Determining blood pressure

Abstract

According to one aspect, a computer-implemented method for determining a blood pressure of a subject is provided. The method includes receiving (101) an envelope dataset for the subject. The envelope dataset includes measurements of blood flow in a body part of the subject for various degrees of restriction applied to the body part. The method then includes using (103) a set of probability density functions, PDFs, to determine, for various pairs of systolic and diastolic blood pressure values, the respective likelihoods of obtaining measurements in the envelope dataset. The method finally includes determining (105) that the systolic and diastolic blood pressure of the subject is the pair of systolic and diastolic blood pressure values ​​that results in the highest likelihood for the envelope dataset.

Description

[Technical Field]

[0001] This disclosure relates to determining blood pressure in a subject, and in particular to computer-implemented methods, computer program products, and apparatus for determining blood pressure from measurements of blood flow. [Background technology]

[0002] A common method for measuring blood pressure noninvasively involves temporarily restricting blood flow in an artery in a limb (e.g., an arm or leg) by applying external pressure, then obtaining measurements of blood flow as the amount of pressure applied to the limb changes. Even at peak systolic blood pressure, if the external pressure is great enough to completely occlude the artery, there will be no blood flow in the limb distal to the restriction. During a portion of the cardiac cycle, if the external pressure is great enough to occlude the artery, blood flow through the limb will be highly pulsatile and turbulent rather than laminar. If the external pressure is too low to occlude the artery at any point, blood flow will be laminar and moderately pulsatile. Blood flow restriction is typically achieved by placing an inflatable cuff around one of the patient's limbs and then inflating and deflating the cuff to achieve the desired degree of blood flow restriction.

[0003] There are at least three methods for determining blood flow with varying degrees of restriction. The most common method is the oscillometric method, which works by detecting and measuring small fluctuations in pressure within the cuff caused by changes in arterial volume due to blood flow. This method is frequently used because it is inexpensive and does not require any sensors or electrical connections within the cuff. The pressure sensor can be located within the blood pressure monitor, along with the pump used to inflate the cuff. A drawback of this method is that it is susceptible to artifacts caused by patient movement, since even small disturbances to the cuff will cause pressure changes within it that are the same magnitude as the vibrations caused by blood flow.

[0004] Another commonly used method is photoplethysmography (PPG), which uses a light source and photoreceptors to measure minute changes in light absorbance caused by blood flow in the distal part of the limb. This method is more tolerant in the presence of motion artifacts but requires the additional connection of a light source and light sensor.

[0005] A third commonly used method uses a microphone to detect sounds produced by turbulent flow within the blood vessels, known as Korotkoff sounds. These sounds are a very good indicator of when the degree of restriction is just enough to occlude that particular artery, but they are also susceptible to ambient noise. This method is widely used when performing manual, noninvasive blood pressure measurements using a stethoscope, cuff, and pressure sensor / gauge to pick up the Korotkoff sounds.

[0006] Other methods of determining blood flow include using Doppler ultrasound to measure the flow velocity within the arteries.

[0007] To determine a patient's blood pressure, it is necessary to measure the degree of pulsatility of blood flow at each of various degrees of restriction (i.e., at various cuff pressure values ​​when using an inflatable cuff). This measurement provides a measurement set including value pairs of the degree of restriction and the corresponding blood flow measurement. In the case of oscillometric measurements, the measurement set would be a pair of static cuff pressure and cuff pressure oscillation amplitude (static cuff pressure, cuff pressure oscillation amplitude). A measurement set of value pairs is referred to herein as an "envelope dataset" or "measured envelope." In the case of an envelope dataset including oscillometric measurements, the envelope dataset may be referred to as an oscillation table.

[0008] Conventional techniques allow systolic and diastolic blood pressure values ​​to be estimated from the envelope data set, provided the envelope range is sufficient (i.e., blood flow measurements are obtained for a range of degrees of restriction).

[0009] A currently used method for calculating blood pressure values ​​from measured envelope data is to use interpolation to find reference points in the envelope and derive blood pressure from the locations of the reference points, as described, for example, in U.S. Pat. No. 4,984,577, "Oscillometric non-invasive method for measuring blood pressure and apparatus for automated oscillometric blood pressure measuring." Another method fits a model function to a set of envelope points and derives blood pressure values ​​from parameters obtained by the curve fitting operation, as described, for example, in U.S. Pat. No. 5,704,362, "Method for oscillometric blood pressure determination employing curve fitting." Summary of the Invention [Problem to be solved by the invention]

[0010] One drawback of conventional methods is that they tend to rely heavily on some points of the measured envelope, particularly the maximum value of blood flow, and ignore information contained in other points. Specifically, blood pressure calculations typically rely heavily on the maximum amplitude and location of the measured envelope. As a result, a single erroneous measurement (particularly an erroneous maximum amplitude) can sometimes result in a large change in the calculated blood pressure value. This means that the methods tend to be less robust when erroneous envelope points occur due to motion artifacts or similar factors.

[0011] Another drawback is that some current methods lack a way to incorporate prior information into blood pressure calculations, which may include knowledge that certain blood pressure ranges are more likely than others (e.g., diastolic blood pressure is more likely to be in the 70...90 mmHg range than outside this range), that certain pulse pressure values ​​are more likely than others, or that a patient's blood pressure is more likely to be close to a previous measurement rather than significantly different from that measurement.

[0012] Therefore, there is a need for improved techniques for determining systolic and diastolic blood pressure from an envelope data set that are less dependent on a particular or single measurement point in the envelope data set. [Means for solving the problem]

[0013] Aspects and embodiments of the disclosed techniques are set forth below.

[0014] It should be noted that some embodiments also provide a way to include one or more types of prior information in the calculation of blood pressure, which can be taken into account by using maximum a posteriori estimation techniques from Bayesian statistics.

