Patent Description:
In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of devices have been developed for monitoring many such physiological characteristics. Such devices provide doctors and other healthcare personnel with the information they need to provide the best possible healthcare for their patients. As a result, such monitoring devices have become an indispensable part of modern medicine.

One technique for monitoring certain physiological characteristics of a patient uses attenuation of light to determine physiological characteristics of a patient. This is used in pulse oximetry, and the devices built based upon pulse oximetry techniques. Light attenuation is also used for regional or cerebral oximetry. Oximetry may be used to measure various blood characteristics, such as the oxygen saturation of hemoglobin in blood or tissue, the volume of individual blood pulsations supplying the tissue, and/or the rate of blood pulsations corresponding to each heartbeat of a patient. The signals can lead to further physiological measurements, such as respiration rate, glucose levels or blood pressure.

Pulse oximetry is often used to noninvasively measure arterial blood oxygenation. To measure blood oxygenation, two optical sources, typically light-emitting-diodes (LEDs), may be used to inject light into the tissue. A photo-diode is used to capture the light after propagating through blood perfused tissue. During a cardiac cycle, the amount of blood in the optical path changes which changes the amount the light that is absorbed. The resulting photocurrent form the photodiode is modulated. As blood oxygenation changes, the relative change in the modulated light at two distinct wavelengths changes. This relative change in the modulated photocurrent is processed (e.g., via signal conditioning, various algorithms) by the oximetry unit to estimate the arterial functional oxygenation (SpO<NUM>).

To make accurate and precise measurements of SpO<NUM>, it is helpful to characterize the components of the system that can lead to changes in the photocurrent produced by the photodiode. For example, if the distribution of optical flux from the LED changes, the photocurrent from the photodiode will change as well. LED-to-LED variability is typical during manufacture and is important to characterize. In addition, the efficiency of the photodetector to convert light to current is wavelength dependent, referred to as responsivity. Hence, changes in detector responsivity can lead to fluctuations in the photocurrent. <CIT> describes a body-worn system that continuously measures pulse oximetry and blood pressure, along with motion, posture, and activity level, from an ambulatory patient. <CIT> describes a method of obtaining a correction factor to compensate for wavelength shifts of a light source, the method comprising (i) detecting by a light detector, a first optical characteristic of substantially unfiltered light emitted by the light source; (ii) detecting by the light detector, a second optical characteristic of filtered light from the light source; and (ii) comparing the first optical characteristic, the second characteristic and a predetermined set of values to obtain the correction factor. <CIT> describes various methods and systems for obtaining calibration coefficients for pulse oximeter sensors. <CIT> describes an adapter which is capable of converting signals from an oximeter sensor such that the signals are readable by an oximeter monitor.

Calibration methods that utilize a single spectral characteristic, such as centroid wavelength parameter or the peak wavelength from LED emission fail to capture the full variability of LED-to-LED optical flux. However, the art would benefit from improved techniques for sensor calibration.

What is needed in the art is improved techniques to digitally calibrate pulse oximeter sensors.

The techniques of this disclosure generally relate to digital calibration of medical devices that monitor physiological parameters of a patient, such as pulse oximeters. Aspects of the invention are in accordance with the appended independent claims. Various optional features are the subject of the dependent claims.

According to a first aspect the present invention there is provided method for calibrating a medical device, comprising:.

According to a second aspect of the present invention there is provided a system for calibrating a medical device, comprising:.

The present document describes systems and methods for calibrating a medical device utilizing LED emission by characterizing the spectrum distribution of the LED emission and mapping such characterized spectrum distribution to calibration coefficients, e.g., gamma coefficients.

In exemplary embodiments, the LED emission provides an asymmetric distribution; and mapping of the characterized spectrum distribution to one or more calibration coefficients reflects such asymmetric distribution.

In further exemplary embodiments, the full spectrum distribution of an LED is characterized, with mapping of the characterized full spectrum distribution to one or more calibration coefficients reflects such full spectrum distribution.

