Patent Publication Number: US-2012029363-A1

Title: Systems and methods for improved computation of differential pulse transit time from photoplethysmograph signals

Description:
SUMMARY 
     The present disclosure may relate to processing photoplethysmograph (PPG) signals and, more particularly, to systems and methods for computing a differential pulse transit time (DPTT) from a pair of PPG signals. 
     In an embodiment, probes or sensors may detect first and second PPG signals. The PPG signals may be detected at any suitable locations. For example, a first probe or sensor may detect a first PPG signal at a subject&#39;s earlobe, while a second probe or sensor may detect a second PPG signal at a subject&#39;s fingertip. These PPG signals may be processed to determine a DPTT, which may in turn be used to determine a blood pressure measurement. 
     In an embodiment, a maximum correlation algorithm may be performed based on the first and second PPG signals. A confidence measure may be determined using a first output generated by the maximum correlation algorithm. For example, the confidence measure may be indicative of how far a first derivative of the first PPG signal must be shifted in time relative to a first derivative of the second PPG signal in order to maximize correlation between the two first derivative waveforms. As another example, the confidence measure may be indicative of how highly correlated a first derivative of the first PPG signal is with a first derivative of the second PPG signal after one of the first derivative waveforms is shifted in time relative to the other. As another example, the confidence measure may be determined based on a combination of confidence measures. 
     In an embodiment, if the confidence measure is in a first numerical range, the DPTT may be determined based on the maximum correlation algorithm. In the example where the confidence measure may be indicative of a time shift that is required to maximize some measure of correlation based on the first and second PPG signals, the first numerical range may contain numbers greater than zero, corresponding to an indication that the first PPG signal (or a signal derived therefrom) must be shifted forward in time to maximize correlation with the second PPG signal (or a signal derived therefrom). If the first PPG signal corresponds to a measurement taken at a subject&#39;s earlobe and the second PPG signal corresponds to a measurement taken at a subject&#39;s fingertip, such a forward shift may be consistent with the observation that a given beat of the subject&#39;s heart usually produces a pulse at the subject&#39;s earlobe before producing a corresponding pulse at the subject&#39;s fingertip. In the example where the confidence measure may be indicative of how highly correlated the first PPG signal (or a signal derived therefrom) is with the second PPG signal (or a signal derived therefrom) after a time shift is performed, the first numerical range can include numbers greater than a certain threshold, corresponding to a relatively high degree of correlation between the PPG signals (or signals derived therefrom) after an appropriate time shift. 
     In an embodiment, if the confidence measure is in a second numerical range, an alternative algorithm may be performed based on the first and second PPG signals (or signals derived therefrom) and the DPTT may be determined based on the alternative algorithm. In the example where the confidence measure may be indicative of a time shift that is required to maximize some measure of correlation, the second numerical range may contain numbers less than zero, corresponding to an indication that the first PPG signal (or a signal derived therefrom) must be shifted backwards in time to maximize correlation with the second PPG signal (or a signal derived therefrom). If the first PPG signal corresponds to a measurement taken at a subject&#39;s earlobe and the second PPG signal corresponds to a measurement taken at a subject&#39;s fingertip, such a backward shift may be inconsistent with the observation that a given beat of the subject&#39;s heart usually produces a pulse at the subject&#39;s earlobe before producing a corresponding pulse at the subject&#39;s fingertip. In the example where the confidence measure may be indicative of how highly correlated the first PPG signal (or a signal derived therefrom) is with the second PPG signal (or a signal derived therefrom) after a time shift is performed, the second numerical range can include numbers less than a certain threshold, corresponding to a relatively low degree of correlation between the PPG signals (or signals derived therefrom) after an appropriate time shift. 
     In an embodiment, the alternative algorithm may be any suitable algorithm for determining a DPTT. For example, the alternative algorithm may include computing a second derivative of each of the first and second PPG signals, identifying a first set of peaks in the second derivative of the first PPG signal, identifying a second set of peaks in the second derivative of the second PPG signal, determining an average time difference between respective peaks in the first and second set of peaks, and outputting the average time difference as the DPTT. As another example, if the maximum correlation algorithm is performed using first derivatives of the first and second PPG signals, the alternative algorithm may include performing another maximum correlation algorithm based on the raw first and second PPG signals (without any derivative operations) or based on second derivatives of the first and second PPG signals. Any suitable DPTT algorithms may be used as the primary algorithm and the alternative algorithm. 
     In an embodiment, a system for processing first and second PPG signals to determine a DPTT may include a sensor (e.g., a pulse oximeter) capable of generating the PPG signal and a processor. The processor may be capable of receiving the first and second PPG signals, performing a maximum correlation algorithm based on the first and second PPG signal, and determining a confidence measure using a first output generated by the maximum correlation algorithm. The processor may further be capable of determining the DPTT based on the maximum correlation algorithm if the confidence measure is in a first numerical range. The processor may further be capable of performing an alternative algorithm based on the first and second PPG signals and determining the DPTT based on the alternative algorithm if the confidence measure is in a second numerical range different from the first numerical range. 
