitted through skin tissue of a user. The device also includes at least one processing circuit configured to determine a color of blood of the user using at least the first PPG signal and determine an abnormal condition that affects red blood cells of the user in response to the color of the blood of the user. 
     According to a third aspect, a device includes a memory that stores at least a first photoplethysmography (PPG) signal at a first wavelength and a second PPG signal at a second wavelength. The device also includes a processing circuit configured to determine a plurality of first R values over a sample window using the first PPG signal and the second PPG signal and determine a first R value of the user using the plurality of first R values, wherein the first R value is determined using at least one of: an average of the plurality of first R values, a mean of the plurality of first R values, and an integration of the plurality of first R values. The processing circuit is further configured to determine the first R value is outside a normal range of R values for the blood type of the user and generate an alert of an abnormal result in response to the first R value being outside the normal range of R values for the known blood type of the user. 
     In one or more of the above aspects, the device is configured to determine the first R value is outside a normal range of R values for the blood type of the user and determine the abnormal condition of the user when the first R value is outside the normal range of R values for the known blood type of the user. 
     In one or more of the above aspects, the device obtains a blood type of the user and obtains a normal range of R values for the blood type of the user. The device determines whether the first R value of the user is within the normal range of R values for the blood type of the user. 
     In one or more of the above aspects, the device is configured to determine a heart rate and a respiration rate of the user using at least the first PPG signal or the second PPG signal and determine an abnormal condition of the user using the heart rate of the user, the respiration rate of the user and the comparison of the first R value to the normal range of R values for the known blood type of the user. 
     In one or more of the above aspects, the device is configured to determine a change in color of blood of the user using at least the first PPG signal or the second PPG signal or a third PPG signal obtained at a third wavelength of light that is reflected from or transmitted through the skin tissue of the user and determine an abnormal condition of the user in response to at least one of: the change in the color of the blood of the user or the comparison of the first R value to the normal range of R values for the known blood type of the user. 
     In one or more of the above aspects, the memory is configured to store a plurality of normal ranges of R values, wherein each of the plurality of normal ranges of R values is associated with one of the plurality of blood types. 
     In one or more of the above aspects, the device is configured to determine the first R value of the user by determining a plurality of R values during a sample window of the first PPG signal and the second PPG signal, wherein the plurality of R values includes the first ratio R value; determining an average value or a mean value of the plurality of R values; and determining the first R value of the user using the average value or the mean value of the plurality of R values. 
     In one or more of the above aspects, the device is configured to determine an abnormal condition that affects red blood cells of the user in response to the color of the blood of the user by determining a normal range of color of blood for a blood type of the user and determining the color of the blood of the user is outside the normal range of color of blood for the blood type of the user. 
     In one or more of the above aspects, the device is configured to determine a change in the color of the blood of the user using at least the first PPG signal and determine the abnormal condition that affects red blood cells of the user in response to the change in the color of the blood of the user. 
     In one or more of the above aspects, the abnormal condition causes hemolysis of at least 0.5% of the red blood cells of the blood of the user. 
     In one or more of the above aspects, the abnormal condition includes one or more of: an infection, anemia, genetic disease, sickle cell anemia, malaria, poison, tick-borne diseases, or blackwater fever. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a schematic block diagram of exemplary components in an embodiment of the biosensor. 
         FIG. 2  illustrates a schematic block diagram of an embodiment of the PPG circuit in more detail. 
         FIG. 3  illustrates a logical flow diagram of an embodiment of a method for determining concentration level of a substance in blood flow using Beer-Lambert principles. 
         FIG. 4A  illustrates a schematic block diagrams of an embodiment of a method for photoplethysmography (PPG) techniques in more detail. 
         FIG. 4B  illustrates another schematic block diagram of an embodiment of a method for photoplethysmography (PPG) techniques in more detail. 
         FIG. 5  illustrates a schematic diagram of a graph of actual clinical data obtained using an embodiment of the biosensor and PPG techniques at a plurality of wavelengths. 
         FIG. 6  illustrates a logical flow diagram of an embodiment of a method of the biosensor. 
         FIG. 7  illustrates a logical flow diagram of an exemplary method to determine levels of a substance in blood flow using the spectral responses at a plurality of wavelengths. 
         FIG. 8  illustrates a logical flow diagram of an exemplary method to determine levels of a substance using the spectral responses at a plurality of wavelengths in more detail. 
         FIG. 9  illustrates a schematic block diagram of an exemplary embodiment of a graph illustrating the extinction coefficients over a range of frequencies for a plurality of hemoglobin species. 
         FIG. 10  illustrates a schematic block diagram of an exemplary embodiment of a graph illustrating a shift in absorbance peaks of hemoglobin in the presence of nitric oxide (NO). 
         FIG. 11  illustrates a schematic block diagram of an exemplary embodiment of a graph illustrating a shift in absorbance peaks of oxygenated and deoxygenated hemoglobin (HB) in the presence of nitric oxide (NO). 
         FIG. 12  illustrates a logical flow diagram of an exemplary embodiment of a method for measuring a concentration level of a substance in vivo using shifts in absorbance spectra 
         FIG. 13  illustrates a logical flow diagram of an exemplary embodiment of a method for measuring a concentration level of a substance in blood flow using one or more measurement techniques. 
         FIG. 14  illustrates a schematic drawing of an exemplary embodiment of results of a spectral response obtained using an embodiment of the biosensor from a user. 
         FIG. 15  illustrates a schematic drawing of an exemplary embodiment of results of R values determined using a plurality of methods. 
         FIG. 16  illustrates a schematic drawing of an exemplary embodiment of results of R values for a plurality of wavelength ratios. 
         FIG. 17A  illustrates a schematic drawing of an exemplary embodiment of an empirical calibration curve for correlating oxygen saturation levels (SpO 2 ) with R values. 
         FIG. 17B  illustrates a schematic drawing of an exemplary embodiment of an empirical calibration curve for correlating NO levels (mg/dl) with R values. 
         FIG. 18  illustrates a schematic block diagram of an embodiment of a calibration database. 
         FIG. 19A  illustrates a schematic drawing of values for the R 530/940  ratio obtained in a clinical setting using a biosensor with a PPG circuit. 
         FIG. 19B  illustrates a schematic drawing of the average values for the R 530/940  ratio obtained using the biosensor. 
         FIG. 20A  illustrates a schematic drawing of values for the R 590/940  ratio obtained using the biosensor. 
         FIG. 20B  illustrates a schematic drawing of average values for the R 590/940  ratio obtained using the biosensor. 
         FIG. 21  illustrates a schematic drawing of an embodiment of a calibration table for a set of blood groups. 
         FIG. 22  illustrates a logical flow diagram of a method for obtaining a blood type using the biosensor. 
         FIG. 23A  illustrates a schematic graph of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type AB and RH−. 
         FIG. 23B  illustrates a schematic graph of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type B and RH−. 
         FIG. 24A  illustrates a schematic graph of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type A and RH−. 
         FIG. 24B  illustrates a schematic graph of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood type O and RH+. 
         FIG. 25  illustrates a schematic graph of an embodiment of predetermined signal quality parameters for obtaining a blood type in a patient. 
         FIG. 26  illustrates a logical flow diagram of an embodiment of a method for determining a blood group of a patient based on a signal quality parameter of a PPG signal. 
         FIG. 27  illustrates a logical flow diagram of an embodiment of a method for determining a blood group of a patient using PPG technology. 
         FIG. 28A  illustrates a schematic graph of an example R 660/940  values for a patient with blood type AB and RH factor—and with an SpO 2  of 97%. 
         FIG. 28B  illustrates a schematic graph of an established calibration curve between R 660/940  values and oxygen saturation SpO 2 . 
         FIG. 29A  illustrates a schematic graph of an example R 660/940  values for a patient with blood type B and RH factor—and with an SpO 2  of 97%. 
         FIG. 29B  illustrates a schematic graph of an established calibration curve between R 660/940  values and oxygen saturation SpO 2 . 
         FIG. 30A  illustrates a schematic graph of an example R 660/940  values for a patient with blood type A and RH factor—and with an SpO 2  of 97%. 
         FIG. 30B  illustrates a schematic graph of an established calibration curve between R 660/940  values and oxygen saturation SpO 2 . 
         FIG. 31A  illustrates a schematic graph of an example R 660/940  values for a patient with blood type O and RH factor+ and with an SpO 2  of 97%. 
         FIG. 31B  illustrates a schematic graph of an established calibration curve between R 660/940  values and oxygen saturation SpO 2 . 
         FIG. 32  illustrates a schematic graph of an error value of example R 660/940  values for patients with various blood types at an SpO 2  of 97%. 
         FIG. 33  illustrates a logical flow diagram of an embodiment of a method for determining a calibration curve of SpO 2  for a blood type. 
         FIG. 34  illustrates a logical flow diagram of a method for determining an oxygen saturation level of a patient. 
         FIG. 35  illustrates a logical flow diagram of a method for determining a patient measurement using a blood type of the patient. 
         FIG. 36  illustrates a logical flow diagram of an exemplary embodiment of a method for detecting an abnormal condition. 
     
    
    