Exemplary embodiments of the present disclosure improve mapping from an LED emission spectrum to gamma coefficients by performing one or more of the following techniques: de-convolving LED distribution into N Gaussian distributions, using the Gaussian distribution to map LEDs to gammas; fitting an LED distribution to a logistic power peak and using the fitted parameters to map LEDs to gammas; fitting an LED distribution to an asymmetric double sigmoidal and mapping LEDs to gammas; characterizing distribution skewness; measuring the peak wavelength and/or FWHM in addition to centroid wavelength and mapping to gammas; and integrating LED distribution with a blood absorption curve and mapping to gammas.

The present disclosure describes systems and methods for improved oximeter sensor calibration by assessing the characteristics of LED emission. As will be described in more detail below, the present disclosure provides systems and methods for calibrating a medical device utilizing one or more LEDs by characterizing the spectrum distribution of the LED emission and mapping such characterized spectrum distribution to calibration coefficients, which are according to the present invention gamma coefficients, which may be used in exemplary embodiments to convert ratio-of-ratio values to SpO<NUM> values.

<FIG> illustrates an embodiment of a patient monitoring system <NUM> that includes a patient monitor <NUM> and a sensor <NUM>, such as a pulse oximetry sensor, to monitor physiological parameters of a patient. By way of example, the sensor <NUM> may be a NELLCOR™, or INVOS™ sensor available from Medtronic (Boulder, CO), or another type of oximetry sensor. Although the depicted embodiments relate to sensors for use on a patient's fingertip, toe, or earlobe, it should be understood that, in certain embodiments, the features of the sensor <NUM> as provided herein may be incorporated into sensors for use on other tissue locations, such as the forehead and/or temple, the heel, stomach, chest, back, or any other appropriate measurement site.

In the embodiment of <FIG>, the sensor <NUM> is a pulse oximetry sensor that includes one or more emitters <NUM> and one or more detectors <NUM>. For pulse oximetry applications, the emitter <NUM> transmits at least two wavelengths of light (e.g., red and/or infrared (IR)) into a tissue of the patient. For other applications, the emitter <NUM> may transmit <NUM>, <NUM>, or <NUM> or more wavelengths of light into the tissue of a patient. The detector <NUM> is a photodetector selected to receive light in the range of wavelengths emitted from the emitter <NUM>, after the light has passed through the tissue. Additionally, the emitter <NUM> and the detector <NUM> may operate in various modes (e.g., reflectance or transmission). In certain embodiments, the sensor <NUM> includes sensing components in addition to, or instead of, the emitter <NUM> and the detector <NUM>.

The sensor <NUM> also includes a sensor body <NUM> to house or carry the components of the sensor <NUM>. In exemplary embodiments, the body <NUM> includes a backing, or liner, provided around the emitter <NUM> and the detector <NUM>, as well as an adhesive layer (not shown) on the patient side. The sensor <NUM> may be reusable (such as a durable plastic clip sensor), disposable (such as an adhesive sensor including a bandage/liner at least partially made from hydrophobic materials), or partially reusable and partially disposable.

In the embodiment shown, the sensor <NUM> is communicatively coupled to the patient monitor <NUM>. In certain embodiments, the sensor <NUM> may include a wireless module configured to establish a wireless communication <NUM> with the patient monitor <NUM> using any suitable wireless standard. For example, the sensor <NUM> may include a transceiver that enables wireless signals to be transmitted to and received from an external device (e.g., the patient monitor <NUM>, a charging device, etc.). The transceiver may establish wireless communication <NUM> with a transceiver of the patient monitor <NUM> using any suitable protocol. For example, the transceiver may be configured to transmit signals using one or more of the ZigBee standard, <NUM>. 4x standards WirelessHART standard, Bluetooth standard, IEEE <NUM>. 11x standards, or MiWi standard. Additionally, the transceiver may transmit a raw digitized detector signal, a processed digitized detector signal, and/or a calculated physiological parameter, as well as any data that may be stored in the sensor, such as calibration data or coefficients, such as the gamma coefficients of the present invention, data relating to wavelengths of the emitters <NUM>, or data relating to input specification for the emitters <NUM>, as discussed below. Additionally, or alternatively, the emitters <NUM> and detectors <NUM> of the sensor <NUM> may be coupled to the patient monitor <NUM> via a cable <NUM> through a plug <NUM> (e.g., a connector having one or more conductors) coupled to a sensor port <NUM> of the monitor. In certain embodiments, the sensor <NUM> is configured to operate in both a wireless mode and a wired mode. Accordingly, in certain embodiments, the cable <NUM> is removably attached to the sensor <NUM> such that the sensor <NUM> can be detached from the cable to increase the patient's range of motion while wearing the sensor <NUM>.