     In an embodiment, a computer-readable medium for processing first and second PPG signals to determine a DPTT may include computer program instructions. The computer program instructions recorded on the computer-readable medium may include instructions for receiving the first and second PPG signals, performing a maximum correlation algorithm based on the first and second PPG signal, and determining a confidence measure using a first output generated by the maximum correlation algorithm. The computer program instructions may further include instructions for determining the DPTT based on the maximum correlation algorithm if the confidence measure is in a first numerical range. The computer program instructions may further include instructions for performing an alternative algorithm based on the first and second PPG signals and determining the DPTT based on the alternative algorithm if the confidence measure is in a second numerical range different from the first numerical range. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other features of the present disclosure, its nature and various advantages will be more apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings in which: 
         FIG. 1  shows an illustrative CNIBP monitoring system in accordance with an embodiment; 
         FIG. 2  is a block diagram of the illustrative CNIBP monitoring system of  FIG. 1  coupled to a patient in accordance with an embodiment; 
         FIG. 3  is a block diagram of an illustrative signal processing system in accordance with an embodiment; 
         FIG. 4  shows an illustrative PPG signal in accordance with an embodiment; 
         FIG. 5  shows an illustrative process for determining blood pressure from PPG signals in accordance with an embodiment; 
         FIG. 6  shows an illustrative process for determining a DPTT measurement from PPG signals in accordance with an embodiment; 
         FIG. 7  shows a first illustrative alternative algorithm for determining a DPTT measurement from PPG signals in accordance with an embodiment; 
         FIG. 8  shows a second illustrative alternative algorithm for determining a DPTT measurement from PPG signals in accordance with an embodiment; 
         FIG. 9  shows a set of illustrative waveforms depicting blood pressure determination using a DPTT measurement computed with a default maximum correlation algorithm in accordance with an embodiment; and 
         FIG. 10  shows a set of illustrative waveforms depicting blood pressure determination using a DPTT measurement computed with an alternative algorithm in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Some CNIBP monitoring techniques may utilize two probes or sensors positioned at two different locations on a subject&#39;s body. The elapsed time, T, between the arrivals of corresponding points of a pulse signal at the two locations may then be determined using signals obtained by the two probes or sensors. The estimated blood pressure, p, may then be related to the elapsed time, T, by 
         p=a+b· ln( T )  (1)
 
     where a and b are constants that may be dependent upon the nature of the subject and the nature of the signal detecting devices. Other suitable equations using an elapsed time between corresponding points of a pulse signal may also be used to derive an estimated blood pressure measurement. In some embodiments, a single probe or sensor may be used, in which case the variable T in equation (1) would represent the time between two characteristic points within a single detected PPG signal. In still other embodiments, the area under at least part of a detected PPG signal may be used to compute blood pressure instead of time. 
       FIG. 1  is a perspective view of an embodiment of a CNIBP monitoring system  10  that may also be used to perform pulse oximetry. System  10  may include sensors  12  and  13  and a monitor  14 . Sensor  12  may include an emitter  16  for emitting light at one or more wavelengths into a patient&#39;s tissue. A detector  18  may also be provided in sensor  12  for detecting the light originally from emitter  16  that emanates from the patient&#39;s tissue after passing through the tissue. Similarly, sensor  13  may include an emitter  17  and a detector  19 , which may operate in a fashion similar to that of emitter  16  and detector  18 , respectively. 
     Sensors  12  and  13  may be attached to different locations of a patient&#39;s body in order to measure values for time T in equation (1) above and thereby facilitate measurement of the patient&#39;s blood pressure. As an example, sensor  12  may be attached to the patient&#39;s fingertip, while sensor  13  may be attached to the patient&#39;s earlobe. It will be appreciated that other sensor locations may be used, as appropriate, and in some embodiments, only a single sensor or probe may be used. 
     According to an embodiment, emitter  16  and detector  18  may be on opposite sides of a digit such as a finger or toe, in which case the light that is emanating from the tissue has passed completely through the digit. In an embodiment, detector  18  (e.g., a reflective sensor) may be positioned anywhere a strong pulsatile flow may be detected (e.g., over arteries in the neck, wrist, thigh, ankle, ear, or any other suitable location). In an embodiment, emitter  16  and detector  18  may be arranged so that light from emitter  16  penetrates the tissue and is reflected by the tissue into detector  18 , such as a sensor designed to obtain pulse oximetry or CNIBP data from a patient&#39;s forehead. 
     Similarly, according to an embodiment, emitter  17  and  19  may be on opposite sides of an ear (e.g., positioned on opposite sides of a patient&#39;s earlobe). In an embodiment, emitter  17  and detector  19  may be arranged so that light from emitter  17  penetrates the tissue and is reflected by the tissue into detector  19 , such as a sensor designed to obtain pulse oximetry or CNIBP data from a patient&#39;s forehead. 
     According to another embodiment, system  10  may include a plurality of sensors forming a sensor array in lieu of either or both of sensors  12  and  13 . Each of the sensors of the sensor array may be a complementary metal oxide semiconductor (CMOS) sensor. Alternatively, each sensor of the array may be charged coupled device (CCD) sensor. In another embodiment, the sensor array may be made up of a combination of CMOS and CCD sensors. The CCD sensor may comprise a photoactive region and a transmission region for receiving and transmitting data whereas the CMOS sensor may be made up of an integrated circuit having an array of pixel sensors. Each pixel may have a photodetector and an active amplifier. 
     In an embodiment, the sensors or sensor array may be connected to and draw its power from monitor  14  as shown. In another embodiment, the sensors may be wirelessly connected to monitor  14  and may each include its own battery or similar power supply (not shown). Monitor  14  may be configured to calculate physiological parameters (e.g., blood pressure) based at least in part on data received from sensors  12  and  13  relating to light emission and detection. In an alternative embodiment, the calculations may be performed on the monitoring device itself and the result of the light intensity reading may be passed to monitor  14 . Further, monitor  14  may include a display  20  configured to display the physiological parameters or other information about the system. In the embodiment shown, monitor  14  may also include a speaker  22  to provide an audible sound that may be used in various other embodiments, such as for example, sounding an audible alarm in the event that a patient&#39;s physiological parameters are not within a predefined normal range. 
     In an embodiment, sensors  12  and  13  may be communicatively coupled to monitor  14  via cables  24  and  25 , respectively. However, in other embodiments, a wireless transmission device (not shown) or the like may be used instead of or in addition to either or both of cables  24  and  25 . 