     DETAILED DESCRIPTION 
     The word “exemplary” or “embodiment” is used herein to mean “serving as an example, instance, or illustration.” Any implementation or aspect described herein as “exemplary” or as an “embodiment” is not necessarily to be construed as preferred or advantageous over other aspects of the disclosure. Likewise, the term “aspects” does not require that all aspects of the disclosure include the discussed feature, advantage, or mode of operation. 
     Embodiments will now be described in detail with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the aspects described herein. It will be apparent, however, to one skilled in the art, that these and other aspects may be practiced without some or all the specific details. In addition, well known steps in a method of a process may be omitted from flow diagrams presented herein in order not to obscure the aspects of the disclosure. Similarly, well known components in a device may be omitted from figures and descriptions thereof presented herein in order not to obscure the aspects of the disclosure. 
     Overview of Blood Typing Using PPG Technology 
     A biosensor identifies a blood type using photoplethysmography (PPG) technology. A PPG circuit obtains a plurality of spectral responses at a plurality of wavelengths that are detected from skin of a user. A processing circuit determines a blood factor indicator using the plurality of spectral responses. The blood factor indicator may include a signal quality parameter of the PPG signal or a ratio R value. A calibration database includes a correlation of the blood factor indicator to a plurality of blood types. The blood type of the user is identified using the blood factor indicator and the calibration database. 
     Embodiment of the Biosensor 
     In an embodiment, a biosensor includes an optical sensor or photoplethysmography (PPG) circuit configured to transmit light at a plurality of wavelengths directed at skin tissue of a user. The user may include any living organism, human or non-human. The PPG circuit detects the light reflected from the skin tissue and generates one or more spectral responses at the plurality of wavelengths. A processing circuit integrated in the biosensor or in communication with the biosensor processes the spectral data to obtain a user&#39;s vitals and/or other health information. For example, a PPG signal reflects the pulsatile volume changes in the arterial or venous blood flow due to the effects of the cardiac cycle and respiratory systems on the circulation. 
       FIG. 1  illustrates a schematic block diagram of exemplary components in an embodiment of the biosensor  100 . The biosensor  100  includes a PPG circuit  110  as described in more detail herein. The PPG circuit  110  may be configured to detect oxygen saturation (SaO2 or SpO2) levels in blood flow, as well as heart rate and respiration rate. In addition, the PPG circuit  110  is configured to detect concentration levels of one or more substances in blood flow of a user, e.g., using one or more measurement techniques as described in more detail herein. 
     The biosensor  100  may include one or more processing circuits  202  communicatively coupled to a memory device  204 . In one aspect, the memory device  204  may include one or more non-transitory processor readable memories that store instructions which when executed by the one or more processing circuits  102 , causes the one or more processing circuits  102  to perform one or more functions described herein. The processing circuit  102  may be co-located with one or more of the other circuits of the biosensor  100  in a same physical circuit board or located separately in a different circuit board or encasement. The processing circuit may also be communicatively coupled to a central control module integrated in a vehicle as described further herein. The biosensor  100  may be battery operated and include a battery  210 , such as a lithium ion battery. The memory device may store spectral data  120  or other health information or data obtained by the biosensor  100 . 
     The biosensor  100  may include a temperature sensor  108  configured to detect a temperature of a user. For example, the temperature sensor  108  may include an array of sensors (e.g., 16×16 pixels) to detect a temperature of skin tissue of a user. The temperature sensor  214  may also be used to calibrate the PPG circuit  110 . 
     The biosensor  100  may also include a touch pad or touch point with a proximity indicator  114  and pressure sensor  112 . The proximity indicator  114  includes one or more LEDs, e.g. in the IR range, that emit pulses of light. When a finger or other body part is positioned near the touch point, a photodiode may then detect a reflectance of the IR light. The biosensor  100  may then activate the PPG circuit  110 . A pressure sensor  112  may detect a pressure on the touch point by a finger or other body part and provide a feedback indicator. The feedback indicator provides a visible, audible or tactile indication that the pressure applied by the finger is within tolerance levels or needs to increase or decrease for proper detection of spectral data by the biosensor  100 . 
     The biosensor  100  may also include an atmospheric gas sensor  116  configured to detect one or more types of gases in the interior of a vehicle. For example, the gas sensor  116  may detect carbon monoxide, carbon dioxide, nitrogen dioxide, sulfur dioxide, or other gases that may be harmful to a user and present in the air in an interior of a vehicle. The vehicular monitoring system may then provide a feedback or indicator when such harmful gases are detected over a predetermined threshold that may be harmful or affect a user. 
     The biosensor  100  also includes a feedback interface  118  configured to initiate a visual indicator  122 , audible indicator  124  or tactile or haptic indicator  126 . 
     The biosensor  100  further includes a transceiver  106 . The transceiver  106  may include a wireless or wired transceiver configured to communicate with or with one or more devices over a LAN, MAN and/or WAN. In one aspect, the wireless transceiver may include a Bluetooth enabled (BLE) transceiver or IEEE 802.11ah, Zigbee, IEEE 802.15-11 or WLAN (such as an IEEE 802.11 standard protocol) compliant transceiver. In another aspect, the wireless transceiver may operate using RFID, short range radio frequency, infrared link, or other short range wireless communication protocol. In another aspect, the wireless transceiver may also include or alternatively include an interface for communicating over a cellular network. The transceiver  106  may also include a wired transceiver interface, e.g., a USB port or other type of wired connection, for communication with one or more other devices over a LAN, MAN and/or WAN. The transceiver  106  may include a wireless or wired transceiver configured to communicate with a vehicle or its components over a controller area network (CAN), Local Interconnect Network (LIN), Flex Ray, Media Oriented Systems Transport (MOST), (On-Board Diagnostics II), Ethernet or using another type of network or protocol. 
     The biosensor  100  may be configured in various form factors, such as a skin patch, ear piece, watch, finger attachment, in a button, etc. The biosensor  100  may be configured for measurement of biosensor data on various skin surfaces of a patient, including on a forehead, arm, wrist, abdominal area, chest, leg, ear lobe, finger, toe, ear canal, etc. The biosensor  100  may be implemented in user equipment (UE) or user device, such as a smart phone, tablet, watch, laptop, or other type of portable user device. In addition, one or more biosensors  100  in one or more form factors may be used in combination with a user device to determine biosensor data at one or more areas of the body. For example, the biosensor  100  may communicate with a user device over a short range wired or wireless connection. 
     In an embodiment, the integrated or external biosensor  100  may include a pulse oximeter configured to detect pulse and blood oxygen levels. The integrated or external biosensor  100  may also include a temperature sensor to detect body or skin temperature. In an embodiment, the integrated or external biosensor  100  includes the PPG circuit  110  configured to detect one or more substances in blood, such as an indicator of glucose levels in arterial blood flow or blood levels of other substances, such as electrolytes, nitric oxide (NO), bilirubin, sodium, potassium, glucose, or blood alcohol levels. 
     Embodiment—PPG Circuit 
       FIG. 2  illustrates a schematic block diagram of an embodiment of the PPG circuit  110  in more detail. The PPG circuit  110  includes a light source  620  configured to emit a plurality of wavelengths of light across various spectrums. The light source  620  may be implemented as part of a camera  250 . 
     In an embodiment, the light source  620  mat include a plurality of LEDs  622   a - n . The PPG circuit  110  is configured to direct the emitted light at an outer or epidermal layer of skin tissue of a user through at least one aperture  628   a . The plurality of LEDs  622   a - n  are configured to emit light in one or more spectrums, including infrared (IR) light, ultraviolet (UV) light, near IR light or visible light, in response to driver circuit  618 . For example, the biosensor  100  may include a first LED  622   a  that emits visible light and a second LED  622   b  that emits infrared light and a third LED  622   c  that emits UV light, etc. In another embodiment, one or more of the LEDs  622   a - n  may include tunable LEDs or lasers operable to emit light over one or more frequencies or ranges of frequencies or spectrums in response to driver circuit  618 . The light source  620  may be implemented as part of a camera as well. 
     In an embodiment, the driver circuit  618  is configured to control the one or more LEDs  622   a - n  to generate light at one or more frequencies for predetermined periods of time. The driver circuit  618  may control the LEDs  622   a - n  to operate concurrently or consecutively. The driver circuit  618  is configured to control a power level, emission period and frequency of emission of the LEDs  622   a - n . The biosensor  100  is thus configured to emit one or more wavelengths of light in one or more spectrums that is directed at the surface or epidermal layer of the skin tissue of a user. 
     The PPG circuit  110  further includes one or more photodetector circuits  630   a - n . The photodetector circuits  630  may be implemented as part of a camera  250 . For example, a first photodetector circuit  630  may be configured to detect visible light and the second photodetector circuit  630  may be configured to detect IR light. Alternatively, one or more of the photodetector circuits  630   a - n  may be configured to detect light across multiple spectrums. When multiple photodetector circuits  630  are implemented, the detected signals obtained from each of the photodetector circuits  630   a - n  may be added or averaged. The first photodetector circuit  630  and the second photodetector circuit  630  may also include a first filter  660  and a second filter  662  configured to filter ambient light and/or scattered light. For example, in some embodiments, only light reflected at an approximately perpendicular angle to the skin surface of the user is desired to pass through the filters. The first photodetector circuit  630  and the second photodetector circuit  632  are coupled to a first A/D circuit  638  and a second A/D circuit  640 . Alternatively, a single A/D circuit may be coupled to each of the photodetector circuits  630   a - n . Though the PPG circuit  110  is configured to detect reflected light, the PPG circuit  110  may also be configured for transmissive light detection as well. 
     In another embodiment, a single photodetector circuit  630  may be implemented operable to detect light over multiple spectrums or frequency ranges. The one or more photodetector circuits  630  include one or more types of spectrometers or photodiodes or other type of circuit configured to detect an intensity of light as a function of wavelength to obtain a spectral response. In use, the one or more photodetector circuits  630  detect the intensity of light reflected from skin tissue of a user that enters one or more apertures  628   b - n  of the biosensor  100 . In another example, the one or more photodetector circuits  630  detect the intensity of light due to transmissive absorption (e.g., light transmitted through tissues such as a fingertip or ear lobe). The one or more photodetector circuits  630   a - n  then obtain a spectral response of the reflected or transmissive light by measuring an intensity of the light at one or more wavelengths. 
     In another embodiment, the light source  620  may include a broad spectrum light source, such as a white light to infrared (IR) or near IR LED  622 , that emits light with wavelengths across multiple spectrums, e.g. from 350 nm to 2500 nm. Broad spectrum light sources  620  with different ranges may be implemented. In an aspect, a broad spectrum light source  620  is implemented with a range across 100 nm wavelengths to 2000 nm range of wavelengths in the visible, IR and/or UV frequencies. For example, a broadband tungsten light source for spectroscopy may be used. The spectral response of the reflected light is then measured across the wavelengths in the broad spectrum, e.g. from 350 nm to 2500 nm, concurrently. In an aspect, a charge coupled device (CCD) spectrometer may be configured in the photodetector circuit  630  to measure the spectral response of the detected light over the broad spectrum. 
     The PPG circuit  110  may also include a digital signal processing (DSP) circuit or filters or amplifiers to process the PPG signals. The spectral data may then be processed by the processing circuit  102  to obtain health data of a user. The spectral data may alternatively or in additionally be transmitted by the biosensor  100  to a central control module in a vehicle for processing to obtain health data of a user. 
     One or more of the embodiments of the biosensor  100  described herein is configured to detect a concentration level of one or more substances within blood flow using photoplethysmography (PPG) techniques. For example, the biosensor  100  may detect nitric oxide (NO) concentration levels and correlate the NO concentration level to a blood glucose level. The biosensor  100  may also detect oxygen saturation (SaO2 or SpO2) levels in blood flow. The biosensor may also be configured to detect a liver enzyme cytochrome oxidase (P450) enzyme and correlate the P450 concentration level to a blood alcohol level. The biosensor  100  may also detect vitals, such as heart rate and respiration rate. Because blood flow to the skin can be modulated by multiple other physiological systems, the biosensor  100  may also be used to monitor hypovolemia and other circulatory conditions. 
     In use, the biosensor  100  performs PPG techniques using the PPG circuit  110  to detect the concentration levels of one or more substances in blood flow. In one aspect, the biosensor  100  receives reflected light from skin tissue to obtain a spectral response. The spectral response includes a spectral curve that illustrates an intensity or power or energy at a frequency or wavelength in a spectral region of the detected light. The ratio of the resonance absorption peaks from two different frequencies can be calculated and based on the Beer-Lambert law used to obtain the levels of substances in the blood flow. 
     First, the spectral response of a substance or substances in the arterial blood flow is determined in a controlled environment, so that an absorption coefficient α g1  can be obtained at a first light wavelength λ1 and at a second wavelength λ2. According to the Beer-Lambert law, light intensity will decrease logarithmically with path length l (such as through an artery of length l). Assuming then an initial intensity I in  of light is passed through a path length l, a concentration C g  of a substance may be determined. For example, the concentration Cg may be obtained from the following equations: 
       At the first wavelength λ 1   , I   1   =I   in1 *10 −(α     g1     C     gw     +α     w1     C     w     )*l  
 
       At the second wavelength λ 2   , I   2   =I   in2 *10 −(α     g2     C     gw     +α     w2     C     w     )*l  
 
     wherein: 
     I in1  is the intensity of the initial light at λ 1    
     I in2  is the intensity of the initial light at λ 2    
     α g1  is the absorption coefficient of the substance in arterial blood at λ 1    
     α g2  is the absorption coefficient of the substance in arterial blood at λ 2    
     α w1  is the absorption coefficient of arterial blood at λ 1    
     α w2  is the absorption coefficient of arterial blood at λ 2    
     C gw  is the concentration of the substance and arterial blood 
     C w  is the concentration of arterial blood 
     Then letting R equal: 
     
       
         
           
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             = 
             
               
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     The concentration of the substance Cg may then be equal to: 
     
       
         
           
             