[0015] According to a first particular aspect, a computer-implemented method for determining a subject's blood pressure is provided. The method includes receiving an envelope dataset for the subject, the envelope dataset including measurements of blood flow in the subject's body part for each of various degrees of restriction applied to the body part; using a set of probability density functions (PDFs) to determine respective likelihoods for obtaining measurements in the envelope dataset for various pairs of systolic and diastolic blood pressure values; and determining the subject's systolic and diastolic blood pressures as the pair of systolic and diastolic blood pressure values ​​that yields the highest likelihood for the envelope dataset. The first aspect thereby provides a technique for determining systolic and diastolic blood pressure measurements that is less dependent on specific or single measurement points in the envelope dataset. As a result, the technique is less susceptible to errors resulting from outlying measurements and other errors in the envelope dataset.

[0016] In some embodiments, using the set of PDFs comprises determining, for each measurement in the envelope dataset, a respective probability of obtaining the measurement in the envelope dataset for various pairs of systolic and diastolic blood pressure values, and determining a likelihood of obtaining the measurement in the envelope dataset by combining the respective determined probabilities of obtaining the measurement in the envelope dataset for the pair of systolic and diastolic blood pressure values ​​by combining the respective determined probabilities for the pair of systolic and diastolic blood pressure values. This embodiment provides that the systolic and diastolic blood pressures are determined according to the joint probability of each measurement in the resulting envelope dataset.

[0017] In such embodiments, combining the respective probabilities includes multiplying the respective probabilities together or summing the logarithms of the respective probabilities. Embodiments that include summing the logarithms have advantages when identifying the systolic and diastolic blood pressure values ​​as having the highest sum.

[0018] In some embodiments, using the set of PDFs comprises determining the respective likelihoods of obtaining measurements in the envelope data set for all possible pairs of systolic and diastolic blood pressure values. This embodiment has the advantage that no optimization algorithm is required to determine the systolic and diastolic blood pressure values, and that by exhaustively searching all possible pairs of systolic and diastolic blood pressure values, it may be possible to quantify the quality of the determined systolic and diastolic blood pressure values, for example by determining the prominence of the most likely one.

[0019] In an alternative embodiment, using the set of PDFs and determining the systolic and diastolic blood pressures comprises using an iterative optimization algorithm to determine the highest likelihood of the envelope data set. Such an embodiment has the advantage that the systolic and diastolic blood pressure values ​​can be determined more quickly and / or efficiently than using an exhaustive search through all possible combinations of systolic and diastolic blood pressures.

[0020] In some embodiments, using the set of PDFs includes determining a blood flow scaling factor based on one or more highest measured values ​​of blood flow in the envelope dataset, and using the blood flow scaling factor to align the blood flow values ​​in the set of PDFs with the measured values ​​of blood flow in the envelope dataset. In such embodiments, the set of PDFs can be normalized to the envelope dataset, or the envelope dataset can be normalized to the set of PDFs. In such embodiments, determining the blood flow scaling factor includes determining the blood flow scaling factor based on a function that median-represents the highest measured values ​​of blood flow in the envelope dataset. Using multiple measured values ​​of blood flow to determine the blood flow scaling factor makes the scaling less susceptible to outlying measurements and other errors.

[0021] In some embodiments, the method further comprises generating a set of PDFs. In such embodiments, generating the set of PDFs comprises generating the set of PDFs from prior distributions for systolic and diastolic blood pressure. In such embodiments, the prior distributions include or are based on one or more of the following: information about physiologically likely values ​​of systolic and diastolic blood pressure, information about physiologically likely differences between systolic and diastolic blood pressure values, one or more previous values ​​of the subject's systolic and diastolic blood pressure, one or more characteristics of the subject, one or more characteristics of a sensor used to measure blood flow, and one or more characteristics of a device used to apply the restriction to the body part. In this way, PDFs can be constructed such that the likelihood of a subject's systolic and diastolic blood pressure being considered outside the normal physiological range for the subject or a population is relatively low unless strongly supported by a measured envelope dataset.

[0022] In some embodiments, the set of PDFs includes PDFs scaled by a PDF scaling factor.

[0023] In some embodiments, varying degrees of restriction are applied to the body part using a cuff that is inflated and / or deflated to multiple different pressure stages or that is continuously inflated or deflated. In such embodiments, the envelope data set is a set of measurement pairs, each including a measurement of blood flow in the body part and the respective pressure in the cuff at which the blood flow measurement is made. In such embodiments, the blood flow measurement is the amplitude of pressure oscillations in the cuff or a measurement of pressure oscillations in a fluid- or gel-filled pad below the cuff. Alternatively, in such embodiments, the blood flow measurement is obtained from a photoplethysmography (PPG) signal from a PPG sensor located on the body part distal to the cuff. Alternatively, in such embodiments, the blood flow measurement is obtained from an acoustic signal from an acoustic or ultrasonic sensor located on the body part distal to the cuff.

[0024] According to a second aspect, a computer program product is provided, comprising a computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured, when executed by a suitable computer or processing unit, to cause the computer or processing unit to perform a method according to the first aspect or any embodiment of the first aspect. According to a third particular aspect, an apparatus configured to determine blood pressure of a subject is provided, comprising a processing unit configured to receive an envelope dataset of the subject including measurements of blood flow in a body part of the subject for each of various degrees of restriction applied to the body part, use a set of probability density functions (PDFs) to determine respective likelihoods of obtaining measurements in the envelope dataset for various pairs of systolic and diastolic blood pressure values, and determine the systolic and diastolic blood pressures for the subject as the pair of systolic and diastolic blood pressure values ​​that results in the highest likelihood for the envelope dataset. This provides an apparatus for determining systolic and diastolic blood pressure measurements in the envelope dataset without significant reliance on a particular or single measurement point. As a result, the device is less susceptible to errors resulting from outlying measurements and other errors in the envelope data set.

[0025] In some embodiments, the processing unit is configured to use the set of PDFs by determining, for each measurement in the envelope dataset, a respective probability of obtaining the measurement in the envelope dataset for various pairs of systolic and diastolic blood pressure values, and by combining the determined respective probabilities of obtaining the measurement in the envelope dataset for the pair of systolic and diastolic blood pressure values, this embodiment enables the systolic and diastolic blood pressures to be determined according to the joint probability of each measurement in the resulting envelope dataset.