     In the illustrated embodiment, system  10  may also include a multi-parameter patient monitor  26 . The monitor may be cathode ray tube type, a flat panel display (as shown) such as a liquid crystal display (LCD) or a plasma display, or any other type of monitor now known or later developed. Multi-parameter patient monitor  26  may be configured to calculate physiological parameters and to provide a display  28  for information from monitor  14  and from other medical monitoring devices or systems (not shown). For example, multi-parameter patient monitor  26  may be configured to display an estimate of a patient&#39;s blood pressure from monitor  14 , blood oxygen saturation generated by monitor  14  (referred to as an “SpO 2 ” measurement), and pulse rate information from monitor  14 . 
     Monitor  14  may be communicatively coupled to multi-parameter patient monitor  26  via a cable  32  or  34  that is coupled to a sensor input port or a digital communications port, respectively and/or may communicate wirelessly (not shown). In addition, monitor  14  and/or multi-parameter patient monitor  26  may be coupled to a network to enable the sharing of information with servers or other workstations (not shown). Monitor  14  may be powered by a battery (not shown) or by a conventional power source such as a wall outlet. 
       FIG. 2  is a block diagram of a CNIBP monitoring system, such as system  10  of  FIG. 1 , which may be coupled to a patient  40  in accordance with an embodiment. Certain illustrative components of sensors  12  and  13  and monitor  14  are illustrated in  FIG. 2 . Because sensors  12  and  13  may include similar components and functionality, only sensor  12  will be discussed in detail for ease of illustration. It will be understood that any of the concepts, components, and operation discussed in connection with sensor  12  may be applied to sensor  13  as well (e.g., emitter  16  and detector  18  of sensor  12  may be similar to emitter  17  and detector  19  of sensor  13 ). Similarly, it will be understood that, as discussed in connection with  FIG. 1 , certain embodiments may use only a single sensor or probe, instead of a plurality of sensors or probes as illustrated in  FIG. 2 . 
     Sensor  12  may include emitter  16 , detector  18 , and encoder  42 . In the embodiment shown, emitter  16  may be configured to emit at least one wavelength of light (e.g., RED or IR) into a patient&#39;s tissue  40 . For calculating SpO 2 , emitter  16  may include a RED light emitting light source such as RED light emitting diode (LED)  44  and an IR light emitting light source such as IR LED  46  for emitting light into the patient&#39;s tissue  40 . In other embodiments, emitter  16  may include a light emitting light source of a wavelength other than RED or IR. In one embodiment, the RED wavelength may be between about 600 nm and about 700 nm, and the IR wavelength may be between about 800 nm and about 1000 nm. In embodiments where a sensor array is used in place of single sensor, each sensor may be configured to emit a single wavelength. For example, a first sensor emits only a RED light while a second only emits an IR light. 
     It will be understood that, as used herein, the term “light” may refer to energy produced by radiative sources and may include one or more of ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation. As used herein, light may also include any wavelength within the radio, microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any suitable wavelength of electromagnetic radiation may be appropriate for use with the present techniques. Detector  18  may be chosen to be specifically sensitive to the chosen targeted energy spectrum of the emitter  16 . 
     In an embodiment, detector  18  may be configured to detect the intensity of light at the emitted wavelengths (or any other suitable wavelength). Alternatively, each sensor in the array may be configured to detect an intensity of a single wavelength. In operation, light may enter detector  18  after passing through the patient&#39;s tissue  40 . Detector  18  may convert the intensity of the received light into an electrical signal. The light intensity is directly related to the absorbance and/or reflectance of light in the tissue  40 . That is, when more light at a certain wavelength is absorbed or reflected, less light of that wavelength is received from the tissue by the detector  18 . After converting the received light to an electrical signal, detector  18  may send the signal to monitor  14 , where physiological parameters may be calculated based on the absorption of one or more of the RED and IR (or other suitable) wavelengths in the patient&#39;s tissue  40 . 
     In an embodiment, encoder  42  may contain information about sensor  12 , such as what type of sensor it is (e.g., whether the sensor is intended for placement on a forehead or digit) and the wavelength or wavelengths of light emitted by emitter  16 . This information may be used by monitor  14  to select appropriate algorithms, lookup tables and/or calibration coefficients stored in monitor  14  for calculating the patient&#39;s physiological parameters. 
     Encoder  42  may contain information specific to patient  40 , such as, for example, the patient&#39;s age, weight, and diagnosis. This information may allow monitor  14  to determine, for example, patient-specific threshold ranges in which the patient&#39;s physiological parameter measurements should fall and to enable or disable additional physiological parameter algorithms. Encoder  42  may, for instance, be a coded resistor which stores values corresponding to the type of sensor  12  or the type of each sensor in the sensor array, the wavelength or wavelengths of light emitted by emitter  16  on each sensor of the sensor array, and/or the patient&#39;s characteristics. In another embodiment, encoder  42  may include a memory on which one or more of the following information may be stored for communication to monitor  14 : the type of the sensor  12 ; the wavelength or wavelengths of light emitted by emitter  16 ; the particular wavelength each sensor in the sensor array is monitoring; a signal threshold for each sensor in the sensor array; any other suitable information; or any combination thereof. 
     In an embodiment, signals from detector  18  and encoder  42  may be transmitted to monitor  14 . In the embodiment shown, monitor  14  may include a general-purpose microprocessor  48  connected to an internal bus  50 . Microprocessor  48  may be adapted to execute software, which may include an operating system and one or more applications, as part of performing the functions described herein. Also connected to bus  50  may be a read-only memory (ROM)  52 , a random access memory (RAM)  54 , user inputs  56 , display  20 , and speaker  22 . 