The patient monitor <NUM> is configured to calculate physiological parameters of the patient relating to the physiological signal received from the sensor <NUM>. For example, the patient monitor <NUM> may include a processor configured to calculate the patient's arterial blood oxygen saturation, tissue oxygen saturation, pulse rate, respiration rate, blood pressure, blood pressure characteristic measure, autoregulation status, brain activity, and/or any other suitable physiological characteristics. Additionally, the patient monitor <NUM> may include a monitor display <NUM> configured to display information regarding the physiological parameters, information about the system (e.g., instructions for disinfecting and/or charging the sensor <NUM>), and/or alarm indications. The patient monitor <NUM> may include various input components <NUM>, such as knobs, switches, keys and keypads, buttons, etc., to provide for operation and configuration of the patient monitor <NUM>. The patient monitor <NUM> may also display information related to alarms, monitor settings, and/or signal quality via one or more indicator lights and/or one or more speakers or audible indicators. The patient monitor <NUM> may also include an upgrade slot <NUM>, in which additional modules can be inserted so that the patient monitor <NUM> can measure and display additional physiological parameters.

Because the sensor <NUM> may be configured to operate in a wireless mode and, in certain embodiments, may not receive power from the patient monitor <NUM> while operating in the wireless mode, the sensor <NUM> may include a battery to provide power to the components of the sensor <NUM> (e.g., the emitter <NUM> and the detector <NUM>). In certain embodiments, the battery may be a rechargeable battery such as, for example, a lithium ion, lithium polymer, nickel-metal hydride, or nickel-cadmium battery. However, any suitable power source may be utilized, such as, one or more capacitors and/or an energy harvesting power supply (e.g., a motion generated energy harvesting device, thermoelectric generated energy harvesting device, or similar devices).

As noted above, in an embodiment, the patient monitor <NUM> is a pulse oximetry monitor and the sensor <NUM> is a pulse oximetry sensor. The sensor <NUM> may be placed at a site on a patient with pulsatile arterial flow, typically a fingertip, toe, forehead or earlobe, or in the case of a neonate, across a foot. Additional suitable sensor locations include, without limitation, the neck to monitor carotid artery pulsatile flow, the wrist to monitor radial artery pulsatile flow, the inside of a patient's thigh to monitor femoral artery pulsatile flow, the ankle to monitor tibial artery pulsatile flow, and around or in front of the ear. The patient monitoring system <NUM> may include sensors <NUM> at multiple locations. The emitter <NUM> emits light which passes through the blood perfused tissue, and the detector <NUM> photoelectrically senses the amount of light reflected or transmitted by the tissue. The patient monitoring system <NUM> measures the intensity of light that is received at the detector <NUM> as a function of time.

A signal representing light intensity versus time or a mathematical manipulation of this signal (e.g., a scaled version thereof, a log taken thereof, a scaled version of a log taken thereof, etc.) may be referred to as the photoplethysmograph (PPG) signal. In addition, the term "PPG signal," as used herein, may also refer to an absorption signal (i.e., representing the amount of light absorbed by the tissue) or any suitable mathematical manipulation thereof. The amount of light detected or absorbed may then be used to calculate any of a number of physiological parameters, including oxygen saturation (the saturation of oxygen in pulsatile blood, SpO2), an amount of a blood constituent (e.g., oxyhemoglobin), as well as a physiological rate (e.g., pulse rate or respiration rate) and when each individual pulse or breath occurs. For SpO2, red and infrared (IR) wavelengths may be used because it has been observed that highly oxygenated blood will absorb relatively less Red light and more IR light than blood with a lower oxygen saturation. By comparing the intensities of two wavelengths at different points in the pulse cycle, it is possible to estimate the blood oxygen saturation of hemoglobin in arterial blood, such as from empirical data that may be indexed by values of a ratio, a lookup table, and/or from curve fitting and/or other interpolative techniques.