[0026] In such embodiments, the processing unit may be configured to combine the respective probabilities by multiplying them together or by summing the logarithms of the respective probabilities. An embodiment involving summing the logarithms has an advantage in identifying the systolic and diastolic blood pressure values ​​whose sum is the highest. In some embodiments, the processing unit is configured to use the set of PDFs by determining, for all possible pairs of systolic and diastolic blood pressure values, their respective likelihoods for obtaining a measurement in the envelope dataset. This embodiment has the advantage that no optimization algorithm is required to determine the systolic and diastolic blood pressure values, and that an exhaustive search of all possible pairs of systolic and diastolic blood pressure values, for example by determining the prominence of the most probable, may allow for quantifying the quality of the determination of the systolic and diastolic blood pressure values.

[0027] In an alternative embodiment, the processing unit is configured to determine the systolic and diastolic blood pressure values ​​using the set of PDFs by using an iterative optimization algorithm to determine the highest likelihood of the envelope data set. Such an embodiment has the advantage that the systolic and diastolic blood pressure values ​​can be determined more quickly and / or efficiently than using an exhaustive search through all possible combinations of systolic and diastolic blood pressures.

[0028] In some embodiments, the processing unit is configured to use the set of PDFs by determining a blood flow scaling factor based on one or more highest measured values ​​of blood flow in the envelope dataset and using the blood flow scaling factor to align the blood flow values ​​in the set of PDFs with the measured values ​​of blood flow in the envelope dataset. In such embodiments, it is possible to normalize the set of PDFs to the envelope dataset or normalize the envelope dataset to the set of PDFs. In such embodiments, the processing unit may be configured to determine the blood flow scaling factor based on a function that median-represents the highest measured values ​​of blood flow in the envelope dataset. Determining the blood flow scaling factor using multiple measured values ​​of blood flow makes the scaling less susceptible to outlying measurements and other errors.

[0029] In some embodiments, the processing unit is further configured to generate the set of PDFs. In such embodiments, the processing unit may be configured to generate the set of PDFs by generating the set of PDFs from a prior distribution for the systolic and diastolic blood pressures. In such embodiments, the prior distribution includes or is based on one or more of: information about physiologically likely values ​​of the systolic and diastolic blood pressures; information about physiologically likely differences between the systolic and diastolic blood pressure values; one or more previous values ​​of the subject's systolic and diastolic blood pressures; one or more characteristics of the subject; one or more characteristics of a sensor used to measure blood flow; and one or more characteristics of a device used to apply the restriction to the body part. In this way, the PDFs may be constructed such that the likelihood of a subject's systolic and diastolic blood pressure being considered to be outside the normal physiological range for the subject or a population is relatively low unless strongly supported by a measured envelope dataset.

[0030] In some embodiments, the set of PDFs includes PDFs scaled by a PDF scaling factor.

[0031] In some embodiments, varying degrees of restriction are applied to the body part using a cuff that is inflated and / or deflated to multiple different pressure stages or that is continuously inflated or deflated. In such embodiments, the envelope data set is a set of measurement pairs, each including a measurement of blood flow in the body part and the respective pressure in the cuff at which the blood flow measurement is made. In such embodiments, the blood flow measurement is the amplitude of pressure oscillations in the cuff or a measurement of pressure oscillations in a fluid- or gel-filled pad below the cuff. Alternatively, in such embodiments, the blood flow measurement is obtained from a photoplethysmography (PPG) signal from a PPG sensor positioned distal to the cuff on the body part. Alternatively, in such embodiments, the blood flow measurement is obtained from an acoustic signal from an acoustic or ultrasonic sensor positioned distal to the cuff on the body part.

[0032] In some embodiments, the processing unit is configured to receive the envelope data set from a pressure sensor that measures the pressure in the cuff. In such embodiments, the device may further comprise the pressure sensor, or the pressure sensor may be separate from the device. In alternative embodiments, the processing unit is configured to receive a measure of the degree of restriction applied to the body part from the pressure sensor that measures the pressure in the cuff, and a measure of blood flow in the body part from a blood flow sensor, such as a PPG sensor, a sound sensor, or an ultrasound sensor. In such embodiments, the device may further comprise the pressure sensor and / or the blood flow sensor, or the pressure sensor and / or the blood flow sensor may be separate from the device.

[0033] These and other aspects will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0034] Exemplary embodiments will now be described, by way of example only, with reference to the following drawings: [Brief explanation of the drawings]

[0035] [Figure 1]1 is a graph illustrating an exemplary envelope data set. [Figure 2] FIG. 1 is a block diagram of an apparatus that can be used to implement the techniques described herein. [Figure 3] 1 is a flow chart illustrating a method for determining blood pressure, according to various embodiments. [Figure 4] FIG. 1 shows a set of probability density functions (PDFs) for normalized oscillation amplitude over a range of cuff pressure values, at fixed systolic and diastolic blood pressures. [Figure 5] FIG. 10 is a map illustrating the likelihood of obtaining an envelope data set for various combinations of systolic and diastolic blood pressures. DETAILED DESCRIPTION OF THE INVENTION

[0036] As mentioned above, the disclosed techniques allow the systolic and diastolic blood pressures to be determined from an envelope dataset with less reliance on specific or single measurement points in the envelope dataset than conventional techniques.

[0037] An envelope dataset includes measurements of blood flow in a body part for various degrees of restriction applied to the body part. For example, if a cuff is used to apply pressure to a subject's upper arm, the blood flow is measured downstream of the cuff, i.e., in a part of the arm distal to the cuff (e.g., the lower arm or wrist). An envelope dataset therefore consists of a series of "measurement pairs" or "measurement points," each pair including a measurement of blood flow and a measurement of the pressure applied when that blood flow measurement was obtained (e.g., the static pressure in the cuff that is restricting blood flow). In the case of oscillometric measurements, the envelope dataset includes pairs of static cuff pressure and cuff pressure oscillation amplitude (static cuff pressure, cuff pressure oscillation amplitude). A set of measurement pairs is referred to herein as an "envelope dataset" or "measured envelope." The form of blood flow measurements may vary depending on the type of sensor used to measure blood flow. The sensor should specifically measure pulsatile blood flow. Measurements of blood flow may be obtained, for example, by sensors that measure or detect changes in volume (such as plethysmography, including photoplethysmography (PPG), oscillometric methods, or pressure sensing using fluid- or gel-filled pads placed between an inflatable cuff and a body part) or by sensors that measure flow velocity (such as Doppler ultrasound, or, to some extent, acoustic detection of Korotkoff sounds).