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     The biosensor  100  may thus determine the concentration of various substances in arterial blood flow from the Beer-Lambert principles using the spectral responses of at least two different wavelengths. 
       FIG. 3  illustrates a logical flow diagram of an embodiment of a method  300  for determining concentration level of a substance in blood flow using Beer-Lambert principles. The biosensor  100  transmits light at a first predetermined wavelength and at a second predetermined wavelength. The biosensor  100  detects the light (reflected from the skin or transmitted through the skin) and determines the spectral response at the first wavelength at  302  and at the second wavelength at  304 . The biosensor  100  then determines an indicator or concentration level of the substance using the spectral responses of the first and second wavelength at  306 . In general, the first predetermined wavelength is selected that has a high absorption coefficient for the substance in blood flow while the second predetermined wavelength is selected that has a lower absorption coefficient for the substance in blood flow. Thus, it is generally desired that the spectral response for the first predetermined wavelength have a higher intensity level in response to the substance than the spectral response for the second predetermined wavelength. 
     In an embodiment, the biosensor  100  may detect a concentration level of NO in blood flow using a first predetermined wavelength in a range of 380-410 nm and in particular at 390 nm or 395 nm. In another aspect, the biosensor  100  may transmit light at the first predetermined wavelength in a range of approximately 1 nm to 50 nm around the first predetermined wavelength. Similarly, the biosensor  100  may transmit light at the second predetermined wavelength in a range of approximately 1 nm to 50 nm around the second predetermined wavelength. The range of wavelengths is determined based on the spectral response since a spectral response may extend over a range of frequencies, not a single frequency (i.e., it has a nonzero linewidth). The light that is reflected or transmitted by NO may spread over a range of wavelengths rather than just the single predetermined wavelength. In addition, the center of the spectral response may be shifted from its nominal central wavelength or the predetermined wavelength. The range of 1 nm to 50 nm is based on the bandwidth of the spectral response line and should include wavelengths with increased light intensity detected for the targeted substance around the predetermined wavelength. 
     The first spectral response of the light over the first range of wavelengths including the first predetermined wavelength and the second spectral response of the light over the second range of wavelengths including the second predetermined wavelengths is then generated at  302  and  304 . The biosensor  100  analyzes the first and second spectral responses to detect an indicator or concentration level of NO in the arterial blood flow at  306 . 
       FIG. 4A  and  FIG. 4B  illustrate schematic block diagrams of an embodiment of a method for photoplethysmography (PPG) techniques in more detail. Photoplethysmography (PPG) is used to measure time-dependent volumetric properties of blood in blood vessels due to the cardiac cycle. For example, the heartbeat affects the volume of arterial blood flow and the concentration or absorption levels of substances being measured in the arterial blood flow. As shown in  FIG. 4A , over a cardiac cycle  402 , pulsating arterial blood  404  changes the volume of blood flow in an artery. 
     Incident light I O    412  is directed at a tissue site and a certain amount of light is reflected or transmitted  418  and a certain amount of light is absorbed  420 . At a peak of arterial blood flow or arterial volume, the reflected/transmitted light I L    414  is at a minimum due to absorption by the venous blood  408 , nonpulsating arterial blood  406 , pulsating arterial blood  404 , other tissue  410 , etc. At a minimum of arterial blood flow or arterial volume during the cardiac cycle, the transmitted/reflected light I H    416  is at a maximum due to lack of absorption from the pulsating arterial blood  404 . 
     The biosensor  100  is configured to filter the reflected/transmitted light I L    414  of the pulsating arterial blood  404  from the transmitted/reflected light I H    416 . This filtering isolates the light due to reflection/transmission of substances in the pulsating arterial blood  404  from the light due to reflection/transmission from venous (or capillary) blood  408 , other tissues  410 , etc. The biosensor  100  may then measure the concentration levels of one or more substances from the reflected/transmitted light I L    814  in the pulsating arterial blood flow  404 . 
     For example, as shown in  FIG. 4B , incident light I O    412  is directed at a tissue site by an LED  422  at one or more wavelengths. The reflected/transmitted light I  418  is detected by photodetector  424  or camera  250 . At a peak of arterial blood flow or arterial volume, the reflected light I L    414  is at a minimum due to absorption by venous blood  808 , non-pulsating arterial blood  406 , pulsating arterial blood  404 , other tissue  410 , etc. At a minimum of arterial blood flow or arterial volume during the cardiac cycle, the Incident or reflected light I H    416  is at a maximum due to lack of absorption from the pulsating arterial blood  404 . Since the light I  418  is reflected or traverses through a different volume of blood at the two measurement times, the measurement provided by a PPG sensor is said to be a ‘volumetric measurement’ descriptive of the differential volumes of blood present at a certain location within the user&#39;s arteriolar bed at different times. Though the above has been described with respect to arterial blood flow, the same principles described herein may be applied to venous blood flow. 
     In general, the relative magnitudes of the AC and DC contributions to the reflected/transmitted light signal I  418  may be used to substantially determine the differences between the diastolic points and the systolic points. In this case, the difference between the reflected light I L    414  and reflected light I H    416  corresponds to the AC contribution of the reflected light  418  (e.g. due to the pulsating arterial blood flow). A difference function may thus be computed to determine the relative magnitudes of the AC and DC components of the reflected light I  418  to determine the magnitude of the reflected light I L    414  due to the pulsating arterial blood  404 . The described techniques herein for determining the relative magnitudes of the AC and DC contributions is not intended as limiting. It will be appreciated that other methods may be employed to isolate or otherwise determine the relative magnitude of the light I L    414  due to pulsating arterial blood flow. 
       FIG. 5  illustrates a schematic diagram of a graph of actual clinical data obtained using an embodiment of the biosensor  100  and PPG techniques at a plurality of wavelengths. In one aspect, the biosensor  100  is configured to emit light having a plurality of wavelengths during a measurement period. The light at each wavelength (or range of wavelengths) may be transmitted concurrently or sequentially. The intensity of the reflected light at each of the wavelengths (or range of wavelengths) is detected and the spectral response is measured over the measurement period. The spectral responses  508  for the plurality of wavelengths obtained using an embodiment of the biosensor in clinical trials is shown in  FIG. 5 . In this clinical trial, two biosensors  100  attached to two separate fingertips of a user were used to obtain the spectral responses  508 . The first biosensor  100  obtained the spectral response for a wavelength at 940 nm  510 , a wavelength at 660 nm  512  and a wavelength at 390 nm  514 . The second biosensor  100  obtained the spectral response for a wavelength at 940 nm  516 , a wavelength at 592 nm  518  and a wavelength at 468 nm  520 . 
     In one aspect, the spectral response obtained at each wavelength may be aligned based on the systolic  502  and diastolic  504  points in their respective spectral responses. This alignment is useful to associate each spectral response with a particular stage or phase of the pulse-induced local pressure wave within the blood vessel (which may mimic the cardiac cycle  506  and thus include systolic and diastolic stages and sub-stages thereof). This temporal alignment helps to determine the absorption measurements acquired near a systolic point in time of the cardiac cycle and near the diastolic point in time of the cardiac cycle  506  associated with the local pressure wave within the user&#39;s blood vessels. This measured local pulse timing information may be useful for properly interpreting the absorption measurements in order to determine the relative contributions of the AC and DC components measured by the biosensor  100 . So, for one or more wavelengths, the systolic points  502  and diastolic points  504  in the spectral response are determined. These systolic points  502  and diastolic points  504  for the one or more wavelengths may then be aligned as a method to discern concurrent responses across the one or more wavelengths. 
     In another embodiment, the systolic points  502  and diastolic points  504  in the absorbance measurements are temporally correlated to the pulse-driven pressure wave within the arterial blood vessels—which may differ from the cardiac cycle. In another embodiment, the biosensor  100  may concurrently measure the intensity reflected at each the plurality of wavelengths. Since the measurements are concurrent, no alignment of the spectral responses of the plurality of wavelengths may be necessary.  FIG. 5  illustrates the spectral response obtained at the plurality of wavelengths with the systolic points  502  and diastolic points  504  aligned. 
       FIG. 6  illustrates a logical flow diagram of an embodiment of a method  600  of the biosensor  100 . In one aspect, the biosensor  100  emits and detects light at a plurality of predetermined frequencies or wavelengths, such as approximately 940 nm, 660 nm, 390 nm, 592 nm, and 468 nm or in ranges thereof. The light is pulsed for a predetermined period of time (such as 100 usec or 200 Hz) sequentially or simultaneously at each predetermined wavelength. In another aspect, light may be pulsed in a wavelength range of 1 nm to 50 nm around each of the predetermined wavelengths. For example, for the predetermined wavelength 390 nm, the biosensor  100  may transmit light directed at skin tissue of the user in a range of 360 nm to 410 nm including the predetermined wavelength 390 nm. For the predetermined wavelength of 940 nm, the biosensor  100  may transmit light directed at the skin tissue of the user in a range of 920 nm to 975 nm. In another embodiment, the light is pulsed simultaneously at least at each of the predetermined wavelengths (and in a range around the wavelengths). 
     The spectral responses are obtained around the plurality of wavelengths, including at least a first wavelength and a second wavelength at  602 . The spectral responses may be measured over a predetermined period (such as 300 usec). This measurement process is repeated continuously, e.g., pulsing the light at 10-100 Hz and obtaining spectral responses over a desired measurement period, e.g. from 1-2 seconds to 1-2 minutes or from 2-3 hours to continuously over days or weeks. The spectral responses are measured over one or more cardiac cycles. The spectral data obtained by the PPG circuit  110 , such as the digital or analog spectral responses over the one or more cardiac cycles, may be processed locally by the biosensor  100  or transmitted to a central control module of a vehicle for processing. 
     The systolic and diastolic points of the spectral response are then determined. Because the human pulse is typically on the order of magnitude of one 1 Hz, typically the time differences between the systolic and diastolic points are on the order of magnitude of milliseconds or tens of milliseconds or hundreds of milliseconds. Thus, spectral response measurements may be obtained at a frequency of around 10-100 Hz over the desired measurement period. The spectral responses are obtained over one or more cardiac cycles and systolic and diastolic points of the spectral responses are determined. 
     A low pass filter (such as a 5 Hz low pass filter) is applied to the spectral response signal at  604 . The relative contributions of the AC and DC components are obtained I AC-DC  and I AC . A peak detection algorithm is applied to determine the systolic and diastolic points at  606 . The systolic and diastolic points of the spectral response for each of the wavelengths may be aligned and may also be aligned with systolic and diastolic points of an arterial pulse waveform or cardiac cycle. 
     Beer Lambert equations are then applied as described herein. For example, the L λ  values are then calculated for the first wavelength λ 1  at  608  and the second wavelength λ 2  at  610 , wherein the L λ  values for a wavelength equals: 
     
       
         
           
             
               L 
               λ 
             
             = 
             
               Log 
                
               
                   
               
                
               10 
                
               
                 ( 
                 
                   
                     
                       I 
                        
                       
                           
                       
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                       AC 
                     
                     + 
                     DC 
                   
                   
                     I 
                      
                     
                         
                     
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                 ) 
               
             
           
         
       
     
     wherein I AC-DC  is the intensity of the detected light with AC and DC components and I DC  is the intensity of the detected light with the AC filtered by the low pass filter. The value L λ  isolates the spectral response due to pulsating arterial blood flow, e.g. the AC component of the spectral response. 
     A ratio R of the L λ  values at two wavelengths may then be determined at  612 . For example, the ratio R may be obtained from the following: 
     
       
         
           
             
               Ratio 
                
               
                   
               
                
               R 
             
             = 
             
               
                 L 
                  
                 
                     
                 
                  
                 λ 
                  
                 
                     
                 
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     The spectral responses may be measured and the L λ  values and Ratio R determined continuously, e.g. every 1-2 seconds, and the obtained L λ  values and/or Ratio R averaged over a predetermined time period, such as over 1-2 minutes. The concentration level of a substance may then be obtained from the R value and a calibration database at  614 . The biosensor  100  may continuously monitor a user over 2-3 hours or continuously over days or weeks. 
     In one embodiment, the R 390,940  value with L λ1=390 nm  and L λ2-940  may be non-invasively and quickly and easily obtained using the biosensor  100  to determine a concentration level of NO in blood flow of a user. In particular, in unexpected results, it is believed that the nitric oxide NO levels in the arterial blood flow is being measured at least in part by the biosensor  100  at wavelengths in the range of 380-410 and in particular at λ 1=390 nm . Thus, the biosensor  100  measurements to determine the L 390 nm  values are the first time NO concentration levels in arterial blood flow have been measured directly in vivo. These and other aspects of the biosensor  100  are described in more detail herein with clinical trial results. 
     Embodiment—Determination of Concentration Level of a Substance Using Spectral Responses at a Plurality of Wavelengths 
       FIG. 7  illustrates a logical flow diagram of an exemplary method  700  to determine levels of a substance in blood flow using the spectral responses at a plurality of wavelengths. The absorption coefficient of a substance may be sufficiently higher at a plurality of wavelengths, e.g. due to isoforms or derivative compounds. For example, the increased intensity of light at a plurality of wavelengths may be due to reflectance by isoforms or other compounds in the arterial blood flow. Another method for determining the concentration levels may then be used by measuring the spectral responses and determining L and R values at a plurality of different wavelengths of light. In this example then, the concentration level of the substance is determined using spectral responses at multiple wavelengths. An example for calculating the concentration of a substance over multiple wavelengths may be performed using a linear function, such as is illustrated herein below. 
       LN( I   1-n )=Σ i=0   n   μi*Ci  
 