Referring now to <FIG>, an embodiment of a patient monitoring sensor <NUM> in accordance with an embodiment is shown. As may be seen, the shape or profile of various components may vary. The sensor <NUM> includes a body <NUM> that includes a flexible circuit. The sensor <NUM> includes one or more LEDs <NUM> (in this case a surface mount LED package with two LEDs) and one or more detectors <NUM> disposed on the body <NUM> of the sensor <NUM>.

While any number of exemplary sensor designs are contemplated herein, in the illustrated exemplary embodiment, the body <NUM> includes a flap portion <NUM> that includes an aperture <NUM>. The flap portion <NUM> is configured to be folded at a hinge portion <NUM> such that the aperture <NUM> overlaps the detector <NUM> to allow light to pass through. In one embodiment, the flap portion <NUM> includes an adhesive <NUM> that is used to secure the flap portion <NUM> to the body <NUM> after the flap portion <NUM> is folded at the hinge portion <NUM> using visual indicator <NUM> that is used to assure proper alignment of the flap portion <NUM> when folded at the hinge portion <NUM>.

The sensor <NUM> includes a plug <NUM> that is configured to be connected to a patient monitoring system, such as the one shown in <FIG>. The sensor <NUM> also includes a cable <NUM> that connects the plug <NUM> to the body <NUM> of the sensor <NUM>. The cable <NUM> includes a plurality of wires <NUM> that connect various parts of the plug <NUM> to terminals <NUM> disposed on the body <NUM>. The flexible circuit is disposed in the body <NUM> and connects the terminals <NUM> to the LED <NUM> and the detector <NUM>. In addition, one of the terminals <NUM> connect a ground wire to the flexible circuit.

Referring now to <FIG>, a patient monitoring sensor <NUM> in accordance with an embodiment is shown. In exemplary embodiments, a faraday cage <NUM> is formed around the detector <NUM> by folding the flap portion <NUM> over a portion of the body <NUM> of the sensor <NUM>.

<FIG> illustrates generally at <NUM> an exemplary graph of normalized spectral flux from an LED (for example a red LED) as normalized spectrometer output <NUM> (in absorbance units (a. )) vs wavelength (in nanometers (nm)). As may be seen from the Figure, the emitted optical flux from an LED is not Gaussian, but instead includes a skewness. The centroid wavelength, shown at <NUM>, is the wavelength representing the mean spectral power distribution, which may be determined using the following equation:
<MAT>
where λc is the centroid wavelength and Φ(λ) is the spectral power distribution of the LED.

In theory, in an LED with a symmetric spectral power distribution, the centroid, mean, and peak wavelengths would all be the same. However, as can be seen from <FIG>, an LED may have some asymmetry. In the illustrated exemplary embodiment of <FIG>, the centroid wavelength <NUM> is at wavelength A (red LEDs generally emit between <NUM> and <NUM>), and the peak wavelength <NUM> is at wavelength B, where the peak wavelength is greater than the centroid wavelength (for example <NUM>-<NUM> greater, though that difference may be more or less). Because the illustrated peak wavelength is greater than the centroid wavelength, <FIG> indicates that more of the optical output of the LED is at the shorter wavelengths than the longer wavelengths.

It can be seen that use of centroid wavelength does not capture the full distribution of the LED and is not the only important parameter. In this disclosure, the benefit of accounting for the full spectral distribution will be shown and methods to characterize the full spectral distribution so that they can be mapped to calibration coefficients will be presented.