[0038] The graph in Figure 1 shows a portion of an exemplary envelope data set including measurements of oscillation amplitude in mmHg versus cuff pressure (also in mmHg) for a series of cuff pressures ranging from 70 mmHg to 160 mmHg. The subject's systolic blood pressure was 132 mmHg and diastolic blood pressure was 74 mmHg, as measured using a reference blood pressure measurement technique. It will be appreciated that the envelope data set may also include measurements of blood flow at cuff pressures outside the range shown in Figure 1.

[0039] In a technique for determining / estimating systolic and diastolic blood pressure, the measurement pairs of the envelope dataset are considered to be samples of a probability distribution that depends on the variables "systolic blood pressure" and "diastolic blood pressure." The technique uses the available measurement pairs of the envelope dataset, and optionally any prior information about the distribution of systolic and diastolic blood pressure, to find the systolic and diastolic blood pressure values ​​that maximize the likelihood of observing the measured envelope dataset.

[0040] In this method, more specifically, the individual observations x corresponding to pairs of measurements in the envelope data set are i Given a set of x, estimate the vector of population parameters θ (corresponding to the combination of systolic and diastolic blood pressure) by finding the value of θ that maximizes the joint probability of observation x from the sampling distribution f and, optionally, the prior distribution g(θ).

number

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[0041] The joint probability can be calculated as the product of the individual probabilities. However, in certain embodiments, it can be taken into account that the measurements of the envelope data set are not statistically independent, in which case the calculation of the joint probability can be more complicated than simply multiplying the individual probabilities. In this case, rather than multiplying the probabilities, the maximum can also be found by maximizing the sum of the logarithms of the individual probabilities, which avoids a long series of multiplications that are numerically ill-behaved.

number

[0042] Empirically, f should reflect the relationship that when the cuff pressure is outside the diastolic-systolic range, the blood flow is more likely to be non-pulsatile or weakly pulsatile, and within the diastolic-systolic range, the flow is more likely to be strongly pulsatile. In some preferred embodiments, f can be constructed as a composition of smooth functions such as logistic functions or bell curves, which facilitates the use of standard optimization algorithms to find the maximum. In some embodiments, the construction of f also takes into account some information available from the envelope data set itself, such as the range of minimum and maximum oscillation amplitudes.

[0043] Once f is constructed, one of several possible methods can be used to find the global maximum of f over the set of valid systolic and diastolic pairs. Because the set of possible systolic and diastolic pressure value pairs is limited in size, some embodiments realize that by calculating likelihood values ​​for all possible pairs and finding the maximum, the entire set can be exhaustively searched with moderate computational power. Standard optimization algorithms, such as interior point methods or gradient search, can also be used. Finally, depending on the choice of f, analytical solutions may be available.

[0044] The presented technique thus minimizes reliance on individual measurement points of the envelope dataset when calculating systolic and diastolic blood pressures. The technique also efficiently uses more or all of the available information, including measurement pairs from the envelope dataset that have no or negligible blood flow measurements (e.g., no or negligible oscillation amplitude). While the information contained in such measurement points is typically ignored in "maximum amplitude algorithm"-type approaches, the disclosed technique allows such measurement points to contribute information that makes the maximum of the likelihood function more prominent. For example, a measurement pair from the envelope dataset with a cuff pressure X and an amplitude close to zero makes a blood pressure range that does not include X more likely and a blood pressure range that includes X less likely.

[0045] FIG. 2 is a block diagram of a system 2 according to various embodiments for determining a subject's blood pressure in accordance with the techniques described herein. The system 2 forms a non-invasive blood pressure (NIBP) monitoring system 2, i.e., a system for non-invasively measuring a subject's blood pressure. The blood pressure is determined from an envelope dataset that includes measurements of blood flow in a body part of the subject for each of various degrees of restriction applied to the body part, as mentioned above. In the embodiment shown in FIG. 2, a cuff 4 is used to apply the restriction to the body part. However, it will be appreciated that other types of devices can be used to apply the restriction to the body part and alter the blood flow in the body part. For example, a tourniquet could be used that is electrically, mechanically, pneumatically, or hydraulically actuated to tighten and vary the applied pressure.

[0046] The system 2 of FIG. 2 includes a cuff 4, a pump 6 connected to the cuff 4 (e.g., via a connecting tube 7), and a cuff pressure sensor 8 for measuring the pressure within the cuff 4. The cuff pressure sensor 8 outputs a cuff pressure signal representative of or related to the pressure within the cuff 4 over time. The cuff pressure signal provides a measurement of various degrees of restriction applied to a body part of an envelope data set. The cuff 4 is to be placed around a body part of interest, e.g., around a limb, such as an arm or leg, and the pump 6 is controllable to selectively inflate the cuff 4. The pump 6 can also selectively deflate the cuff 4 and / or a valve (not shown) is provided to enable the cuff 4 to be deflated. The cuff pressure sensor 8 measures the pressure within the cuff 4 at least when the cuff 4 is applying a predetermined stimulus to the artery located inside the cuff 4 (which may be during inflation of the cuff 4, deflation of the cuff 4, or while the pressure within the cuff 4 is held at a particular level).