     wherein, 
     I 1-n =intensity of light at wavelengths λ 1-n    
     μ n =absorption coefficient of substance 1, 2, . . . n at wavelengths λ 1-n    
     C n =Concentration level of substance 1, 2, . . . n 
     When the absorption coefficients of a substance, its isoforms or other compounds including the substance are known at the wavelengths λ 1-n , then the concentration level C of the substances may be determined from the spectral responses at the wavelengths λ 1-n  (and e.g., including a range of 1 nm to 50 nm around each of the wavelengths). The concentration level of the substance may be isolated from the isoforms or other compounds by compensating for the concentration of the compounds. Thus, using the spectral responses at multiple frequencies provides a more robust determination of the concentration level of a substance. 
     In use, the biosensor  100  transmits light directed at skin tissue at a plurality of wavelengths or over a broad spectrum at  702 . The spectral response of light from the skin tissue is detected at  704 , and the spectral responses are analyzed at a plurality of wavelengths (and in one aspect including a range of +/−10 to 50 nm around each of the wavelengths) at  706 . Then, the concentration level C of the substance may be determined using the spectral responses at the plurality of wavelengths at  708 . The concentration level of the substance may be isolated from isoforms or other compounds by compensating for the concentration of the compounds. 
       FIG. 8  illustrates a logical flow diagram of an exemplary method  800  to determine levels of a substance using the spectral responses at a plurality of wavelengths in more detail. The spectral responses are obtained at  802 . The spectral response signals include AC and DC components I AC+DC . A low pass filter (such as a 5 Hz low pass filter) is applied to each of the spectral response signals I AC+DC  to isolate the DC component of each of the spectral response signals I DC  at  804 . The AC fluctuation is due to the pulsatile expansion of the arteriolar bed due to the volume increase in arterial blood. In order to measure the AC fluctuation, measurements are taken at different times and a peak detection algorithm is used to determine the diastolic point and the systolic point of the spectral responses at  806 . A Fast Fourier transform (FFT) function may also be used to isolate the DC component I DC  of a spectral response signal I AC+DC  at  806 . The I DC  component is thus isolated from the spectral signal at  808 . 
     The I AC+DC  and I DC  components are then used to compute the L values at  810 . For example, a logarithmic function may be applied to the ratio of I AC+DC  and I DC  to obtain an L value for each of the wavelengths L λ1-n . Since the respiratory cycle affects the PPG signals, the L values may be averaged over a respiratory cycle and/or over another predetermined time period (such as over a 1-2 minute time period) or over a plurality of cardiac cycles at  812 . 
     In an embodiment, isoforms of a substance may be attached in the blood stream to one or more species of hemoglobin compounds. The concentration level of the hemoglobin species may then need to be accounted for to isolate the concentration level of the substance from the hemoglobin compounds. For example, nitric oxide (NO) is found in the blood stream in a gaseous form and also attached to hemoglobin compounds. Thus, the spectral responses obtained around 390 nm may include a concentration level of the hemoglobin species as well as nitric oxide. The hemoglobin concentration levels must thus be compensated for to isolate the nitric oxide NO concentration levels. 
     Multiple wavelengths and absorption coefficients for hemoglobin species are used to determine a concentration of one or more of the hemoglobin species at  814 . Other methods may also be used to obtain a concentration level of various species of hemoglobin in the blood flow as well. The concentration of the hemoglobin species is then adjusted from the measurements at  816 . The concentration values of the substance may then be obtained at  818 . For example, the R values are then determined at  818 . 
     To determine a concentration level of the substance, a calibration table or database is used that associates the obtained R value to a concentration level of the substance at  820 . The calibration database correlates the R value with a concentration level. The calibration database may be generated for a specific user or may be generated from clinical data of a large sample population. For example, it is determined that the R values should correlate to similar NO concentration levels across a large sample population. Thus, the calibration database may be generated from testing of a large sample of a general population to associate R values and NO concentration levels. 
     In addition, the R values may vary depending on various factors, such as underlying skin tissue. For example, the R values may vary for spectral responses obtained from an abdominal area versus measurements from a wrist or finger due to the varying tissue characteristics. The calibration database may thus provide different correlations between the R values and concentration levels of a substance depending on the underlying skin tissue characteristics that were measured. 
     The concentration level of the substance in blood flow is then obtained at  824 . The concentration level may be expressed as mmol/liter, as a saturation level percentage, as a relative level on a scale, etc. 
     In another embodiment, in order to remove the hemoglobin concentration(s) from the original PPG signals, a mapping function may be created which is constructed through clinical data and tissue modeling. For example, known SpO 2  values in the infrared region and the same signals at the UV side of the spectrum are obtained. Then a linear inversion map can be constructed where the R values are input into a function and the desired concentration(s) can be determined. For example, a curve that correlates R values to concentration levels may be tabulated. A polynomial equation with multiple factors can also be used to account for different R values to represent the linear inversion map. This correlation may be derived from validated clinical data. 
     For example, a regression curve that correlates R values and NO concentration levels may be generated based on clinical data from a large general population. A polynomial may be derived from the curve and used to solve for an NO concentration level from the R value. The polynomial is stored in the calibration database and may be used rather than using a calibration look-up table or curve. 
     Embodiment—Determination of a Concentration of Hemoglobin Species 
     The Beer-Lambert theory may be generalized for a multi-wavelength system to determine a concentration of known hemoglobin species using the following matrix notation: 
     
       
         
           
             
               
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                                 HbX 
                                 m 
                               
                             
                           
                         
                       
                     
                     ] 
                   
                 
                 · 
                 
                   [ 
                   
                     
                       
                         
                           HbX 
                           1 
                         
                       
                     
                     
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           HbX 
                           m 
                         
                       
                     
                   
                   ] 
                 
                 · 
                 
                   c 
                    
                   
                     ( 
                     Hb 
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     wherein 
     dλ λ   LB  is a differential absorption within the Beer-Lambert model 
     ε λn1,HbX1  is an extinction coefficient 
     HbX are hemoglobin fractions 
     Δlλ is the optical path-length for wavelength λ 
     c(Hb) is the hemoglobin concentration 
     This Beer-Lambert matrix equation for determining concentration levels of one or more hemoglobin species may be solved when m is equal or greater than n, e.g., which means that at least four wavelengths are needed to solve for four hemoglobin species. The spectral responses at these four wavelengths may be analyzed to determine the concentration of the plurality of hemoglobin species. 
       FIG. 9  illustrates a schematic block diagram of an exemplary embodiment of a graph  900  illustrating the extinction coefficients over a range of frequencies for a plurality of hemoglobin species. The hemoglobin species include, e.g., Oxyhemoglobin [HbO 2  or OxyHb]  902 , Carboxyhemoglobin [HbCO or CarboxyHb]  904 , Methemoglobin [HbMet or MetHb]  906 , and deoxygenated hemoglobin (DeoxyHb or RHb)  908 . A method for determining the relative concentration or composition of hemoglobin species included in blood is described in more detail in U.S. Pat. No. 6,104,938 issued on Aug. 15, 2000, which is hereby incorporated by reference herein. 
     A direct calibration method for calculating hemoglobin species may be implemented by the biosensor  100 . Using four wavelengths and applying a direct model for four hemoglobin species in the blood, the following equation results: 
     
       
         
           
             HbX 
             = 
             
               
                 
                   
                     a 
                     1 
                   
                   * 
                   
                     dA 
                     1 
                   
                 
                 + 
                 
                   
                     a 
                     2 
                   
                   * 
                   
                     dA 
                     2 
                   
                 
                 + 
                 
                   
                     a 
                     3 
                   
                   * 
                   
                     dA 
                     3 
                   
                 
                 + 
                 
                   
                     a 
                     4 
                   
                   * 
                   
                     dA 
                     4 
                   
                 
               
               
                 
                   
                     b 
                     1 
                   
                   * 
                   
                     dA 
                     1 
                   
                 
                 + 
                 
                   
                     b 
                     2 
                   
                   * 
                   
                     dA 
                     2 
                   
                 
                 + 
                 
                   
                     b 
                     3 
                   
                   * 
                   
                     dA 
                     3 
                   
                 
                 + 
                 
                   
                     b 
                     4 
                   
                   * 
                   
                     dA 
                     4 
                   
                 
               
             
           
         
       
     
     wherein 
     dA λ  is the differential absorption signal 
     a n  and b n  are calibration coefficients 
     The calibration coefficients a n  and b n  may be experimentally determined over a large population average. The biosensor  100  may include a calibration database to account for variances in the calibration coefficients a 1  and b 1  or extinction coefficients) for the hemoglobin species for various underlying tissue characteristics. 
     A two-stage statistical calibration and measurement method for performing PPG measurement of hemoglobin species concentrations may also be implemented by the biosensor  100 . Concentrations of MetHb, HbO 2 , RHb and HbCO are estimated by first estimating a concentration of MetHb (in a first stage) and subsequently, if the concentration of MetHb is within a predetermined range, then the estimated concentration of MetHb is assumed to be accurate and this estimated concentration of MetHb is utilized as a “known value” in determining the concentrations of the remaining analytes HbO 2 , RHb and HbCO (in a second stage). This method for determining a concentration of hemoglobin species using a two stage calibration and analyte measurement method is described in more detail in U.S. Pat. No. 5,891,024 issued on Apr. 6, 1999, which is hereby incorporated by reference herein. 
     The concentration of various hemoglobin compounds may thus be determined. The biosensor  100  compensates for the hemoglobin concentration in determinations to obtain the concentration level of one or more substances in blood flow, such as NO. Though several methods are described herein for obtaining a concentration of hemoglobin species, other methods or processes may be used by the biosensor  100  to determine the concentration of hemoglobin analytes or otherwise adjusting or compensating the obtained measurements to account for a hemoglobin concentration when determining the concentration levels of NO in a blood stream. 
     Embodiment—Determination of Concentration Levels of a Substance Using Shifts in Absorbance Peaks 
     In another embodiment, a concentration level of a substance may be obtained from measuring a characteristic shift in an absorbance peak of hemoglobin. For example, the absorbance peak for methemoglobin shifts from around 433 nm to 406 nm in the presence of NO. The advantage of the measurement of NO by monitoring methemoglobin production includes the wide availability of spectrophotometers, avoidance of sample acidification, and the relative stability of methemoglobin. Furthermore, as the reduced hemoglobin is present from the beginning of an experiment, NO synthesis can be measured continuously, removing the uncertainty as to when to sample for NO. 
       FIG. 10  illustrates a schematic block diagram of an exemplary embodiment of a graph  1000  illustrating a shift in absorption peaks of hemoglobin in the presence of NO. In graph A, the curve  1002  illustrates the absorption spectra of reduced hemoglobin. The addition of nitric oxide (NO) shifts the absorption spectra curve  1002  to a lower wavelength curve  1004  due to the production of methemoglobin. In graph B, the absorption spectra curve  1002  of reduced hemoglobin is again illustrated. Endothelial cells are then added and the absorption spectra measured again. The curve  1006  illustrates that little change occurs in the absorption spectra curve  1002  of reduced hemoglobin in the presence of unstimulated endothelial cells. The curve  1008  illustrates the production of methemoglobin when the same dose of endothelial cells was given after stimulation of EDRF synthesis by the ionophore. 
     Though the absorbance spectrums shown in the graph  1000  were measured using in vitro assays, the biosensor  100  may detect nitric oxide in vivo using PPG techniques by measuring the shift in the absorption spectra curve  1002  of reduced hemoglobin in tissue and/or arterial blood flow. The absorption spectra curve  1002  shifts with a peak from around 430 nm to a peak around 411 nm depending on the production of methemoglobin. The greater the degree of the shift of the peak of the absorption spectra curve  1002 , the higher the production of methemoglobin and NO concentration level. Correlations may be determined between the degree of the measured shift in the absorption spectra curve  1002  of reduced hemoglobin to an NO concentration level. The correlations may be determined from a large sample population or for a particular user and stored in a calibration database. The biosensor  100  may thus obtain an NO concentration level by measuring the shift of the absorption spectra curve  1002  of reduced hemoglobin. A similar method of determining shifts in an absorbance spectra may be implemented to determine a blood concentration level of other substances. 
       FIG. 11  illustrates a schematic block diagram of an exemplary embodiment of a graph  1100  illustrating a shift in absorbance peaks of oxygenated and deoxygenated hemoglobin (HB) in the presence of nitric oxide NO. The absorbance spectra curve  1102  of deoxygenated HB has a peak of around 430 nm. After a one minute time period of exposure to a nitric oxide mixture, the absorbance spectra curve  1104  of deoxygenated HB shifted to a peak of around 405 nm. In addition, the absorbance spectra curve  1106  of oxygenated HB has a peak around 421 nm. After a twenty minute time period of exposure to a nitric oxide mixture, the absorbance spectra curve  1108  of oxygenated HB shifted to a peak of around 393 nm. The Deoxygenated Hb has an absorption peak at 430 nm (curve  1102 ) and in the presence of NO has a peak shift to 405 nm (curve  1104 ). The Oxygenated Hb has absorption peak at 421 nm (curve  1106 ) in presence of NO has peak shift to 393 nm (curve  1108 ). 
     Though the absorbance spectrums shown in the graph  1100  were measured using in vitro assays, the biosensor  100  may obtain an NO concentration level by measuring the shift of the absorbance spectra curve  1102  of deoxygenated hemoglobin and/or by measuring the shift of the absorbance spectra curve  1106  of oxygenated hemoglobin in vivo. The biosensor  100  may then access a calibration database that correlates the measured shift in the absorbance spectra curve  1102  of deoxygenated hemoglobin to an NO concentration level. Similarly, the biosensor may access a calibration database that correlates the measured shift in the absorbance spectra curve  1106  of oxygenated hemoglobin to an NO concentration level. 
       FIG. 12  illustrates a logical flow diagram of an exemplary embodiment of a method  1200  for measuring a concentration level of a substance in vivo using shifts in absorbance spectra. The biosensor  100  may obtain a concentration of the substance by measuring shifts in absorbance spectra of one or more substances that interact with the substance. For example, the one or more substances may include oxygenated and deoxygenated hemoglobin (HB). The PPG circuit  110  detects a spectral response at a plurality of wavelengths of the one or more substances that interact with the substance at  1202 . The biosensor  100  determines the relative shift in the absorbance spectra for the substance at  1204 . For example, the biosensor  100  may measure the absorbance spectra curve  1202  of deoxygenated HB and determine its relative shift or peak between the range of approximately 430 nm and 405 nm. In another example, the biosensor  100  may measure the absorbance spectra curve of oxygenated HB and determine its relative shift or peak between 421 nm and 393 nm. 
     The biosensor  100  accesses a calibration database that correlates the relative shift in the absorbance spectra of the substance with a concentration level of the substance at  1206 . The biosensor  100  may thus obtain a concentration level of the substance in blood flow using a calibration database and the measured relative shift in absorbance spectra at  1208 . 
     The various methods thus include one or more of: Peak &amp; Valley (e.g., peak detection), fast Fourier transform (FFT), and differential absorbance spectra curves. Each of the methods require different amounts of computational time which affects overall embedded computing time for each signal, and therefore can be optimized and selectively validated with empirical data through large clinical sample studies. 
       FIG. 13  illustrates a logical flow diagram of an exemplary embodiment of a method  1300  for measuring a concentration level of a substance in blood flow using one or more measurement techniques. In an embodiment, the biosensor  100  is configured to determine a concentration level of the substance in vivo using PPG technology and one or more measurement techniques described herein. For example, the biosensor  100  may determine an R value from L values obtained from a plurality of spectral responses at  1302 . In another example, the biosensor may determine concentration level using absorption coefficients for the substance and associated isotopes and compounds over a plurality of wavelengths and adjusting or compensating for the compound concentrations (such as hemoglobin concentrations) at  1304 . In another example, the biosensor  100  may determine the relative shift in the absorbance spectra for a substance (such as hemoglobin) and access a calibration database that correlates the relative shift in the absorbance spectra of the substance with a concentration level of the substance at  1306 . 
     The biosensor  100  may use a plurality of these methods to determine a plurality of values for the concentration level of the substance at  1308 . The biosensor  100  may determine a final concentration value using the plurality of values. For example, the biosensor  100  may average the values, obtain a mean of the values, etc. 
     The biosensor  100  may be configured for measurement on a fingertip or palm, wrist, an arm, forehead, chest, abdominal area, ear lobe, or other area of the skin or body or living tissue. The characteristics of underlying tissue vary depending on the area of the body, e.g. the underlying tissue of an abdominal area has different characteristics than the underlying tissue at a wrist. The operation of the biosensor  100  may need to be adjusted in response to its positioning due to such varying characteristics of the underlying tissue. The PPG circuit  110  may adjust a power of the LEDs or a frequency or wavelength of the LEDs based on the underlying tissue. The biosensor  100  may adjust processing of the data. For example, an absorption coefficient may be adjusted when determining a concentration level of a substance based on Beer-Lambert principles due to the characteristics of the underlying tissue. 
     In addition, the calibrations utilized by the biosensor  100  may vary depending on the positioning of the biosensor. For example, the calibration database may include different table or other correlations between R values and concentration level of a substance depending on position of the biosensor. Due to the different density of tissue and vessels, the R value obtained from measurements over an abdominal area may be different than measurements over a wrist or forehead or fingertip. The calibration database may thus include different correlations of the R value and concentration level depending on the underlying tissue. Other adjustments may also be implemented in the biosensor  100  depending on predetermined or measured characteristics of the underlying tissue of the body part. 
     Embodiment—Clinical Data 
       FIG. 14  illustrates a schematic drawing of an exemplary embodiment of results of a spectral response  1400  obtained using an embodiment of the biosensor  100  from a user. The spectral response  1400  was obtained at a wavelength of around 395 nm and is illustrated for a time period of about 40 seconds. The spectral response  1400  was filtered using digital signal processing techniques to eliminate noise and background interference to obtain the filtered spectral response  1400 . A first respiration cycle  1402  and a second respiration cycle  1404  may be obtained by measuring the low frequency fluctuation of the filtered spectral response  1400 . Due to this low frequency fluctuation, the biosensor  100  may obtain a respiratory rate of a user from the spectral response  1400 . A heart rate may also be determined from the spectral response  1400  as well. For example, the biosensor  100  may determine the time between diastolic points or between systolic points to determine a time period of a cardiac cycle. 
       FIG. 15  illustrates a schematic drawing of an exemplary embodiment of results of R values  1500  determined using a plurality of methods. The R values  1500  corresponding to the wavelengths of 395 nm/940 nm is determined using three methods. The R Peak Valley curve  1502  is determined using the Ratio 
     