A theoretical model may be used to explore the asymmetric distribution of light form an LED. To model the ratio of ratio, the following equation may be used.

Here, R is the ratio of ratios and the measurement of light exiting the tissue (Iout) of and is made at two different time points, t<NUM> and t<NUM> and at two wavelengths λ<NUM> and λ<NUM>. The light exiting tissue, Iout, is modeled in following equation:
<MAT>.

Here, I is the optical intensity of the LED (or the light entering the tissue), µblood is the optical absorption of the blood, ctHb(t) is the concentration of hemoglobin, and <NUM> is the path length at the wavelength. The optical absorption coefficient of blood is shown generally at <NUM> in <FIG>. The optical absorption coefficient of oxy and deoxy-hemoglobin are illustrated by lines <NUM> and <NUM>, respectively. The absorption coefficient at two different saturation levels are illustrated, with dashed line <NUM> at <NUM>% and dotted line <NUM> at <NUM>%.

To understand the effect of skew and FWHM, an asymmetric double sigmoidal function may be used to model the LED spectral emission, the function is shown in the following equation:
<MAT>.

The amplitude may be adjusted such that the total spectral flux is 1mW. The values for C, W, S1, and S2 may be determined by fitting to a measured spectral emission. The effect of changing S1 is shown generally at <NUM> in <FIG> and <NUM> in <FIG>.

S1 mostly impacts shorter wavelengths, but also effects changing the peak, centroid, and FWHM. To understand how skew and wavelengths relate, S1 and C may be altered such that the centroid wavelength is always the same, but skewness changes (which can be seen as the difference between peak and centroid), as per <FIG>, which shows the effect on SpO2 due to changing skewness, but not centroid wavelength. <FIG> shows the spectral emission generally at <NUM>. <FIG> show generally at <NUM> and <NUM> that the centroid does not change and is constant at <NUM>, but the peak wavelength does change. <FIG> shows generally at <NUM> the difference in SpO2 due to the skewness.

Here, a <NUM> shift in peak verse centroid produced approximately <NUM> RMSD. Note that centroid corrections are about twice important where a <NUM> shift in centroid produces approximately <NUM> RMSD. Hence, it is important to characterize the full distribution of the spectral emission and not just the centroid.

Accordingly, exemplary embodiments of the present disclosure improve mapping from an LED emission spectrum to gamma coefficients. In exemplary embodiments, the present system and method may perform one or more of the following: de-convolve LED distribution into N Gaussian distributions, using the Gaussian distribution to map LEDs to gammas; fit an LED distribution to a logistic power peak and use the fitted parameters to map LEDs to gammas; fit an LED distribution to an asymmetric double sigmoidal and map LEDs to gammas; characterize distribution skewness; measure the peak wavelength and/or FWHM in addition to centroid wavelength and map to gammas; and integrate LED distribution with a blood absorption curve and map to gammas.

Exemplary aspects of the present disclosure improve accuracy to blood (SaO<NUM>) for SpO<NUM>, improving digital calibration by accounting for the full spectral emission. The methods disclosed outline how to characterize the full spectral emission. Once characterized, the output parameters may be mapped to calibration coefficients determined, e.g., during a hypoxia study.

With further regard to de-convolving an LED into <NUM> Gaussian distributions, an LED may be considered as three symmetric (Gaussian or normal distribution) LEDs instead of one. These three LEDs may be mapped to the calibration coefficients instead of just the centroid of the one emission. Mapping the LED can have all three parameters used to describe a Gaussian distribution: mean, FWHM (full width at half maximum), and amplitude; or it can have various combinations (e.g., mean and FWHM). This technique may be applied with regard to a red LED and/or an IR LED.

With further regard to fitting an LED, as will be described in more detail below, different functions may be utilized to accurately fit the LED distribution, for example, the logistic power peak and the asymmetric double sigmoidal. The fitted parameters may then be used to map to the calibration coefficients.