[0047] The cuff 4, pump 6, and cuff pressure sensor 8 can be considered to form a measurement device 10. In addition to the measurement device 10, the system 2 shown in FIG. 1 also includes an apparatus 12 that operates in accordance with the techniques described herein to determine the subject's blood pressure from the envelope data set provided by the measurement device 10. The apparatus 12 is therefore configured to receive a cuff pressure signal from the cuff pressure sensor 8. In some embodiments, the apparatus 12 is configured to control the operation of the pump 6, thereby initiating inflation / deflation of the cuff 4 at the appropriate times. Although the apparatus 12 is shown separate from the measurement device 10 in FIG. 2, it will be understood that in some implementations, the measurement device 10 can be part of the apparatus 12, or vice versa.

[0048] The device 12 may be in the form of, or may be part of, a computing device such as a server, desktop computer, laptop, tablet computer, smartphone, smartwatch, or other type of device typically found in a clinical environment, such as a patient monitoring device (e.g., a monitoring device located at a patient's bedside in a clinical environment) used to monitor (and optionally display) various physiological characteristics of a subject / patient.

[0049] System 2 includes a blood flow sensor for measuring blood flow (or pulsatile blood flow) in a body part of a subject. In some embodiments, the measurements of blood flow in the body part that form the envelope data set are measurements of the amplitude of pressure oscillations in cuff 4 due to blood flow. In such embodiments, the measurements of blood flow are obtained from the cuff pressure signal, and thus cuff pressure sensor 8 is used as the blood flow sensor. This type of blood flow measurement is also called oscillometric measurement or oscillometric method.

[0050] In an alternative embodiment, measurements of blood flow in a body portion forming the envelope data set are obtained using a blood flow sensor in the form of a PPG sensor 14 that provides a PPG signal. This type of blood flow measurement is also called photoplethysmography. Accordingly, in such an embodiment, the system 2 includes a cuff pressure sensor 8 and one or more PPG sensors 14. The one or more PPG sensors 14 may be part of the measurement device 10. The PPG sensor 14 should be positioned on the subject's body distal to the cuff 4 and outputs a PPG signal related to blood flow through that body portion. The PPG sensor 14 includes one or more optical sensors and, typically, one or more light sources, as known to those skilled in the art. The PPG signal output from the PPG sensor 14 is a raw measurement signal from the optical sensor; the PPG signal may be, for example, an analog or digital signal representing light intensity over time.

[0051] In another alternative embodiment, the measurements of blood flow in the body portion forming the envelope data set are obtained using a blood flow sensor in the form of a pressure sensor that measures the pressure or pressure oscillations in a fluid- or gel-filled pad positioned between the inflatable cuff 4 and the body portion. This type of sensor provides a hydraulic connection between the body portion and the cuff 4 and provides a higher quality signal than a pneumatic connection between the body portion and the air-filled cuff 4. The pressure sensor used to measure the pressure / pressure oscillations in the pad may be of the type typically used to invasively measure pressure in arteries. An example of such a blood flow sensor, also known as a "shell cuff," is described in "Clinical Evaluation of a High-Fidelity Upper Arm Cuff to Measure Arterial Blood Pressure during Noncardiac Surgery" by Josef Briegel, MD, et al., Anesthesiology, November 2020, Vol. 133, pp. 997-1006. The pressure sensor may be part of the measurement device 10. The pressure sensor may output a pressure signal related to the pressure in the pad due to pressure exerted by the cuff 4 and the blood flow of the body part passing through the pad and under the cuff 4. The pressure signal output from the pressure sensor is a raw measurement signal from the pressure sensor, the pressure signal may be an analog or digital signal, for example, representing the pressure / pressure oscillations in the pad over time.

[0052] In other alternative embodiments, the measurements of blood flow in the body part forming the envelope data set are obtained using a blood flow sensor in the form of one or more microphones 16 (to measure sounds of blood flow, including Korotkoff sounds caused by turbulent flow in the arteries) or one or more ultrasonic sensors 18 (to measure blood flow velocity from the Doppler shift). This type of blood flow measurement is also called auscultatory measurement. Thus, in such an embodiment, the system 2 comprises a cuff pressure sensor 8 and one or more microphones 16 or ultrasonic sensors 18. The one or more microphones 16 or one or more ultrasonic sensors 18 may be part of the measurement device 10. The sensors 16, 18 should be placed on the subject's body distal to the cuff 4 and output a sound signal related to blood flow through that body part. The sound signal output from the microphone 16 or ultrasonic sensor 18 is the raw measurement signal from the sensor 16, 18; the sound signal may be, for example, an analog or digital signal representing sound / ultrasound over time.

[0053] Those skilled in the art will understand that other types of (non-invasive) sensors can be used to obtain measurements of blood flow in a body part, and that the disclosed techniques are not limited to the sensors described above.

[0054] Device 12 includes a processing unit 22 that controls the operation of device 12 and may be configured to implement or perform the methods described herein. Processing unit 22 may be implemented in many ways using software and / or hardware to perform the various functions described herein. Processing unit 22 includes one or more microprocessors or digital signal processors (DSPs) programmed using software or computer program code to perform the necessary functions and / or to control the components of processing unit 22 to achieve the necessary functions. Processing unit 22 may be implemented as a combination of dedicated hardware (e.g., amplifiers, preamplifiers, analog-to-digital converters (ADCs) and / or digital-to-analog converters (DACs)) to perform some functions and processors (e.g., one or more programmed microprocessors, controllers, DSPs, and associated circuitry) to perform other functions. Examples of components that may be used in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, DSPs, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), hardware for implementing neural networks, and / or so-called artificial intelligence (AI) hardware accelerators (i.e., processors or other hardware specifically designed for AI applications that can be used in conjunction with a main processor).

[0055] Processing unit 22 is coupled to memory unit 24, which may store data, information, and / or signals used by processing unit 22 in controlling the operation of device 12 and / or in implementing or performing the methods described herein. In some implementations, memory unit 24 stores computer-readable code that may be executed by processing unit 22 to cause processing unit 22 to perform one or more functions, including the methods described herein. In particular embodiments, the program code may be in the form of an application for a smartwatch, smartphone, tablet, laptop, or computer. Memory unit 24 may comprise any type of non-transitory machine-readable medium, such as cache or system memory, including volatile and non-volatile computer memory, such as random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), and electrically erasable PROM (EEPROM), and memory unit 24 may be embodied in the form of a memory chip, an optical disk (such as a compact disk (CD), digital versatile disk (DVD), or Blu-ray disk), a hard disk, tape storage, or solid-state device, including a memory stick, solid-state drive (SSD), memory card, etc.