       
         
           
             R 
             = 
             
               
                 L 
                  
                 3 
                  
                 9 
                  
                 5 
               
               
                 L 
                  
                 9 
                  
                 4 
                  
                 0 
               
             
           
         
       
     
     as described hereinabove. The R FFT curve  1504  is obtained using FFT techniques to determine the I DC  values of the spectral responses. The R differential absorption curve  1506  is determined using the shift in absorbance spectra as described hereinabove. As seen in  FIG. 15 , the determination of the R values using the three methods provides similar results, especially when averaged over a period of time. A mean or average of the R values in the curves  1502 ,  1504  and  1506  may be calculated to obtain a final R value or one of the methods may be preferred depending on the positioning of the biosensor or underlying tissue characteristics. 
       FIG. 16  illustrates a schematic drawing of an exemplary embodiment of results of R values  1600  for a plurality of wavelength ratios. The R values for 395 nm/940 nm  1606 , the R values for 470 nm/940 nm  1604  and the R values for 660 nm/940 nm  1606  are shown over a time period of about 4 seconds. 
       FIG. 17A  illustrates a schematic drawing of an exemplary embodiment of an empirical calibration curve  1700  for correlating oxygen saturation levels (SpO 2 ) with R values. The calibration curve  1700  may be included as part of the calibration database for the biosensor  100 . For example, the R values may be obtained for L 660 nm /L 940 nm . In one embodiment, the biosensor  100  may use a light source in the 660 nm wavelength or in a range of +/−50 nm to determine SpO 2  levels, e.g. rather than a light source in the IR wavelength range. The 660 nm wavelength has been determined in unexpected results to have good results in measuring oxygenated hemoglobin, especially in skin tissue with fatty deposits, such as around the abdominal area. 
       FIG. 17B  illustrates a schematic drawing of an exemplary embodiment of an empirical calibration curve  1702  for correlating NO levels (mg/dl) with R values. The calibration curve  1702  may be included as part of the calibration database for the biosensor  100 . For example, the R values may be obtained in clinical trials from measurements of L 395 nm /L 940 nm  and the NO levels of a general sample population. The NO levels may be measured using one or more other techniques for verification to generate such a calibration curve  1702 . This embodiment of the calibration curve  1702  is based on limited clinical data and is for example only. Additional or alternative calibration curves may also be derived from measurements of a general population of users at one or more different positions of the biosensor  100 . For example, a first calibration curve may be obtained at a forehead, another for an abdominal area, another for a fingertip, etc. 
     From the clinical trials, the L values obtained at wavelengths around 390 nm (e.g. 380-410) are measuring NO levels in the arterial blood flow. The R value for L 390 /L 940 nm  may thus be used to obtain NO levels in the pulsating arterial blood flow. From the clinical trials, it seems that the NO levels are reflected in the R values obtained from L 390 nm /L 940 nm  and wavelengths around 390 nm such as L 395 nm /L 940 nm . The NO levels may thus be obtained from the R values and a calibration database that correlates the R value with known concentration level of NO for the user or for a large general population. 
     In other embodiments, rather than L λ1 =390 nm, the L value may be measured at wavelengths in a range from 410 nm to 380 nm, e.g., as seen in the graphs wherein L λ1 =395 nm is used to obtain a concentration level of NO. In addition, L λ2  may be obtained at any wavelength at approximately 660 nm or above. Thus, R obtained at approximately Lλ1=380 nm-400 nm and Lλ2≥660 nm may also be obtained to determine concentration levels of NO. 
     The concentration level of NO may be correlated to a diabetic risk or to blood glucose levels using a calibration database. 
     Embodiment—Measurements of Other Substances 
     Using similar principles described herein, the biosensor  100  may measure concentration levels or indicators of other substances in pulsating blood flow. For example, absorption coefficients for one or more frequencies that have an intensity level responsive to concentration level of substance may be determined. The biosensor  100  may then detect the substance at the determined one or more frequencies as described herein and determine the concentration levels using the Beer-Lambert principles and the absorption coefficients. The L values and R values may be calculated based on the obtained spectral response. In one aspect, the biosensor  100  may detect various electrolyte concentration levels or blood analyte levels, such as bilirubin and sodium and potassium. In another aspect, the biosensor  100  may detect sodium NACL concentration levels in the arterial blood flow to determine dehydration. In yet another aspect, the biosensor  100  may be configured to detect proteins or abnormal cells or other elements or compounds associated with cancer. In another aspect, the PPG sensor may detect white blood cell counts. In another aspect, the biosensor may detect blood alcohol levels. 
     For example, the biosensor  100  may also determine alcohol levels in the blood using wavelengths at approximately 390 nm and/or 468 nm. For example, an R 468,940  value for at least L468 nm/L940 nm may be used as a liver enzyme indicator, e.g. P450 enzyme indicator. The P450 liver enzyme is generated in response to alcohol levels. Thus, the measurement of the spectral response for the wavelength at approximately 468 nm may be used to obtain blood alcohol levels from the concentration levels of P450 and a calibration database. 
     In another embodiment, an R 592,940  value for at least L 592 nm /L 940 nm  may be used as a digestive indicator to measure digestive responses, such as phase 1 and phase 2 digestive stages. In another aspect, the biosensor  100  may detect electrolytes, such as sodium, potassium, chloride, and bicarbonate. The biosensor  100  may detect white blood cell counts or concentration levels in arterial blood flow using similar PPG techniques. The presence of white blood cell counts may indicate the presence of infection. 
     In another aspect, abnormal cells or proteins or compounds that are present or have higher concentrations in the blood with persons having cancer, may be detected using similar PPG techniques described herein at one or more other wavelengths. Thus, cancer risk may then be obtained through non-invasive testing by the biosensor  100 . Since the biosensor  100  may operate in multiple frequencies, various health monitoring tests may be performed concurrently. 
       FIG. 18  illustrates a schematic block diagram of an embodiment of a calibration database  1800 . The calibration database  1800  includes one or more calibration tables  1802 , calibration curves  1804  or calibration functions  1806  for correlating obtained values to concentration levels of one or more substances A-N. The concentration level of the substances may be expressed in the calibration tables  1802  as units of mmol/liter, as a saturation level percentage (SpNO %), as a relative level on a scale (e.g., 0-10), etc. 
     The calibration database  1800  may also include one or more calibration tables for one or more underlying skin tissue types. In one aspect, the calibration database  1800  may correlate an R value to a concentration level of a substance for a plurality of underlying skin tissue types. 
     In another aspect, a set of calibration tables  1802  may correlate an absorption spectra shift to a concentration level of one or more substances A-N. For example, a first table may correlate a degree of absorption spectra shift of oxygenated hemoglobin to NO concentration levels. The degree of shift may be for the peak of the absorbance spectra curve of oxygenated hemoglobin from around 421 nm. In another example, the set of table  1802  may correlate a degree of absorption spectra shift of deoxygenated hemoglobin to NO concentration levels. The degree of shift may be for the peak of the absorbance spectra curve of deoxygenated hemoglobin from around 430 nm. 
     The calibration database  1800  may also include a set of calibration curves  1804  for a plurality of substances A-N. The calibration curves may correlate L values or R values or degree of shifts to concentration levels of the substances A-N. 
     The calibration database  1800  may also include calibration functions  1806 . The calibration functions  1806  may be derived (e.g., using regressive functions) from the correlation data from the calibration curves  1804  or the calibration tables  1802 . The calibration functions  1806  may correlate L values or R values or degree of shifts to concentration levels of the substances A-N for one or more underlying skin tissue types. 
     Embodiment—Detection of Blood Type Using R Values 
     Determination of blood groups is a vital factor for overall healthcare needs. The human race by nature has any one of the blood groups namely A, B, AB and O. The blood group “AB” is called the “Universal acceptor” and the people with the “O” group are called “Universal donors”. During blood transfusion any mismatch can lead to great harm or possible the death of a person. Hence it is very important for every person to identify his/her blood group. 
     There is no quick, non-invasive method to determine a patient&#39;s blood group. Known blood grouping methods are often a manual process with large &amp; expensive equipment. Within a hospital or blood bank center, a number of blood samples have to be identified within a short span of time. The manual process is a laborious and time-consuming. Thus, there is a need for a quick, convenient and non-invasive method to determine a blood group of a patient, either human or animal. 
     The biosensor  100  described herein may be configured to assess the blood group of a patient, human or animal, using the PPG circuit  110 . Blood type or group is represented by the ABO and Rh(D) systems. The A, B,  0 , AB blood type of a person depends on the presence or absence of two genes, A and B. These genes determine the configuration of the red blood cell surface. A person who has two A genes has red blood cells of type A. A person who has two B genes has red cells of type B. If the person has one A and one B gene, the red cells are type AB. If the person has neither the A nor the B gene, the red cells are type O. It is essential to match the ABO status of both donor and recipient in blood transfusions and organ transplants. In addition to the four main blood groups—A, B, AB and O, each group can either be RhD positive or RhD negative. Antigens are proteins on the surface of blood cells that can cause a response from the immune system. The Rh factor is a type of protein on the surface of red blood cells. Most people who have the Rh factor are Rh-positive. Those who do not have the Rh factor are Rh-negative. This means that in total there are eight main blood groups. The blood type of a patient exhibits different antigens present on the surface of red blood cells. The Rh factor is another type of protein on the surface of red blood cells. 
     In the last 30 years fully automated blood testing instruments have been developed and are in operation at blood centers and major hospitals. These instruments have advantages like high speed and sensitivity but they also require large size and high costs. These are major drawbacks. Development of a portable, low-cost, and sensitive instrument for blood typing is therefore required to make an on-site blood testing feasible. Aggulutination of RBCs (hemagglutination) is caused by an immune reaction between the RBCs and antibodies against the corresponding blood type. In conventional blood typing methods, hemagglutination caused by antibodies is detected by human eyes or by imaging techniques. Alternative methods of blood types using optical techniques have also been reported. 
     The optical properties of the different blood groups can be detected. The red blood cells comprise about 45% of the human blood. The color differences between the different blood groups are detectable. Thus, the optical differences between the different RBC groups (A, B, AB and O) can be determined using an optical sensor. For example, in the Armenian Journal of Physics, 2011, vol. 4, issue 3 pp. 165-168 shows a method for blood grouping detection using fiber optics. The basic premise of the method described is to use a laser operating at 820 nm to fire a series of pulses into a blood sample at 10 Khz, then using a photo diode, convert the optical variations back into electrical variations by amplifying, filtering, rectifying and feed the primary signal into a capacitor filter. This capacitor changes a voltage which is different for various blood groups. Since the different blood types have different optical spectrum characteristics, this method of fiber optic injection into a blood sample and then reading the approximate integration response of the corresponding signal shows a basic mathematical integration method is possible. However, this method requires a raw blood sample and is expensive and time consuming. 
     The spectral differences of the antigens present in the different blood types allow for several methods to be developed to determine the basic blood grouping using a multi-wavelength PPG circuit  110 . For example, since the different blood groups have different optical spectrums due to variances in the antigen groups, the PPG circuit  110  described herein may be used to measure the spectral response of blood flow using multiple wavelengths and comparing the R values to determine a particular blood group. The various R values indicate a presence of an antigen to identify a blood group of A, B,  0  or AB using the plurality of spectral responses. The PPG circuit may use the same R values or different R values to determine a presence of another antigen within a blood group to identify an RH factor using the plurality of spectral responses. 