     RAM  54  and ROM  52  are illustrated by way of example, and not limitation. Any suitable computer-readable media may be used in the system for data storage. Computer-readable media are capable of storing information that can be interpreted by microprocessor  48 . This information may be data or may take the form of computer-executable instructions, such as software applications, that cause the microprocessor to perform certain functions and/or computer-implemented methods. Depending on the embodiment, such computer-readable media may include computer storage media and communication media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media may include, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by components of the system. 
     In the embodiment shown, a time processing unit (TPU)  58  may provide timing control signals to a light drive circuitry  60 , which may control when emitter  16  is illuminated and multiplexed timing for the RED LED  44  and the IR LED  46  for each sensor. TPU  58  may also control the gating-in of signals from detector  18  through an amplifier  62  and a switching circuit  64 . These signals are sampled at the proper time, depending upon which light source is illuminated. The received signal from detector  18  may be passed through an amplifier  66 , a low pass filter  68 , and an analog-to-digital converter  70 . The digital data may then be stored in a queued serial module (QSM)  72  (or buffer) for later downloading to RAM  54  as QSM  72  fills up. In one embodiment, there may be multiple separate parallel paths having amplifier  66 , filter  68 , and A/D converter  70  for multiple light wavelengths or spectra received. 
     In an embodiment, microprocessor  48  may determine the patient&#39;s physiological parameters, such as blood pressure, SpO 2 , and pulse rate, using various algorithms and/or look-up tables based on the value of the received signals and/or data corresponding to the light received by detector  18 . Signals corresponding to information about patient  40 , and particularly about the intensity of light emanating from a patient&#39;s tissue over time, may be transmitted from encoder  42  to a decoder  74 . These signals may include, for example, encoded information relating to patient characteristics. Decoder  74  may translate these signals to enable the microprocessor to determine the thresholds based on algorithms or look-up tables stored in ROM  52 . User inputs  56  may be used to enter information about the patient, such as age, weight, height, diagnosis, medications, treatments, and so forth. In an embodiment, display  20  may exhibit a list of values which may generally apply to the patient, such as, for example, age ranges or medication families, which the user may select using user inputs  56 . 
     The optical signal through the tissue can be degraded by noise, among other sources. One source of noise is ambient light that reaches the light detector. Another source of noise is electromagnetic coupling from other electronic instruments. Movement of the patient also introduces noise and affects the signal. For example, the contact between the detector and the skin, or the emitter and the skin, can be temporarily disrupted when movement causes either to move away from the skin. In addition, because blood is a fluid, it responds differently than the surrounding tissue to inertial effects, thus resulting in momentary changes in volume at the point to which the sensor or probe is attached. 
     Noise (e.g., from patient movement) can degrade a CNIBP or pulse oximetry signal relied upon by a physician, without the physician&#39;s awareness. This is especially true if the monitoring of the patient is remote, the motion is too small to be observed, or the doctor is watching the instrument or other parts of the patient, and not the sensor site. Processing CNIBP or pulse oximetry (i.e., PPG) signals may involve operations that reduce the amount of noise present in the signals or otherwise identify noise components in order to prevent them from affecting measurements of physiological parameters derived from the PPG signals. 
       FIG. 3  is an illustrative processing system  300  in accordance with an embodiment. In this embodiment, input signal generator  310  generates an input signal  316 . As illustrated, input signal generator  310  may include oximeter  320  (or similar device) coupled to sensor  318 , which may provide as input signal  316  a PPG signal. It will be understood that input signal generator  310  may include any suitable signal source, signal generating data, signal generating equipment, or any combination thereof to produce signal  316 . Additionally, input signal generator  310  may in some embodiments include more than one sensor  318 . 
     In this embodiment, signal  316  may be coupled to processor  312 . Processor  312  may be any suitable software, firmware, and/or hardware, and/or combinations thereof for processing signal  316 . For example, processor  312  may include one or more hardware processors (e.g., integrated circuits), one or more software modules, computer-readable media such as memory, firmware, or any combination thereof. Processor  312  may, for example, be a computer or may be one or more chips (i.e., integrated circuits). Processor  312  may perform some or all of the calculations associated with the blood pressure monitoring methods of the present disclosure. For example, processor  312  may determine the time difference, T, between any two chosen characteristic points of a PPG signal obtained from input signal generator  310 . As another example, if input signal generator contains more than one sensor  318 , processor  312  may determine the time difference, T, required for a PPG signal to travel from one sensor  318  to another. Processor  312  may also be configured to apply equation (1) (or any other blood pressure equation using an elapsed time value) and compute estimated blood pressure measurements on a continuous or periodic basis. Processor  312  may also perform any suitable signal processing of signal  316  to filter signal  316 , such as any suitable band-pass filtering, adaptive filtering, closed-loop filtering, and/or any other suitable filtering, and/or any combination thereof. For example, signal  316  may be filtered one or more times prior to or after identifying characteristic points in signal  316 . 
     Processor  312  may be coupled to one or more memory devices (not shown) or incorporate one or more memory devices such as any suitable volatile memory device (e.g., RAM, registers, etc.), non-volatile memory device (e.g., ROM, EPROM, magnetic storage device, optical storage device, flash memory, etc.), or both. Processor  312  may perform initial calibration, recalibration, or both of the CNIBP measuring system, using information received from input signal generator  310  or any other suitable device. 
     Processor  312  may be coupled to output  314 . Output  314  may be any suitable output device such as, for example, one or more medical devices (e.g., a medical monitor that displays various physiological parameters, a medical alarm, or any other suitable medical device that either displays physiological parameters or uses the output of processor  212  as an input), one or more display devices (e.g., monitor, PDA, mobile phone, any other suitable display device, or any combination thereof), one or more audio devices, one or more memory devices (e.g., hard disk drive, flash memory, RAM, optical disk, any other suitable memory device, or any combination thereof), one or more printing devices, any other suitable output device, or any combination thereof. 