[0056] In some embodiments, device 12 includes a user interface 26 comprising one or more components that enable a user of device 12 to input information, data, and / or commands into device 12 and / or that enable device 12 to output information or data to a user of device 12. Information that may be output from user interface 26 may include systolic and diastolic blood pressures determined from an envelope dataset in accordance with the techniques described herein. User interface 26 may include any suitable input components, including, but not limited to, a keyboard, a keypad, one or more buttons, switches, or dials, a mouse, a trackpad, a touchscreen, a stylus, a camera, a microphone, etc., and / or user interface 26 may include any suitable output components, including, but not limited to, a display screen, one or more lights or light elements, one or more loudspeakers, a vibrating element, etc.

[0057] It will be appreciated that actual implementations of the apparatus 12 will include additional components to those shown in Figure 2. The apparatus 12 also includes a power source, such as a battery, or components to enable the apparatus 12 to be connected to a mains power source. The apparatus 12 also includes interface circuitry to enable data connection to and / or exchange of data with other devices, including any one or more of the measurement device 10 (or a separate cuff pressure sensor 8), sensors 14, 16, 18, servers, databases, and user devices.

[0058] The flowchart of Figure 3 illustrates a method for determining blood pressure according to an embodiment of the techniques described herein. Various steps of the method may be performed by device 12, specifically processing unit 22. In that regard, computer program code may be provided that, when executed by processing unit 22, causes processing unit 22, and thus device 12, to perform the methods described below. The method of Figure 3 may generally be implemented by a processor, processing unit, or computer. One or more of the steps of the method of Figure 3 may use or require the use of cuff 4 (or other blood flow restriction device), pump 6, cuff pressure sensor 8 (or other "degree of restriction" sensor and / or blood flow sensor), and (if present) any of PPG sensor 14, microphone 16, and ultrasonic sensor 18, as further mentioned below.

[0059] In step 101 of the method of Figure 3, an envelope dataset is received for a subject. The envelope dataset includes measurements of blood flow in a body part of the subject for various degrees of restriction applied to the body part, as mentioned above. The envelope dataset may be received in step 101 directly from the associated sensors 8, 14, 16, 18, for example, as blood flow measurements are acquired, in which case the envelope dataset is formed from the received measurements. Step 101 may alternatively comprise retrieving a previously acquired envelope dataset from a memory, for example, memory unit 24.

[0060] In step 103, a set of probability density functions (PDFs) is used to determine the respective likelihoods of obtaining an envelope data set measurement for various pairs of systolic and diastolic blood pressure values. As mentioned above, PDFs are used to quantify the likelihood of a particular envelope data set occurring for various pairs of systolic and diastolic blood pressure values. The PDFs in the set are functions of one or more variables: a blood flow measurement (e.g., oscillation amplitude), a degree of restriction (e.g., cuff pressure), systolic blood pressure, and diastolic blood pressure. In one embodiment, the PDFs in the set are functions of five variables: a blood flow measurement (e.g., oscillation amplitude), a degree of restriction (e.g., cuff pressure), systolic blood pressure, diastolic blood pressure, and maximum oscillation amplitude. In one embodiment, the PDFs in the set are functions of six variables: a blood flow measurement (e.g., oscillation amplitude), a degree of restriction (e.g., cuff pressure), systolic blood pressure, diastolic blood pressure, maximum oscillation amplitude, and a parameter representing prior information. Because the blood flow measurement is the key unknown variable obtained by measurement, PDFs are used to quantify the likelihood that a particular blood flow measurement at a particular degree of restriction corresponds to various systolic and diastolic blood pressures. Thus, each PDF in a set of PDFs can be considered to be for a particular value of the degree of restriction and a particular pair of systolic and diastolic blood pressure values.

[0061] Using the terminology of equations (1) and (2), the set of PDFs quantifies how likely it is to observe the set of measurement pairs x and parameter vector θ (corresponding to a combination of systolic and diastolic blood pressures) that form the envelope data set. The set of PDFs can be viewed as being made up of a series of subsets of PDFs, each corresponding to a particular pair of systolic and diastolic blood pressure values ​​(e.g., 120 mmHg systolic and 80 mmHg diastolic blood pressure), and each PDF in the subset is the PDF for that pair of systolic and diastolic blood pressure values ​​for a particular cuff pressure value (e.g., 100 mmHg).

[0062] An example PDF subset f(x) for a fixed systolic blood pressure of 120 mmHg and a fixed diastolic blood pressure of 80 mmHg is i |θ) is shown in FIG. 4 . This subset of PDFs consists of a respective PDF for each cuff pressure value. Based on this exemplary subset of PDFs, it is very likely to measure a low (or zero) oscillation amplitude far outside the cuff pressure interval of 80 mmHg to 120 mmHg, and extremely unlikely to measure a high oscillation amplitude. The opposite is true for cuff pressures within the interval of 80 mmHg to 120 mmHg. Normalization is discussed further below, and in some embodiments, oscillation amplitude normalization is performed using a number of highest magnitude oscillation amplitudes, e.g., using the median of a number of highest N magnitude oscillation amplitudes. Normalization is therefore not based solely on the maximum observed oscillation amplitude; therefore, the maximum observed oscillation amplitude will be normalized to a value greater than 1. This is taken into account by having the set of PDFs also cover normalized amplitudes greater than 1. A “good quality” measurement will typically have another envelope point close to the maximum value. Therefore, even when normalizing using the median, the maximum oscillation amplitude will be close to 1. However, in artifactual measurements, the maximum value may be an outlier, with a normalized value significantly greater than 1. This large value will have little effect on the location of the likelihood maximum, since it is equally unlikely for any combination of systolic / diastolic pressures.