With further regard to characterizing the skewness of the distribution (in addition to the centroid), the technique may include measuring the peak wavelength and looking at the difference in peak verse centroid. Other exemplary methods may also be used, such as calculating kurtosis, and/or skewness (such as Pearson's moment coefficient of skewness or Pearson's first or second skewness coefficient).

With further regard to integrating LED distribution with blood absorption curve, the LED spectral distribution can be numerically integrated with the absorption curve. The resulting summation can be used to map to the calibration coefficients. In another exemplary method, the LED spectral distribution may be input into a mathematical model that could be, e.g., a Beer's Lambert expression for RatRat to SpO2 or e.g., may be based on a MonteCarlo homogenous tissue model. The output parameters may then be mapped to the calibration coefficients.

These and other techniques will be for fully described below:.

As we have noted above, theoretically the LED spectral irradiance distribution should follow a Maxwell-Boltzman distribution, shown below:
<MAT>.

Here, h is Planck's constant, c is the speed of light, λ is the wavelength of light, k is the Boltzmann constant, T is temperature and Eg is the bandgap energy.

<FIG> shows at <NUM> a best fit <NUM> of the Maxwell-Boltzmann distribution to a measured red LED <NUM> at a predetermined temperature and bandgap energy. The equation fits to the shorter wavelengths with reasonable accuracy but fails to capture the distribution at the longer wavelengths. Instead of utilizing the theoretical distribution, various non-symmetrical distribution functions may also be used and compared to the empirical data.

For example, logistic power peak may be utilized according to the following equation:
<MAT>.

Here, A is used to fit the amplitude, S models the skewness, W is the full-width at half the maximum (FWHM), and C models the peak wavelength. To fit the measured data, Matlab's nonlinear fit function may be used, with the fit is shown generally at <NUM> in <FIG> and the error to the fit shown generally at <NUM> in <FIG>.

The fitted function parameters are shown in Table <NUM>, below; and Table <NUM> shows the difference between the fitted distribution and measured distribution for peak wavelength, FWHM, centroid and center wavelength.

As can be seen from <FIG>, the logistic power peak function fits to the LED distribution well, capturing the asymmetry. <FIG> is an exemplary graph showing the error of the fit in <FIG>. The difference (error) between the fitted distribution compared to the measured was <NUM> for the centroid and -<NUM> the for peak.

In another example, asymmetric double sigmoidal may be utilized, with the functional form of the logistic power peak shown in the following equation: <MAT>.

Here, A is used to fit the amplitude, S<NUM> models the skewness for wavelengths shorter than peak, S<NUM> models the skewness for wavelengths longer than peak, W models the full-width at half the maximum, and C models the peak wavelength. To fit the measured data, Matlab's nonlinear fit function may be used, with the fit shown generally at <NUM> in <FIG> and the error to the fit shown generally at <NUM> in <FIG>. The fitted function parameters are shown in Table <NUM>; with Table <NUM> showing the difference between the fitted distribution and measured distribution for peak wavelength, FWHM, centroid and center wavelength.

As may be seen from <FIG>, the asymmetric double sigmoidal function fits to the LED distribution well, capturing the asymmetry. The difference (error) between the fitted distribution compared to the measured was <NUM> for the centroid and -<NUM> the for peak.

In other exemplary embodiments, the distribution may be fit by summing Gaussian distributions, with the functional form of the sum of Gaussians shown from the following equation:
<MAT>.

Here, Ai is used to fit the amplitude of the ith Gaussian, Wi models the full-width at half the maximum of the ith Gaussian, and Ci models the peak wavelength of the ith Gaussian. To fit the measured data, Matlab's nonlinear fit function may be used, with the fit shown generally at <NUM> in <FIG> and the error to the fit shown generally at <NUM> in <FIG>. The fitted function parameters are shown in Table <NUM>; with Table <NUM> showing the difference between the fitted distribution and measured distribution for peak wavelength, FWHM, centroid and center wavelength.

As may be seen from <FIG>, the sum of Gaussians method fits to the LED distribution well, capturing the asymmetry. The difference (error) between the fitted distribution compared to the measured was -<NUM> for the centroid and -<NUM> the for peak.