     In an embodiment, the PPG circuit emits a series of pulses at a patient&#39;s tissue to obtain spectral responses at plurality of wavelengths. The spectral data is processed to obtain a series of R values. The series or average of the series of R values is used to identify a blood grouping or antigen group. The PPG circuit  110  pulses a series of LEDs at a rate of between 100-200 Hz to obtain a PPG signal. The PPG signal includes a series of pressure waveforms over a series of cardiac cycles. One or more of the following R values for 550/940 nm, 660/940 nm, and 880 nm/940 nm frequencies may be obtained over an integration of a series of the cardiac cycles. Due to the division of the L values, the R value eliminates the input from the skin tissue and non-pulsating blood flow to isolate the input from the pulsating blood flow (venous or arterial). To determine a blood group, the R values may be obtained over a sample window, such as over a plurality of pressure waveforms (e.g., generated over a plurality of cardiac cycles). A blood group indicator may be derived from the values of the R ratio over the sample window. For example, an integration of the R values over the sample window may be determined, and then the integrated R values used as the blood group indicator. The blood group indicator is then used to identify a blood group from one or more blood group reference tables. 
     In order to enhance the data signal of a spectral response, the data signal in a spectral response over a series of heart beats is used for the sample window. The R value may be obtained over the sample window using spectral responses around a plurality of frequencies. The frequencies may include, e.g., 550, 660 and 880 nm frequencies or in ranges of wavelengths around such frequencies. In one embodiment, the frequencies include 530 and 590 nm and values for the ratio R=L 530 /L 940  and R=L 590 /L 940  are determined over the sample window. The values for the first R 530/940  ratio are then integrated across the sample window to determine an integrated R value as a first blood group indicator. The values for the second R 590/940  ratio are then integrated across the sample window to determine an integrated R value as a second blood group indicator. A simple integration algorithm for each individual frequency may be implemented to obtain the blood group indicators. In another embodiment, the values for the R ratios are averaged over the sample window. Other functions using the values of the R ratios over the sample window may be implemented to obtain one or more blood group indicators. 
     The obtained one or more blood group indicators are then used with a calibration table to identify a blood group of the patient, human or animal. For example, the calibration table includes a correlation of values or ranges of the one or more blood group indicators to blood group or blood type. The calibration table may be determined by obtaining the blood group indicator for a sample general population for each known blood type. 
       FIG. 19A  illustrates a schematic drawing of values for the R 530/940  ratio  1900  obtained in a clinical setting using a biosensor  100  with a PPG circuit  110 . The biosensor  100  obtained values for the R 530/940  ratio  1900  over a sample window of approximately two minutes. In this embodiment, it was known that the patient had a blood type of B RH−. An average of the values for the R 530/940  ratio  1900  over the sample window was obtained. 
       FIG. 19B  illustrates a schematic drawing of the average values for the R 530/940  ratio  1902  obtained using the biosensor  100 . As seen in  FIGS. 19A and 19B , the values for the R 530/940  ratio are determined over the sample window. In this example, an average R value over the sample window is obtained between 1.88 and 1.855. 
       FIG. 20A  illustrates a schematic drawing of values for the R 590/940  ratio  2000  obtained using the biosensor  100 . The biosensor  100  obtained values for the R 590/940  ratio  2000  over a sample window of approximately two minutes. In this embodiment, it was known that the patient had a blood type of B RH−. An average of the values for the R 590/940  ratio  2000  over the sample window was obtained. 
       FIG. 20B  illustrates a schematic drawing of average values for the R 590/940  ratio  2002  obtained using the biosensor  100 . As seen in  FIGS. 20A and 20B , the values for the R 590/940  ratio are determined over the sample window. In this example, an average R value over the sample window is obtained between 1.308 and 1.32. 
     In one aspect, the blood type indicators for the patient may include an average R 530/940  value and an average R 530/940  value. Other blood type indicators may be derived from the values for the R 530/940  ratio and the values for the R 590/940  ratio. For example, other functions or processes, such as mean values, integral values, etc., obtained using the R values may also act as blood type indicators. 
       FIG. 21  illustrates a schematic drawing of an embodiment of a calibration table for a set of blood groups. The blood group reference table  2100  includes an expected or predetermined range of average values for R ratios for a set of blood groups types A, O, B, and AB. For example, the blood group reference table  2100  associates the average values for R ratios to a plurality of types of antigens on a surface of red blood cells. Each of the plurality of types of antigens corresponds to a blood type. By accessing the calibration table, a blood group may be identified. In this embodiment, the blood type indicators include an average R 530/940  value  2102  and an average R 530/940  value  2104 . The expected average values for the blood group indicators of R 530/940  ratio  2102  and R 590/940  ratio  2104  are shown for each of the blood groups A, O, B and AB. 
     To determine the blood group of a patient, a measured average R 530/940  value of the patient is compared to the blood group reference table  2100 . A measured average R 590/940  value of the patient may also be compared to the blood group reference table  2100 . For the patient in the example shown in  FIG. 19A , for the measured average R 530/940  value between 1.88 and 1.855, the Blood Group type of B may thus be determined using the blood group reference table  2100 . Similarly, using the example shown in  FIG. 19B , for the measured average R 590/940  value between 1.308 and 1.32, the Blood Group type of B may thus be determined using the blood group reference table  2100 . 
     Though the RH factor (RH+ and RH−) is not shown in this blood group reference table  2100 , a similar calibration graph or table may be used to determine the RH factor of each Blood Group A, B, AB and O. For example, the blood group A, B, AB and O may first be determined and then the RH+ and RH− type determined using the same or different blood type indicators. In another embodiment, the blood group A, B, AB and O and RH+ and RH− type may be determined using a same calibration table and blood type indicators. For example, values of the R ratio at 535 nm/940 nm may be used to detect either Rh+ or Rh−. 
     In another embodiment, though two blood type indicators are illustrated herein, three or more blood type indicators may be used to determine the blood type or a single blood type indicator may be used to determine the blood type. For example, a first blood type indicator may be determined and compared with the blood group reference table  2100 . If the first blood type indicator fails to correlate with an expected value for a blood type, one or more additional blood type indicators may be obtained and compared with the blood group reference table  2100 . In addition, though the blood group reference table  2100  illustrates a single predetermined value for each blood type indicator, the blood group reference table  2100  may indicate a range of predetermined values for one or more blood type indicators. 
     The various R values indicate a presence of an antigen to identify a blood group of A, B, O or AB using the plurality of spectral responses. The PPG circuit may use the same R values or different R values to determine a presence of another antigen within a blood group to identify an RH factor using the plurality of spectral responses. Though R values are described herein, other blood type indicators derived from a spectral response may also be implemented herein. 
       FIG. 22  illustrates a logical flow diagram of a method  2200  for obtaining a blood type using the biosensor  100 . The biosensor  100  obtains values for a first R ratio over a sample window at  2202 . The biosensor  100  obtains values for a second R ratio over a sample window at  2204 . The values of the first and second R ratios are used to obtain a first blood factor indicator and a second blood factor indicator at  2206 . For example, the blood factor indicator may be derived from an average, mean or integration of the values of the R ratio over the sample window. Other functions or processes may be used to determine a blood factor indicator from the values of an R ratio over a sample window. 
     The first blood factor indicator is compared to a range of expected or predetermined values for each of a plurality of blood groups in a calibration table at  2208 . For example, the calibration table associates the blood factor indicator to a plurality of types of antigens on a surface of red blood cells. Each of the plurality of types of antigens corresponds to a blood type. By accessing the calibration table, a blood group may be identified. The second blood factor indicator is compared to a range of expected or predetermined values for each of a plurality of blood groups in the calibration table at  2210 . Based on the first or second comparison or both, a blood group is identified at  2212 . 
     The biosensor  100  may thus be configured to determine a blood group A, B, O, AB and RH+ and RH− using one or more blood type indicators and a blood group reference table  2100 . A blood type indicator is obtained using values of an R ratio over a sample window. A different R ratio and blood type indicator may be used for comparison based on the blood group (such as, A, B, O, AB). For example, an expected range of values for a first blood type indicator derived from a first R ratio may be listed for blood group A while an expected range of values for a second blood type indicator derived from a second different R ratio may be listed for blood group O. The biosensor  100  may obtain the first and/or second blood type indicators in series or parallel and use the calibration table to determine the blood type. The various calibration tables, curves or other correlations may be stored in the calibration database  1800 . 
     Embodiment—Detection of Blood Type Using Signal Quality Parameters 
     The spectral differences of the antigens present on the surface of red blood cells in different blood types affects the quality of the PPG signal. For example, the reflectance of different types of surfaces of the red blood cells affects the scattering of light transmitted from the PPG circuit  110 . These differences in signal quality are measurable, especially in a reflectance PPG signal (vs. a transmissive PPG signal) due to the differing light scattering properties. In known solutions, an automatic gain filter or other digital signal processing compensates for the different qualities of the PPG signal. 
     However, when a uniform gain or filter is applied to the PPG signal from patients of different blood types, the differences in the signal strength and qualities of the PPG signal may be measured and used to determine the blood type. The different blood groups have different optical properties due to variances in the antigen groups on the surface of the red blood cells. Thus, the quality of the PPG signal quality is affected by the type of blood group due to the different antigens on the surface of the RBCs. Various parameters that measure signal quality or signal strength of the PPG signal may be determined and compared to predetermined values to determine the blood type. These differences in PPG signal quality are preferably determined at a similar gain or amplification. 
     There are various signal quality parameters that may be implemented to compare the differences in signal quality and strength of the PPG signal across blood types. For example, using a similar gain or amplification and other filtering or signal processing, the cross-correlation and auto-correlation of PPG signals, may be measured to determine different blood types. Other signal quality parameters may also be implemented to determine different blood types, such as a signal to noise ratio (SNR), skewness index (S SQI ), a kurtosis index (K SQI ), entropy (E SQI ), relative power, or other indices of signal quality or strength. An example of types of signal quality parameters that may be implemented herein are described in, Elgendi, Mohamed, “Optimal Signal Quality Index for Photoplethysmogram Signals.” Ed. Gou-Jen Wang. Bioengineering 3.4 (2016): 21, which is hereby incorporated by reference herein. The various signal quality parameters measure the signal quality and/or signal strength of the PPG signal. A signal quality parameter may be implemented as another blood type indicator. 
       FIG. 23A  illustrates a schematic graph  2300  of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type AB and RH−. In this example, the signal quality parameters include a cross-correlation  2302  and an auto-correlation  2306  of PPG signals. The cross correlation  2402  illustrates a cross correlation of a PPG signal obtained at 530 nm and a PPG signal obtained at 940 nm. The auto-correlation  2406  illustrates an auto-correlation of a PPG signal obtained at 530 nm. Though these PPG signals at 530 nm and 940 nm were used in this example, other PPG signals of other wavelengths may also be implemented. For example, for the cross correlation a PPG signal with a wavelength in the IR spectrum and another with a wavelength in the UV spectrum may be used. In this example of a person with patient with blood Type AB and RH−, the average value of the cross-correlation and the auto-correlation is approximately 0.2. 
       FIG. 23B  illustrates a schematic graph  2310  of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type B and RH−. In this example, the signal quality parameters include a cross-correlation  2312  and an auto-correlation  2314  of PPG signals. Preferably, when determining a blood type, the PPG signals and signal quality parameters are obtained at a same or similar gain as the PPG signals and signal quality parameters of other patients. Thus, the automatic gain control is disabled and a uniform gain or no gain is applied to the detected PPG signal. 
     The cross correlation  2312  illustrates a cross correlation of a PPG signal obtained at 530 nm and a PPG signal obtained at 940 nm. The auto-correlation  2314  illustrates an auto-correlation of a PPG signal obtained at 530 nm. Though these PPG signals at 530 nm and 940 nm were used in this example, other PPG signals of other wavelengths may also be implemented. For example, for the cross correlation, a PPG signal with a wavelength in the IR spectrum and another with a wavelength in the UV spectrum may be used. In this example of a person with patient with blood Type B and RH−, the average value of the cross-correlation and the auto-correlation is around 0.9. 