[0063] Thus, step 103 uses the set of PDFs to determine likelihoods of pairs of systolic and diastolic blood pressure values ​​for the received envelope data set. In other words, step 103 provides likelihoods that a subject will have various pairs of systolic and diastolic blood pressure values ​​given the received envelope data set. Each likelihood determined in step 103 is also referred to herein as a "joint likelihood," because it represents the likelihood of all measurement pairs in the envelope data set occurring for a given systolic and diastolic blood pressure value pair.

[0064] A particular embodiment of step 103 has a two-step process for determining the respective likelihoods. In the first step, for each measurement pair in the envelope data set, respective probabilities of obtaining the measurement pair in the envelope data set for various pairs of systolic and diastolic blood pressure values ​​are determined. That is, for each measurement pair, a first probability is determined for a first pair of systolic and diastolic blood pressure values, a second probability is determined for a second pair of systolic and diastolic blood pressure values, and so on. In the second step, for each pair of systolic and diastolic blood pressure values ​​evaluated in the first step, the likelihood of obtaining the envelope data set is determined by combining the probabilities for that pair of systolic and diastolic blood pressure values ​​determined in the first step. In the second step, the probabilities can be combined by multiplying the probabilities or by summing the logarithms of the respective probabilities. Summing the logarithms of the respective probabilities may be recommended because it avoids a series of multiplications that may result in numerically ill-behaved results when multiplying probabilities.

[0065] In some embodiments, step 103 can include determining the likelihood of each of the envelope data sets for all possible pairs of systolic and diastolic blood pressure values, i.e., all possible combinations of systolic and diastolic blood pressure values ​​are evaluated in step 103. This exhaustive search is possible because the set of pairs of systolic and diastolic blood pressure values ​​is relatively small in size.

[0066] In alternative embodiments, step 103 may comprise applying an iterative optimization algorithm to determine each likelihood. Such an embodiment may typically be used when steps 103 and 105 (described below) are performed in parallel or otherwise simultaneously. In such an embodiment, it is not necessary to perform an exhaustive search and determine the likelihood for every possible pair of systolic and diastolic blood pressures.

[0067] Returning to the example subset of PDFs in FIG. 4, a highly probable measurement pair under this distribution would increase the likelihood of a blood pressure of 120 / 80 mmHg generating the set of measured envelope points, while a less probable measurement pair (e.g., a small oscillation amplitude at a cuff pressure of 100 mmHg, or a large amplitude at a cuff pressure of 150 mmHg) would decrease the likelihood of a blood pressure of 120 / 80 mmHg generating the measurement pair in the envelope data set.

[0068] Once the respective likelihoods have been determined in step 103, the subject's systolic and diastolic blood pressures are determined in step 105 to be the pair of systolic and diastolic blood pressure values ​​that yield the highest likelihood for the envelope data set.

[0069] In embodiments of step 103 in which an exhaustive search of possible systolic and diastolic blood pressure pairs is performed, step 105 can include determining the subject's systolic and diastolic blood pressure pair that yields the highest likelihood for the received envelope data set. In embodiments of step 103 in which an iterative optimization algorithm is used to determine each likelihood, the algorithm can be applied iteratively over the systolic and diastolic blood pressure value pairs to find the highest likelihood value for the envelope data set. Those skilled in the art will be aware of various different iterative optimization algorithms that can be used to implement steps 103 / 105, but details will not be provided herein. Nevertheless, techniques such as sequential quadratic programming, interior point methods, or active constraint methods can be used in steps 103 / 105. Depending on how the likelihood function is constructed, multiple local maxima may exist. In this case, a single run of a local optimization algorithm may reach one of the local maxima rather than the global maximum. A global optimization algorithm can also be used.

[0070] In embodiments where an exhaustive or substantially exhaustive search is performed (e.g., over all plausible combinations of systolic and diastolic blood pressure values), the method may further comprise determining the prominence or significance of the maximum likelihood. The prominence or significance may be determined by examining a neighborhood of a predetermined size around the maximum. The algorithm may calculate the average or minimum likelihood in the neighborhood and calculate the ratio of this value to the maximum. If the ratio is large, the maximum is considered prominent. If the ratio is small, the maximum is not very distinguishable / prominent / significant. The prominence or significance of the maximum may provide a quantification of the quality of the determination of the systolic and diastolic blood pressure values. In some embodiments, if the received envelope data set does not yield a sufficiently prominent or significant maximum, the systolic and diastolic blood pressure values ​​may be ignored, possibly as unreliable or not sufficiently accurate. The exact definition of prominence may depend on the function used to construct the likelihood function. The values ​​can be determined empirically (e.g., using actual envelopes that are known to be good or bad) as well as through simulation (e.g., using synthetic envelopes generated as inputs that represent both known good and bad envelopes), where statistical methods, including machine learning, can be used to determine optimal thresholds for ignoring / retaining systolic / diastolic blood pressure values.

[0071] FIG. 5 is a map illustrating the likelihood of obtaining the envelope data set shown in FIG. 1 for various combinations of systolic and diastolic blood pressures. The likelihoods shown in FIG. 5 are log-likelihoods of the envelope data set shown in FIG. 1. Thus, FIG. 5 represents the output of an exhaustive search of all possible pairs of systolic and diastolic blood pressures for the envelope data set of FIG. 1 in step 103. From FIG. 5, it can be seen that the highest likelihood for this envelope data set occurs for a systolic blood pressure of 132 mmHg and a diastolic blood pressure of 74 mmHg, and that the determined likelihood drops significantly (even to zero) for very small differences between these values ​​(e.g., the likelihood is near zero outside the systolic blood pressure range of 110-140 mmHg and near zero outside the diastolic blood pressure range of 60-80 mmHg).