     It will be understood that system  300  may be incorporated into system  10  ( FIGS. 1 and 2 ) in which, for example, input signal generator  310  may be implemented as parts of sensor  12  and monitor  14  and processor  312  may be implemented as part of monitor  14 . In some embodiments, portions of system  300  may be configured to be portable. For example, all or a part of system  300  may be embedded in a small, compact object carried with or attached to the patient (e.g., a watch (or other piece of jewelry) or cellular telephone). In such embodiments, a wireless transceiver (not shown) may also be included in system  300  to enable wireless communication with other components of system  10 . As such, system  10  may be part of a fully portable and continuous blood pressure monitoring solution. 
     As mentioned above, multi-parameter equation (1) may be used to determine estimated blood pressure measurements from the time difference, T, between two or more characteristic points of a PPG signal. In an embodiment, the PPG signals used in the CNIBP monitoring techniques described herein are generated by a pulse oximeter or similar device. Systems  10  ( FIGS. 1 and 2 ) and  300  ( FIG. 3 ) may also include a calibration device (e.g., an aneroid or mercury sphygmomanometer and occluding cuff) that generates blood pressure or other measurements to calibrate the CNIBP calculations. 
     The present disclosure may be applied to measuring systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP), or any combination of the foregoing on an on-going, continuous, or periodic basis. U.S. patent application Ser. No. 12/242,238 filed Sep. 30, 2008, which is hereby incorporated by reference herein in its entirety, discloses some techniques for continuous and non-invasive blood pressure monitoring that may be used in conjunction with the present disclosure. 
       FIG. 4  shows illustrative PPG signal  400 . As described above, in some embodiments PPG signal  400  may be generated by a pulse oximeter or similar device positioned at any suitable location of a subject&#39;s body. Additionally, PPG signal  400  may be generated at each of a plurality of locations of a subject&#39;s body, with at least one probe or sensor attached to each location. The time difference T that it takes for PPG signal  400  to appear at one location and another location (e.g., at a patient&#39;s ear and at the patient&#39;s finger or toe) may then be measured and used to derive a blood pressure measurement for the patient using a calibrated version of equation (1) or using any other relationship, such as lookup tables and the like. Time T may be measured, for example, by determining the difference between how long it takes for a given characteristic point, observed in the PPG signal at the first sensor or probe location, to appear in the PPG signal at the second sensor or probe location. 
     In an embodiment, PPG signal  400  may be generated using only a single sensor or probe attached to the subject&#39;s body. In such a scenario, the time difference, T, may correspond to the time it takes the pulse wave to travel a predetermined distance (e.g., a distance from the sensor or probe to a reflection point and back to the sensor or probe). Characteristic points in the PPG signal may include the time between various peaks in the PPG signal and/or in some derivative of the PPG signal. For example, in some embodiments, the time difference, T, may be calculated between (1) the maximum peak of the PPG signal in the time domain and the second peak in the 2nd derivative of the PPG signal (the first 2nd derivative peak may be close to the maximum peak in the time domain) and/or (2) peaks in the 2nd derivative of the PPG signal. Any other suitable time difference between any suitable characteristic points in the PPG signal (e.g., PPG signal  400 ) or any derivative of the PPG signal may be used as T in other embodiments. 
     In an embodiment, the time difference between the adjacent peaks in the PPG signal, the time difference between the adjacent valleys in the PPG signal, or the time difference between any combination of peaks and valleys, can be used as the time difference T. As such, adjacent peaks and/or adjacent valleys in the PPG signal (or in any derivative thereof) may also be considered characteristic points. In an embodiment, these time differences may be divided by the actual or estimated heart rate to normalize the time differences. In an embodiment, the resulting time difference values between two peaks may be used to determine the systolic blood pressure, and the resulting time difference values between two valleys may be used to determine the diastolic blood pressure. 
     Characteristic points in a PPG signal (e.g., PPG signal  400 ) may be identified in a number of ways. For example, in some embodiments, the turning points of 1st, 2nd, 3rd (or any other) derivative of the PPG signal are used as characteristic points. Additionally or alternatively, points of inflection in the PPG signal (or any suitable derivative thereof) may also be used as characteristic points of the PPG signal. 
     In an embodiment, blood pressure may be determined by, for example, measuring the area under a pulse or a portion of the pulse in the PPG signal (e.g., PPG signal  400 ). These measurements may be correlated with empirical blood pressure data (corresponding to previous blood pressure measurements of the patient or one or more other patients) to determine the blood pressure. In some implementations, the blood pressure may be determined by looking up the area measurement values in a table, which may be stored in a memory, to obtain corresponding blood pressures. Alternatively, the blood pressure may be determined by using any suitable blood pressure-area mapping equation which is generated based on blood pressure and area measurements associated with one or more patients. For example, measured samples may be plotted in a graph that maps blood pressure to area. The graph may be analyzed to generate a linear-best-fit-line approximation, non-linear best fit line approximation or other suitable approximation from which to derive an equation that may be used to determine blood pressure by providing an area measurement. 
       FIG. 5  shows an illustrative process  500  for determining blood pressure from PPG signals in accordance with an embodiment. At step  502 , PPG signals may be detected from a patient. For example, monitor  14  ( FIGS. 1 and 2 ) may be used to detect PPG signals from patient  40  ( FIG. 2 ) using, for example, sensors such as sensors  12  and  13  ( FIGS. 1 and 2 ). The sensors may be located at any suitable site on the patient, e.g., forehead, earlobe, toe, finger, or chest. In an embodiment, a first PPG signal may be detected from a sensor located relatively close to the patient&#39;s heart (e.g., the earlobe), while a second PPG signal may be detected from a sensor located relatively far from the patient&#39;s heart (e.g., the fingertip). The PPG signals may be detected by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. 