The above provides exemplary distribution functions, including: logistic peak power; asymmetric double sigmoidal; and sum of Gaussians, each of which advantageously fit the spectral distribution well, accounting for longer wavelengths and accurately capturing the measured distribution. Fitting the measured spectral distribution to three Gaussian functions was found to accurately reproduce the peak, center and centroid wavelengths with a small error (<NUM>) in FWHM. The asymmetric double sigmoidal function also has the benefit of modeling the skewness for shorter and longer wavelengths, relative to the peak, with independent parameters (S<NUM> and S<NUM>).

In some embodiments, the physiological monitoring system may comprise a sensor configured to store algorithm configuration data, which may be algorithm coefficient(s) and generate a photoplethysmography (PPG) signal. A port (e.g., a bi-directional input/output) may be communicatively coupled to the sensor and may be configured to receive an algorithm configuration data and the PPG signal from the sensor. At least one processor may be configured to configure or modify at least a part of the first algorithm based upon the algorithm configuration data received by a monitor from the sensor and to execute the first algorithm as configured or modified to determine at least one physiological parameter of a subject based on the PPG signal. The at least one processor may further be configured to delete the algorithm configuration data from the monitor, or deactivate the configuration or modifications of the first algorithm after the sensor becomes communicatively disconnected from the port. By providing algorithm configuration data on the sensor, new algorithm configurations may be provided to the monitor without the need for afield update of all installed monitors. Rather, the sensor may carry the most updated configuration data to the monitor for execution during patient monitoring, providing a higher quality calculation of patient parameters than otherwise would be possible without the algorithm configuration data. Additionally, different types of sensors may have different capabilities, which can be reflected in the algorithm configuration data they carry.

In some embodiments, a physiological sensor may be provided. The physiological sensor may comprise at least one light source configured to generate a light signal, at least one light detector configured to receive the light signal after the light signal has been attenuated by body tissue of a subject, and non-transitory memory (e.g., integrated memory) configured to store algorithm configuration data. The physiological sensor may further comprise a port. The port may comprise a bi-directional input/output port, a wireless interface, NFC (near field communication) interface, RFID link, one-wire interface, 12C, SPI, UART, or any other type of a port or communication interface. In some embodiments, the port may also have other capabilities. For example, the port may comprise an output of a photo-detector capable of transmitting PPG data. The port may be configured to transmit the light signal to a physiological monitor that is communicatively coupled to the port and to transmit algorithm configuration data to the physiological monitor, which is configured to execute an executable code segment stored on the physiological monitor, as configured or modified by the algorithm configuration data received from the sensor to determine at least one physiological parameter of the subject based on the light signal. The algorithm configuration data may be deleted, or algorithm configurations or modifications may be deactivated after the physiological monitor becomes communicatively disconnected from the sensor. In some embodiments, the physiological monitor may become communicatively disconnected from the sensor, for example, when the sensor is physically disconnected form the physiological monitor, or when the sensor is moved out of the wireless range of the physiological monitor.

In some embodiments a physiological monitoring system may be provided. The physiological monitoring system may comprise a sensor that is configured to store algorithm configuration data and generate a photoplethysmographic (PPG) signal. The physiological monitoring system may further comprise a physiological monitor. The physiological monitor may comprise a port that is communicatively coupled to the sensor and is configured to receive the algorithm configuration data and the PPG signal from the sensor. The physiological monitor may comprise non-transitory memory configured to store a sequence of ordered algorithm stages wherein one of the algorithm stages comprises a configurable algorithm stage, the configurable algorithm stage comprising a plurality of alternative executable code segments.

Claim 1:
A method for calibrating a medical device, comprising:
providing a medical device including a sensor (<NUM>) having at least one LED (<NUM>) configured for LED emission through tissue;
characterizing the spectrum distribution of the emission of the at least one LED (<NUM>);
mapping such characterized spectrum distribution to at least one calibration coefficient; and wherein said at least one calibration coefficient comprises a gamma coefficient.