       FIG. 24A  illustrates a schematic graph  2400  of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood Type A and RH−. In this example, the signal quality parameters include a cross-correlation  2402  and an auto-correlation  2406  of PPG signals. Preferably, when determining a blood type, the PPG signals and signal quality parameters are obtained at a same or similar gain as the PPG signals and signal quality parameters of other patients, e.g. as in  FIGS. 23A and 23B . Thus, the automatic gain control is disabled and a uniform gain or no gain is applied to the detected PPG signal for determining a blood type. 
     The cross correlation  2402  illustrates a cross correlation of a PPG signal obtained at 530 nm and a PPG signal obtained at 940 nm. The auto-correlation  2406  illustrates an auto-correlation of a PPG signal obtained at 530 nm. Though these PPG signals at 530 nm and 940 nm were used in this example, other PPG signals of other wavelengths may also be implemented. For example, for the cross correlation function, a PPG signal with a wavelength in the visible spectrum (e.g., 660 nm) and another with a wavelength in the IR spectrum (e.g., 940 nm) may be used. In this example of a person with patient with blood Type A and RH−, the average value of the cross-correlation and the auto-correlation is around 0.3. 
       FIG. 24B  illustrates a schematic graph  2410  of an example of clinical data of signal quality parameters for detecting blood type in a patient with blood type O and RH+. In this example, the signal quality parameters include a cross-correlation  2412  and an auto-correlation  2414  of PPG signals. Preferably, when determining a blood type, the PPG signals and signal quality parameters are obtained at a same or similar gain as the PPG signals and signal quality parameters of other patients, e.g. as in  FIGS. 23A, 23B and 24A . Thus, the automatic gain control is disabled and a uniform gain or no gain is applied to the detected PPG signal for determining a blood type. 
     The cross correlation  2412  illustrates a cross correlation of a PPG signal obtained at 530 nm and a PPG signal obtained at 940 nm. The auto-correlation  2414  illustrates an auto-correlation of a PPG signal obtained at 530 nm. Though these PPG signals at 530 nm and 940 nm were used in this example, other PPG signals of other wavelengths may also be implemented. For example, for the cross correlation, a PPG signal with a wavelength in the IR spectrum and another with a wavelength in the UV spectrum may be used. In this example of a person with patient with blood Type O and RH+, the average value of the cross-correlation and the auto-correlation is around 0.23. 
       FIG. 25  illustrates a schematic graph  2500  of an embodiment of predetermined signal quality parameters for obtaining a blood type in a patient. In this graph  2500 , the average values  2510  of the auto and cross-correlations of PPG signals from various blood types  2512  is illustrated. The predetermined average values include, e.g., an approximate 0.2 average for AB Blood group  2502 , an approximate 0.3 average for A blood group  2504 , an approximate 0.9 average for B blood group  2506 , and an approximate 0.23 average for O blood group  2508 . Though the approximate averages are shown, a range of average values may be predetermined for the different blood groups. Alternatively, a mean, threshold value, or other parameter derived from the auto or cross correlation functions may be implemented. 
     In use, PPG signals are obtained from a patient at a plurality of wavelengths. The automatic gain control is disabled, and a uniform gain or no gain is applied to the PPG signal. The average value of the auto-correlation and cross correlation functions for the PPG signals are determined over a measurement window (such as 2-10 cardiac cycles). The obtained average value is compared to predetermined average values of the auto-correlation and cross correlation functions for each blood group. Based on the comparison, a blood group for the patients is determined. Thus, a signal quality parameter may be implemented as another blood type indicator. The predetermined values of the signal quality parameters may be stored in the calibration database  1800 . 
       FIG. 26  illustrates a logical flow diagram of an embodiment of a method  2600  for determining a blood group of a patient based on a signal quality parameter of a PPG signal. The state of the gain controller is changed or disabled at  2602 . For example, the automatic gain control is disabled, and a uniform gain or no gain is applied to the PPG signal across patients when determining blood group. In general, the PPG signal should have no gain applied or a similar gain applied for consistent measure and comparison of signal quality for blood typing. 
     One or more signal quality parameters are measured using one or more PPG signals at one or more wavelengths at  2604 . The signal quality parameters may relate to signal quality and/or signal strength of the PPG signal. For example, a signal to noise ratio of a PPG signal with a wavelength in an IR range may be determined. An average value of an auto-correlation of a PPG signal may be determined or a cross-correlation of two PPG signals at two different wavelengths may be determined. Other signal quality parameters may also be implemented to determine different blood types, such as a skewness index (S SQI ), a kurtosis index (K SQI ), entropy (E SQI ), relative power, or other indices of signal quality or strength. 
     Though the signal quality parameter of the PPG signal I AC+DC  is illustrated herein, the signal quality parameter may be measured from an isolated I AC  component of the PPG signal. For example, melatonin or other skin tone differences may affect the I DC  component of the PPG signal, but the I AC  component reflects the pulsating volume of arterial or venous blood. The signal quality parameter of the isolated I AC  component of the PPG signal may thus be used to determine a blood type as well. 
     The one or more measured signal quality parameters are compared to predetermined signal quality parameters for the one or more blood groups at  2606 . For example, a calibration database  1800  may be accessed that associates values of predetermined signal quality parameters to a plurality of blood groups (e.g., types of antigens on surfaces of red blood cells). Based on the comparison, a blood group is obtained for the user. The blood group may be determined based on a single comparison or multiple comparisons. 
     Upon completion of the blood typing process, the state of the automatic gain controller may be changed or enabled at  2610 . For example, the automatic gain controller or other varied gain may be applied to the PPG signal for determination of other patient vitals. 
       FIG. 27  illustrates a logical flow diagram of an embodiment of a method  2700  for determining a blood group of a patient using PPG technology. The biosensor  100  may be configured to identify the blood group of a patient, human or animal, using one or more different types of methods. A first identification of a blood type or group of a patient may be determined using signal quality parameters of a PPG signal at  2702 . A second identification of the blood type or group of a patient may also be determined using one or more R values at  2704 . The first identification and the second identification from the two methods may be compared to verify the blood type of the patient at  2706 . When the first and second identifications differ, one or more of the methods of identification may be repeated. When the results are still different, an error may be generated. 
     The biosensor  100  is thus able to non-invasively determine the blood type of a patient using PPG technology. The biosensor  100  provides a quick, convenient and easy to use tool for blood typing. 
     Embodiment—Adjustment of Measurements Based on Blood Type 
     The blood type varies the color of blood and may affect measurements of a PPG circuit  110 , including measurements of SpO 2  and other substances. For example, the R ratio derived from a 660 nm spectral response varies in response to a blood type of a patient. Thus, depending on the blood type, the average R value of a patient may differ and affect the SpO2 measurement. 
     In an embodiment herein, the biosensor  100  calibrates one or more measurements using the blood type of a patient. For example, the biosensor  100  may use the blood type of the patient as a factor in calibration tables when determining SpO 2 . In use, the biosensor  100  obtains a blood type of a patient. The biosensor  100  then obtains a first spectral response around approximately 660 nm and a second spectral response around approximately 940 nm and determines a ratio R 660/940 . The biosensor  100  then accesses a calibration table to determine an SpO 2  value using the ratio R 660/940  and blood type. The calibration table includes a different correlation between oxygen saturation SpO 2  values and R values for different blood types. 
       FIG. 28A  illustrates a schematic graph of an example R 660/940  values  2800  for a patient with blood type AB and RH factor—and with an SpO 2  of 97%. In this example, the patient was determined to have an oxygen saturation level SpO 2  of 97%. The biosensor  100  obtained a first spectral response around approximately 660 nm and a second spectral response around approximately 940 nm and determined the R 660/940  values  2800  over a measurement window. The average R 660/940  value over the measurement window was approximately 0.43 for the patient with blood type AB−. 
       FIG. 28B  illustrates a schematic graph of an established calibration curve  2810  between R 660/940  values and oxygen saturation SpO 2 . According to the established calibration curve  2810 , the R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% is about 0.575. Thus, the difference between the theoretical R 660/940  value of 0.575 and the measured R 660/940  value for the patient with blood type AB− is approximately −0.145. The calibration curve  2810  may thus be recalibrated as shown with point  2812  for AB− blood type. Point  2812  recalibrates an oxygen saturation SpO 2  of 97% to approximately 0.43. This recalibration may be performed for differing oxygen saturation SpO 2  values and R values for patients with AB− blood type. 
       FIG. 29A  illustrates a schematic graph of an example R 660/940  values  2900  for a patient with blood type B and RH factor—and with an SpO 2  of 97%. In this example, the patient was determined to have an oxygen saturation level SpO 2  of 97%. The biosensor  100  obtained a first spectral response around approximately 660 nm and a second spectral response around approximately 940 nm and determined the R 660/940  values  2900  over a measurement window. The average R 660/940  value over the measurement window was approximately 1.02 for the patient with the B− blood type. 
       FIG. 29B  illustrates a schematic graph of an established calibration curve  2910  between R 660/940  values and oxygen saturation SpO 2 . According to the established calibration curve  2910 , the R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% is about 0.575. Thus, the difference between the theoretical R 660/940  value of 0.575 and the measured R 660/940  value for the patient with blood type B− is approximately 0.445. The calibration curve  2910  may thus be recalibrated as shown with point  2912  for B− blood type. Point  2912  recalibrates an oxygen saturation SpO 2  of 97% to approximately 1.02. This recalibration may be performed for differing oxygen saturation SpO 2  values and R values to generate a calibration curve for patients with B− blood type. 
       FIG. 30A  illustrates a schematic graph of an example R 660/940  values  3000  for a patient with blood type A and RH factor—and with an SpO 2  of 97%. In this example, the patient was determined to have an oxygen saturation level SpO 2  of 97%. The biosensor  100  obtained a first spectral response around approximately 660 nm and a second spectral response around approximately 940 nm and determined the R 660/940  values  3000  over a measurement window. The average R 660/940  value over the measurement window was approximately 0.242 for the patient with the A− blood type. 
       FIG. 30B  illustrates a schematic graph of an established calibration curve  3010  between R 660/940  values and oxygen saturation SpO 2 . According to the established calibration curve  3010 , the R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% is about 0.575. Thus, the difference between the theoretical R 660/940  value of 0.575 and the measured R 660/940  value for the patient with blood type B− is approximately −0.333. The calibration curve  3010  may thus be recalibrated as shown with point  3012  for A− blood type. Point  3012  recalibrates an oxygen saturation SpO 2  of 97% to approximately 0.242. This recalibration may be performed for differing oxygen saturation SpO 2  values and R values to generate a calibration curve for patients with A− blood type. 
       FIG. 31A  illustrates a schematic graph of an example R 660/940  values  3100  for a patient with blood type O and RH factor+ and with an SpO 2  of 97%. In this example, the patient was determined to have an oxygen saturation level SpO 2  of 97%. The biosensor  100  obtained a first spectral response around approximately 660 nm and a second spectral response around approximately 940 nm and determined the R 660/940  values  3100  over a measurement window. The average R 660/940  value over the measurement window was approximately 0.65 for the patient with the O+ blood type. 
       FIG. 31B  illustrates a schematic graph of an established calibration curve  3110  between R 660/940  values and oxygen saturation SpO 2 . According to the established calibration curve  3110 , the R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% is about 0.575. Thus, the difference between the theoretical R 660/940  value of 0.575 and the measured R 660/940  value for the patient with blood type O+ is approximately 0.075. The calibration curve  3010  may thus be recalibrated as shown with point  3112  for O+ blood type. Point  3112  recalibrates an oxygen saturation SpO 2  of 97% to approximately 0.65. This recalibration may be performed for differing oxygen saturation SpO 2  values and R values to generate a calibration curve for patients with O+ blood type. 
       FIG. 32  illustrates a schematic graph  3200  of an error value of example R 660/940  values for patients with various blood types at an SpO 2  of 97%. According to the established calibration curve, the R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% is about 0.575. However, as described herein, the measured average R 660/940  value for a patient with an oxygen saturation SpO 2  of 97% differs depending on the blood type of the patient. For example, the error is approximately −0.145 for AB− blood type, approximately 0.445 for A− blood type, approximately −0.333 for B− blood type and approximately 0.075 for O+ blood type. The empirical data must thus be recalibrated for blood type, especially in PPG signals obtained from reflectance mode. 