     At step  504 , the PPG signals detected at step  502  may be filtered using any suitable circuitry, such as filter  68  ( FIG. 2 ), microprocessor  48  ( FIG. 2 ), and/or processor  312  ( FIG. 3 ) of CNIBP monitoring or pulse oximetry system  10  ( FIG. 1 ) of a CNIBP monitoring or pulse oximetry system. Step  504  may include high-pass filtering, low-pass filtering, band-pass filtering, or any suitable combination thereof. For example, in an embodiment step  504  may include low-pass filtering the PPG signals detected at step  502 , to eliminate relatively high-frequency noise, then high-pass filtering the signal that results from the low-pass filtering. 
     At step  506 , a DPTT may be determined from the filtered PPG signals resulting from step  504 . The DPTT determination at step  506  may be performed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system, and may use any suitable algorithm, such as a correlation algorithm, a peak-picking algorithm, or any suitable combination thereof. In an embodiment, the DPTT determination in step  506  may be performed using process  600  ( FIG. 6 ). 
     At step  508 , a blood pressure measurement may be determined based, at least in part, on the DPTT determined at step  506 . For example, equation (1) above (or any other blood pressure equation using an elapsed time between the arrival of corresponding points of a pulse signal or any other suitable computed time difference) may be used to compute estimated blood pressure measurements. In an embodiment, the computed time difference between characteristic points in a single PPG signal may be substituted for the elapsed time between the arrival of corresponding points of a pulse signal. 
     After blood pressure measurements are determined, the measurements may be outputted, stored, or displayed in any suitable fashion at step  510 . For example, multi-parameter patient monitor  26  ( FIG. 1 ) may display a patient&#39;s blood pressure on display  28  ( FIG. 1 ). Additionally or alternatively, the measurements may be saved to memory or a storage device (e.g., ROM  52  or RAM  54  of monitor  14  ( FIG. 2 )) for later analysis or as a log of a patient&#39;s medical history. 
     In practice, one or more steps shown in process  500  may be combined with other steps, performed in any suitable order, performed in parallel (e.g., simultaneously or substantially simultaneously), or removed. 
       FIG. 6  shows an illustrative process  600  for determining a DPTT measurement from PPG signals in accordance with an embodiment. Process  600  may be used as part of step  506  of process  500  ( FIG. 5 ). At step  602 , the first derivative of each of the filtered PPG signals (e.g., generated at step  504  of  FIG. 5 ) may be computed. The derivatives may be computed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. The use of first derivative waveforms may advantageously facilitate computation of a DPTT using systolic measurements. In another embodiment, a second derivative of the filtered PPG signals may be taken instead (e.g., to facilitate processing of a DPTT using diastolic measurements) or the filtered PPG signals themselves may be used without any derivative operations. 
     At step  604 , a maximum correlation algorithm may be performed on the first derivatives computed at step  602 . The maximum correlation algorithm may be computed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. By way of example, consider a scenario where one first derivative waveform computed at step  602  corresponds to a PPG signal measured at a patient&#39;s earlobe, while another first derivative waveform computed at step  602  corresponds to a PPG signal measured at the patient&#39;s fingertip. Each beat of the patient&#39;s heart typically results in the arrival of a pulse at the patient&#39;s earlobe before the arrival of a pulse at the patient&#39;s fingertip. Thus, a DPTT may be computed by determining how much time elapses between the arrival of a pulse at the earlobe and the arrival of a corresponding pulse at the fingertip. The maximum correlation algorithm may shift the two first-derivative waveforms relative to each other in time, and identify what amount of time shift results in the highest correlation between the two first-derivative waveforms. The correlation between the two waveforms may be measured based on peaks or valleys in the first-derivative waveforms, based on other relevant portions of the waveforms, or based on the waveforms in their entirety. The amount of time shift resulting in the highest correlation may then be used as a DPTT measurement for the purpose of measuring a patient&#39;s blood pressure. 
     At step  606 , a degree of confidence in the results of the maximum correlation algorithm of step  604  may be determined. The confidence determination may be computed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. Confidence may be determined by examining the DPTT generated at step  604 , a measure of the degree of correlation resulting from the alignment chosen at step  604 , or any other suitable metric or any combination thereof. 
     With respect to the time-based confidence determination, the maximum correlation algorithm is generally expected to yield a DPTT within a certain range. In the example described in connection with step  604 , for instance, the difference between the time it takes for a pulse of blood to reach a patient&#39;s fingertip and the time for it to reach the patient&#39;s earlobe typically falls into a certain numerical range. Thus, a relatively low DPTT (e.g., falling below a certain threshold) may indicate a relatively low degree of confidence in the results of the maximum correlation algorithm. As an extreme example, a DPTT falling below zero (often referred to as a “zero crossing”) is typically an indication that the results of the maximum correlation algorithm are erroneous, as it is almost physiologically impossible for a given pulse of blood to arrive at the fingertip before it arrives at the ear. Likewise, a DPTT that is above a certain threshold may indicate a relatively high degree of confidence in the results of the maximum correlation algorithm performed in step  604 . It will be noted that this discussion assumes that the first PPG signal corresponds to a location closer to the heart than the location used to measure the second PPG signal. If the first PPG signal is measured at a more remote location, then the DPTT determined at step  604  would be expected to be below a certain negative threshold. 
     With respect to the correlation-based confidence determination, maximum correlation algorithms may choose the DPTT based on the amount of time shift that maximizes some metric representing the amount of correlation between the first and second waveforms. The resulting maximum value of the correlation metric may then be examined as an indication of the degree of confidence in the DPTT computed. A relatively high correlation metric measurement (e.g., above a given threshold) may be indicative of a relatively high degree of confidence, while a relatively low correlation measurement (e.g., below a given threshold) may be indicative of a relatively low degree of confidence. 
     Thus, step  606  may determine whether or not the system is relatively confident in the results of the maximum correlation algorithm performed in step  604  using a time-based metric, a correlation based metric, any other suitable metric, or any combination thereof (e.g., a weighted sum). If a relatively high degree of confidence is detected at step  606 , process  600  may proceed to step  608 , where the DPTT may be set to the maximum-correlation time shift amount computed by the maximum correlation algorithm at step  604 . On the other hand, if a relatively low degree of confidence is detected at step  606 , process  600  may proceed to step  610 , where the DPTT may be determined using an alternative algorithm. Illustrative alternative algorithms are depicted in  FIGS. 7 and 8 , and will be described in greater detail later herein in connection with those figures. Steps  608  and  610  may be performed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. 
     Advantageously, the approach depicted in  FIG. 6  allows the DPTT to be determined more accurately in cases when the default maximum correlation algorithm performed in steps  602  and  604  yields results that are relatively unreliable. In those cases, the DPTT may be determined using an alternative algorithm in step  610  that may be less susceptible to the errors that caused the relatively unreliable results in the original maximum correlation algorithm. In this way, the accuracy of the resulting blood pressure measurements may be improved. 
     In practice, one or more steps shown in process  600  may be combined with other steps, performed in any suitable order, performed in parallel (e.g., simultaneously or substantially simultaneously), or removed. 
       FIG. 7  shows a first illustrative alternative algorithm  700  for determining a DPTT measurement from PPG signals in accordance with an embodiment. Alternative algorithm  700  may be used as part of step  610  of process  600  ( FIG. 6 ). At step  702 , the second derivative of each filtered PPG signal may be computed. The second derivatives may be computed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. In alternative embodiments, the first derivative of each filtered PPG signal may be computed instead, or the filtered PPG signals themselves may be used in alternative algorithm  700  without any derivative operations. 
     At step  704 , a set of peaks may be identified in each of the second-derivative waveforms generated at step  702 . The peaks may be identified by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system, and may be identified in any suitable manner. For example, a derivative of each second-derivative waveform may be generated and times at which the value of the generated derivative equals zero may be recorded. As another example, peaks may be identified in the second-derivative waveform directly without any further derivative operations. In an embodiment, only peaks above a certain height, wider than a certain width, or both are identified. Such height and width requirements may advantageously filter out false peaks generated by noise or otherwise not corresponding to beats of the patient&#39;s heart. 
     At step  706 , the average time between corresponding peaks identified at step  704  may be determined. The average time may be determined by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system, and may be determined in any suitable manner. For instance, each pair of corresponding peaks may be iterated through in turn, and the time between the two peaks may be added to a running total. When all peaks have been iterated through, the total may be divided by the number of pairs examined, to yield an average time. That average time may then be used as an alternative DPTT measurement in computing a patient&#39;s blood pressure. 
     In practice, one or more steps shown in process  700  may be combined with other steps, performed in any suitable order, performed in parallel (e.g., simultaneously or substantially simultaneously), or removed. 
       FIG. 8  shows a second illustrative alternative algorithm  800  for determining a DPTT measurement from PPG signals in accordance with an embodiment. Alternative algorithm  800  may be used as part of step  610  of process  600  ( FIG. 6 ). At step  802 , the second derivative of each filtered PPG signal may be computed. The second derivatives may be computed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. In alternative embodiments, the filtered PPG signals themselves may be used in alternative algorithm  800  without any derivative operations. 
     At step  804 , a maximum correlation algorithm may be performed on the second-derivative waveforms generated at step  802 . The maximum correlation algorithm may be performed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system. The maximum correlation algorithm may be substantially similar to the algorithm performed at step  604  of process  600  ( FIG. 6 ), with the inputs to the algorithm now being the second derivatives of the PPG signals rather than the first derivatives. 
     At step  806 , the DPTT may be determined based on the results of the maximum correlation algorithm. The determination may be performed by microprocessor  48  ( FIG. 2 ) and/or processor  312  ( FIG. 3 ) of a CNIBP monitoring or pulse oximetry system, and may be substantially similar to that of step  608  of process  600  ( FIG. 6 ). In particular, the DPTT may be set to be the amount of time shift identified at step  804  as maximizing the correlation between the two second-derivative waveforms, according to any suitable correlation metric. 
     In practice, one or more steps shown in process  800  may be combined with other steps, performed in any suitable order, performed in parallel (e.g., simultaneously or substantially simultaneously), or removed. 
       FIG. 9  shows a set of illustrative waveforms depicting blood pressure determination using a DPTT measurement computed with a default maximum correlation algorithm in accordance with an embodiment. Both  FIG. 9  and  FIG. 10  depict waveforms for “diastolic” measurements, corresponding to expansion of the heart chambers, and “systolic” measurements, corresponding to contraction of the heart chambers. Diastolic and systolic blood pressure measurements may be determined in any suitable manner. For example, in  FIGS. 9 and 10 , it is assumed that two different sets of DPTT measurements are taken, one diastolic and one systolic. In this case, each set of DPTT measurements may be used to compute a corresponding set of blood pressure measurements (e.g., by using equation (1) with two sets of values for T). As another example, a single set of DPTT measurements may be used to compute separate diastolic and systolic blood pressure measurements. In this case, equation (1) (or another suitable equation relating DPTT to blood pressure) may be used with one set of a and b values to determine diastolic blood pressure and another set of a and b values to determine systolic blood pressure. It will be understood that the methods depicted in  FIGS. 5-8  may be used with diastolic measurements, systolic measurements, or any suitable combination thereof. 
     In practice, certain sets of measurements may be relatively well-adapted for computing diastolic DPTT and blood pressure values, while other sets of measurements may be relatively well-adapted for computing systolic DPTT and blood pressure values. For example, diastolic DPTT and blood pressure values may be computed relatively accurately using the first derivative of incoming PPG signals (as compared to, e.g., the raw PPG signals or their second derivatives). Similarly, systolic DPTT and blood pressure values may be computed relatively accurately using the second derivative of incoming PPG signals (as compared to, e.g., the raw PPG signals or their first derivatives). It will be understood, however, that any suitable set of PPG measurements may be used to compute diastolic and systolic DPTT and blood pressure values, and the disclosure is not limited in this respect. 