     The spectral responses in  FIGS. 28-31  were detected from reflected light in a reflectance mode of operation of the biosensor  100 . In general, it was determined that spectral responses obtained from reflectance include a greater degree of error than from transmissive light in the R 660/940  values. Thus, to obtain more accurate readings of oxygen saturation levels, calibration curves or tables need to be used for different blood types. 
       FIG. 33  illustrates a logical flow diagram of an embodiment of a method  3300  for determining a calibration curve of SpO 2  for a blood type. The blood type of a general population is tested to identify a group of patients with a specific blood type at  3302 . The average R 660/940  value for a patient is determined and the SpO 2  level using a reliable method independent of PPG technology at  3304 . A range of SpO 2  levels are determined with corresponding average R 660/940  values for the patients with a specific blood type. A recalibration error may be determined to a standard calibration curve at  3306  for the specific blood type. The recalibration error may vary depending on the SpO 2  level. Alternatively or in addition thereto, a calibration curve may be generated for the specific blood type that associates the average R 660/940  values for the patients with the measured SpO 2  level. Though the average R 660/940  values are illustrated herein, other R values may also be correlated to SpO 2  levels for a specific blood type, e.g. R 590/940  or R 660/920 , etc. 
     Though described herein for SpO 2  levels, a similar calibration process may be performed for other patient vitals. For example, the biosensor  100  determines a patient vital using a known method and then determines an average R value of the patient. The biosensor  100  correlates the average R value to the value of the patient vital for the blood type of the user. The biosensor  100  may then store the correlation in a calibration table for the specific blood type in a calibration database  1800 . The patient vital may include a concentration level of nitric oxide (NO) in the blood stream, a concentration level of a liver enzyme in the blood stream, a concentration level of glucose, a concentration level of an electrolyte, a concentration of one or more species of hemoglobin, or a concentration level of another substance in the blood stream. 
       FIG. 34  illustrates a logical flow diagram of a method  3400  for determining an oxygen saturation level of a patient. The blood type of the patient is obtained at  3402 . The patient may input a known blood type or the biosensor  100  may one or methods as described herein to obtain the blood type of the patient. The value for an R ratio for an SpO 2  measurement are obtained at  3404 , e.g., the average R 660/940  values or other R values correlated to SpO 2  levels, e.g. R 590/940  or R 660/920 , etc. A calibration table or curve that includes calibration values for a specific blood type between the R value and SpO 2  level is then accessed at  3406 . The SpO 2  measurement is then obtained for the patient at  3408 . 
     The blood type of a patient may affect measurements other than oxygen saturation SpO 2 . For example, measurements using PPG technology of nitric oxide (NO) levels, liver enzyme (P450) levels, etc. may be affected by blood type. The biosensor  100  may thus determine calibrations based on blood type for one or more different types of measurements, as shown the calibration database  1800  in  FIG. 18 . 
     Alternatively, other types of measurements (e.g., absorption spectra shift) using a plurality of spectral responses may be calibrated based on blood type. For example, the calibration database  1800  may include different values for an R ratio that correlate to a patient vital for different blood groups. In another example, the calibration database  1800  may include different values for an absorption spectra shift that correlate to a patient vital for different blood groups. The patient vital may include an oxygen saturation level, a concentration level of another substance, or other measurement. 
       FIG. 35  illustrates a logical flow diagram of a method  3500  for determining a patient measurement using a blood type of the patient. The biosensor  100  obtains a blood type of the patient at  3502 . A known blood type of the patient may be input or stored in a database of the biosensor  100 , or the biosensor  100  may use the PPG circuit  110  as described herein to obtain the blood type of the patient. 
     Based on the blood type, the biosensor  100  may modify operation of the PPG circuit  110  or other components of the biosensor  100  at  3504 . For example, a different frequency, amplification or intensity of light may be used to obtain one or more spectral responses depending on the blood type. The biosensor  100  then obtains values for an R ratio or other type of measurements (e.g., absorption spectra shift) using a plurality of spectral responses at  3506 . Based on the measurement and the blood type, a patient vital is determined from a calibration table stored in a calibration database  1800 . The calibration database  1800  includes calibrations based on blood type of the user, e.g., a plurality of calibrations that associate predetermined ratio R values to a patient vital for a plurality of specific blood types. For example, the calibration table may include a correlation of R ratio values to SpO 2  levels for a plurality of different blood groups. In another example, the calibration table may include values for an absorption spectra shift that correlate to a patient vital for different blood groups. The patient vital may include an oxygen saturation SpO 2 , a concentration level of nitric oxide (NO) in the blood stream, a concentration level of a liver enzyme in the blood stream, a concentration level of glucose, a concentration level of an electrolyte, a concentration of one or more species of hemoglobin, or a concentration level of another substance in the blood stream. 
       FIG. 36  illustrates a logical flow diagram of an exemplary embodiment of a method  3600  for detecting an abnormal condition. In an embodiment, an abnormal condition may be detected upon a change of a predetermined value of a blood type indicator. For example, the biosensor  100  may store a known blood type of a user or an average or normal value for a blood type indicator of the user. When the expected or normal value of the blood type indicator of the user changes, it may signal an abnormal condition. In particular, various conditions may affect the red blood cells, such as a Hemolytic or Non-Hemolytic staph infection. The red blood cells (RBCs) exhibit abnormal optical properties in hemolytic staph infections versus Non-Hemolytic varieties of Staph. For example, the RBC exhibit an abnormal composition since a destruction of RBCs occurs during the infection period. 
     Hemolysis is the rupturing of RBCs and the release of their contents (cytoplasm) into surrounding fluid (e.g. blood plasma). So, an abnormal count and color of the RBCs can be observed in the PPG signal of a spectral response. If as little as 0.5% of the red blood cells are hemolyzed, the released hemoglobin will cause the serum or plasma to change color, e.g. appear pale red or cherry red in color. The PPG circuit may measure the effect of hemolysis due to the color change and determine an abnormal condition. 
     Additionally, the biosensor  100  may non-invasively measure a temperature of a patient, at periodic intervals, e.g. one or more times per minute. The biosensor  100  may also monitor liver enzyme levels or nitric oxide (NO) levels in blood flow of a user, at periodic intervals, e.g. one or more times per minute. The PPG circuit may also determine other user vitals, such as heart rate, vasodilation and respiration rate. The one or more various measured parameters are compared to predetermined thresholds to determine an abnormal condition is present, such as an infection, anemia, genetic disease (e.g., sickle cell anemia), malaria, snake venom, toxic chemicals, tick-borne diseases, or blackwater fever. 
     In an embodiment, a predetermined value of a blood type indicator is obtained by the biosensor  100  at  3602 . For example, the biosensor  100  may store a measured blood type indicator of a user in a memory device  104 . Alternatively, a blood type (ABO and/or RH factor) of the user is stored in a memory device or communicated to the biosensor  100  via user input or from another device. The biosensor  100  may then determine a predetermined range of a blood type indicator from the blood type and a calibration database. For example, the biosensor  100  may access the calibration database to determine an expected range of values for an R 530/940  ratio or R 590/940  ratio value or a signal quality parameter for a specific blood type. 
     The biosensor  100  then measures the blood type indicator of the user at  3604  using one or more methods described herein. The blood type indicator may include an R ratio value, signal quality parameter or other parameter derived from a PPG signal of a spectral response. The measured blood type indicator is compared to the predetermined value of the blood type indicator at  3606 . The predetermined value of the blood type indicator may include a normal range or threshold for the blood type indicator. The predetermined value may include an expected value of the indicator for the specific blood type stored in the calibration database. Or the predetermined value may be based on differences from one or more stored values of previous measurements for the user, such as a percentage difference or other predetermined threshold. 
     When the measured blood type indicator is not within a normal range, an alert may be generated at  3608 . The alert may indicate that the blood type indicator is not within a normal range for the user or exceeds expected values for the specific blood type stored in the calibration database. The biosensor  100  thus determines an abnormal condition and generates an error or alert message. 
     In one or more aspects herein, a processing module or circuit includes at least one processing device, such as a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. A memory is a non-transitory memory device and may be an internal memory or an external memory, and the memory may be a single memory device or a plurality of memory devices. The memory may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any non-transitory memory device that stores digital information. 
     As may be used herein, the term “operable to” or “configurable to” indicates that an element includes one or more of circuits, instructions, modules, data, input(s), output(s), etc., to perform one or more of the described or necessary corresponding functions and may further include inferred coupling to one or more other items to perform the described or necessary corresponding functions. As may also be used herein, the term(s) “coupled”, “coupled to”, “connected to” and/or “connecting” or “interconnecting” includes direct connection or link between nodes/devices and/or indirect connection between nodes/devices via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, a module, a node, device, network element, etc.). As may further be used herein, inferred connections (i.e., where one element is connected to another element by inference) includes direct and indirect connection between two items in the same manner as “connected to”. 
     As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent and corresponds to, but is not limited to, frequencies, wavelengths, component values, integrated circuit process variations, temperature variations, rise and fall times, and/or thermal noise. Such relativity between items ranges from a difference of a few percent to magnitude differences. 
     Note that the aspects of the present disclosure may be described herein as a process that is depicted as a schematic, a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function. 
     The various features of the disclosure described herein can be implemented in different systems and devices without departing from the disclosure. It should be noted that the foregoing aspects of the disclosure are merely examples and are not to be construed as limiting the disclosure. The description of the aspects of the present disclosure is intended to be illustrative, and not to limit the scope of the claims. As such, the present teachings can be readily applied to other types of apparatuses and many alternatives, modifications, and variations will be apparent to those skilled in the art. 
     In the foregoing specification, certain representative aspects of the invention have been described with reference to specific examples. Various modifications and changes may be made, however, without departing from the scope of the present invention as set forth in the claims. The specification and figures are illustrative, rather than restrictive, and modifications are intended to be included within the scope of the present invention. Accordingly, the scope of the invention should be determined by the claims and their legal equivalents rather than by merely the examples described. For example, the components and/or elements recited in any apparatus claims may be assembled or otherwise operationally configured in a variety of permutations and are accordingly not limited to the specific configuration recited in the claims. 
     Furthermore, certain benefits, other advantages and solutions to problems have been described above about particular embodiments; however, any benefit, advantage, solution to a problem, or any element that may cause any particular benefit, advantage, or solution to occur or to become more pronounced are not to be construed as critical, required, or essential features or components of any or all the claims. 
     As used herein, the terms “comprise,” “comprises,” “comprising,” “having,” “including,” “includes” or any variation thereof, are intended to reference a nonexclusive inclusion, such that a process, method, article, composition or apparatus that comprises a list of elements does not include only those elements recited, but may also include other elements not expressly listed or inherent to such process, method, article, composition, or apparatus. Other combinations and/or modifications of the above-described structures, arrangements, applications, proportions, elements, materials, or components used in the practice of the present invention, in addition to those not specifically recited, may be varied or otherwise particularly adapted to specific environments, manufacturing specifications, design parameters, or other operating requirements without departing from the general principles of the same. 
     Moreover, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is intended to be construed under the provisions of 35 U.S.C. § 112(f) as a “means-plus-function” type element, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”