     Illustrative graph  902  includes an example waveform  902   a  representing a patient&#39;s diastolic DPTT as it varies with time, computed using the default maximum correlation algorithm based on the first derivative of incoming PPG signals. Example waveform  902   b  represents the patient&#39;s systolic DPTT as it varies with time, computed using the default maximum correlation algorithm based on the second derivative of the PPG signals. In this illustrative example, the DPTT measurements include zero crossings that adversely affect the accuracy of the blood pressure measurement. Thus, diastolic waveform  902   a  exhibits relatively substantial deviation from systolic waveform  902   b.    
     Illustrative graph  904  includes example waveforms  904   a  and  904   b , representing smoothed versions of the patient&#39;s diastolic DPTT waveform  902   a  and systolic waveform  902   b , respectively. Smoothed waveforms  904   a  and  904   b  may be generated with any suitable technique, such as low-pass filtering. Again, smoothed diastolic waveform  904   a  exhibits relatively substantial deviation from smoothed systolic waveform  904   b.    
     Illustrative graph  906  depicts blood pressure measurements that may be generated using the DPTT data of graph  904 . Waveform  906   a  may represent the systolic blood pressure estimate, while waveform  906   c  may represent the diastolic blood pressure estimate. Additionally included in graph  906  is waveform  906   b , which may represent the patient&#39;s a-line systolic blood pressure measurement of the patient, and waveform  906   d , which may represent the patient&#39;s a-line diastolic blood pressure measurement. Because a-line measurements are usually generated using a device that is directly inserted into a patient&#39;s blood vessel, they are considered relatively accurate and are thus often used to gauge the accuracy of non-invasive blood pressure measurements. Here, the non-invasive blood pressure measurements represented by systolic waveform  906   a  show noticeable deviations from a-line systolic waveform  906   b  due to the incorporation of zero crossings into the original DPTT measurements. On the other hand, in this example diastolic waveform  906   c  seems relatively well-aligned with a-line diastolic waveform  906   d , reflecting the fact that second-derivative PPG measurements may be less susceptible to noise than first-derivative PPG measurements. 
       FIG. 10  shows a set of illustrative waveforms depicting blood pressure determination using a DPTT measurement computed at least partially with an alternative algorithm in accordance with an embodiment. Illustrative graph  1002  includes an example waveform  1002   a  representing a patient&#39;s diastolic DPTT as it varies with time. Example waveform  1002   b  represents the patient&#39;s systolic DPTT as it varies with time. In this illustrative example, the DPTT measurements of waveforms  1002   a  and  1002   b  have been advantageously computed using an approach such as that depicted in  FIG. 6 , which detects relatively low-confidence correlation results and computes the DPTT using an alternative algorithm when such a detection occurs. Thus, diastolic waveform  1002   a  and systolic waveform  1002   b  match more closely than corresponding respective waveforms  902   a  and  902   b  ( FIG. 9 ). 
     Illustrative graph  1004  includes example waveforms  1004   a  and  1004   b , representing smoothed versions of the patient&#39;s diastolic DPTT waveform  1002   a  and systolic waveform  1002   b , respectively. Smoothed waveforms  1004   a  and  1004   b  may be generated with any suitable technique, such as low-pass filtering. Again, smoothed diastolic waveform  1004   a  and smoothed systolic waveform  1004   b  match more closely than corresponding respective waveforms  904   a  and  904   b  ( FIG. 9 ). 
     Illustrative graph  1006  depicts blood pressure measurements that may be generated using the DPTT data of graph  1004 . Waveform  1006   a  may represent the systolic blood pressure estimate, while waveform  1006   c  may represent the diastolic blood pressure estimate. Additionally included in graph  1006  is waveform  1006   b , which may represent the patient&#39;s a-line systolic blood pressure measurement of the patient, and waveform  1006   d , which may represent the patient&#39;s a-line diastolic blood pressure measurement. Because a-line measurements are usually generated using a device that is directly inserted into a patient&#39;s blood vessel, they are considered relatively accurate and are thus often used to gauge the accuracy of non-invasive blood pressure measurements. Here, the non-invasive blood pressure measurements represented by systolic waveform  1006   a  match more closely than corresponding respective waveform  906   a  ( FIG. 9 ). Diastolic waveform  1006   c  appears approximately as well-aligned as corresponding waveform  906   c  ( FIG. 9 ), again reflecting the fact that second-derivative PPG measurements may be less susceptible to noise than first-derivative PPG measurements. 
     The foregoing is merely illustrative of the principles of this disclosure and various modifications can be made by those skilled in the art without departing from the scope and spirit of the disclosure. The above described embodiments are presented for purposes of illustration and not of limitation. The present disclosure also can take many forms other than those explicitly described herein. For example, steps  602  and  604  ( FIG. 6 ) may be replaced with steps representing any suitable algorithm that can be used to compute a DPTT value. As another example, two or more algorithms may be performed in parallel to compute two or more DPTT measurements, a confidence measure may be determined for the results of each of those algorithms, and the highest-confidence DPTT measurement may be used to compute the blood pressure measurement. As another example, the two more DPTT measurements may be averaged together based on fixed weights or variable weights (e.g., selected based on the determined confidence measures) to obtain a final DPTT measurement for use in computing the blood pressure measurement. Other variations are possible. Accordingly, it is emphasized that the disclosure is not limited to the explicitly disclosed methods, systems, and apparatuses, but is intended to include variations to and modifications thereof which are within the spirit of the following claims.