[0072] Although not shown in FIG. 3 , the systolic and / or diastolic blood pressure values ​​determined in step 105 can be output, for example, visually and / or audibly, to the subject or another user of the method / apparatus 12, such as a physician or other healthcare provider, and / or electronically, for example, in the form of a signal transmitted to a device or apparatus that stores the systolic and / or diastolic blood pressure values ​​in the subject's patient record. Each PDF in the set of PDFs can be formed by a smooth function, such as a logistic function and / or a Gaussian bell curve. The use of a smoothing function can facilitate the use of an iterative optimization algorithm to find maximum likelihood. In a preferred embodiment, the set of PDFs can be constructed such that certain combinations of systolic and diastolic blood pressures have an associated probability of zero. For example, all pairs of systolic and diastolic blood pressures where the diastolic blood pressure is higher than the systolic blood pressure should have a probability of zero (from the definition of systolic and diastolic blood pressure).

[0073] The example subset of PDFs in FIG. 4 was generated by approximating the expected oscillation amplitude as a Gaussian bell curve, with cuff pressure as a parameter. Tracing the cuff pressure axis, the location of the maximum on the oscillation amplitude axis represents this bell curve. A family of bell curves with the largest mean value is used to define a set of probability density functions, with each bell curve in the family corresponding to a specific pair of systolic and diastolic blood pressure values. Each individual curve is scaled so that the area under the curve is 1 (because a real Gaussian bell curve is defined over the real world, but pressure and oscillation amplitude cannot be negative). While this is a rough approximation, it is still a suitable detector for the envelope data set obtained from 120 / 80 blood pressure.

[0074] Instead of approximating the envelope shape as a Gaussian bell curve, other bell-shaped functions can be used, including the extreme (non-smooth) case of a function that is 0 outside the diastolic / systolic interval and 1 inside the diastolic / systolic interval. This will still result in a detector for a given blood pressure.

[0075] To form the subset of probability density functions, a bell-shaped distribution can be used in the diastolic / systolic interval, while outside this interval a scaled bell-shaped distribution or a one-sided distribution such as the exponential distribution can be used.

[0076] It will be appreciated that in certain embodiments, the functions used to form the PDFs in the set may be selected as a compromise between computational complexity and modeling accuracy: a simple set of basis functions that performs "good enough" may be preferable to a computationally complex model that provides minimal additional benefit.

[0077] In some embodiments, step 103 includes scaling the envelope dataset or the set of PDFs based on the envelope dataset. This scaling is performed using a scaling factor called a "blood flow scaling factor." The blood flow scaling factor can be used to align the blood flow values ​​of the set of PDFs with the blood flow measurements of the envelope dataset. That is, the blood flow scaling factor can be used to normalize the blood flow values ​​of each PDF to the envelope dataset or to normalize the blood flow measurements of the envelope dataset. Alternatively, the blood flow scaling factor can be used to align the blood flow values ​​of the set of PDFs with the blood flow measurements of the envelope dataset by distributing the blood flow scaling factor to both the blood flow values ​​of the set of PDFs and the blood flow measurements of the envelope dataset. For example, the blood flow scaling factor can be calculated by multiplying one of (i) the blood flow values ​​in the set of PDFs and (ii) the blood flow measurements in the envelope dataset by the square root of the blood flow scaling factor, and dividing the other of (i) the blood flow values ​​in the set of PDFs and (ii) the blood flow measurements in the envelope dataset by the square root of the blood flow scaling factor. The blood flow scaling factor can be based on one or more maximum blood flow measurements in the envelope dataset. The magnitude of the maximum blood flow measurement in the envelope dataset typically depends on several factors that may vary between subjects and between measurements on the same subject, such as the subject's physical characteristics, cuff size, cuff type, and how securely the cuff is attached to the body part. Therefore, the PDF can be fitted with a blood flow scaling factor by using the magnitude of the maximum blood flow measurement to derive the blood flow scaling factor for the PDF. In some embodiments, the blood flow scaling factor is determined based on the largest magnitude blood flow measurement in the envelope dataset. For example, the blood flow measurement with the largest amplitude can be scaled to 1, and all other amplitudes can be scaled accordingly.However, because artifacts in the largest magnitude blood flow measurements can lead to significant errors, the blood flow scaling factor is preferably determined from multiple largest magnitude blood flow measurements so that the scaling is less susceptible to outlying measurements and other errors. The blood flow scaling factor can be determined from a function of multiple largest magnitude measurements, where the function can be a representative value such as the mean, mode, or median (the median is a preferred option over the mean and mode). The number of measurements used to determine the blood flow scaling factor can be between 2 and 10, e.g., 3, 5, or 7. The blood flow scaling factor is tolerant to one of the maximum values ​​being an artifact if the median is taken and the three largest magnitude measurements are used; the blood flow scaling factor is tolerant to two of the maximum values ​​being an artifact if the median is taken and the five largest magnitude measurements are used; etc. Determining the blood flow scaling factor from multiple largest magnitude blood flow measurements also makes the blood pressure measurement technique less dependent on a single measurement point in the envelope data set.

[0078] In some embodiments, the method may further comprise generating a set of PDFs. The set of PDFs may be generated as outlined above with respect to FIG. 4. As mentioned above, in some embodiments, prior information regarding the distribution of systolic and diastolic blood pressures may be used or considered when generating the PDFs. The prior information is in the form of one or more prior distributions g(θ). The prior information is based on static information, i.e., information that does not change or does not change quickly. Static information may include, for example, characteristics of the subject (e.g., age, height, weight, medical condition, medications, etc.), characteristics of the sensor used to measure blood flow (e.g., an indication of offset and / or noise present in measurements using that sensor), characteristics of the device (e.g., cuff) used to apply the restriction to the body part, information about physiologically likely values ​​of systolic and diastolic blood pressure, and information about physiologically likely differences between systolic and diastolic blood pressure values. In the last two examples (mentioned above), certain combinations of systolic and diastolic blood pressures have a zero probability of occurring, while other combinations of systolic and diastolic blood pressures have a near-zero or low probability of occurring. For example, since diastolic blood pressure cannot be higher than systolic blood pressure, the probability of any pair in which diastolic blood pressure is higher than systolic blood pressure should be zero. Particular values ​​of systolic and diastolic blood pressure at both extremes, e.g., very low and very high values ​​outside known physiological ranges, may also have a zero or near-zero probability. Similarly, certain values ​​of pulse pressure (the difference between systolic and diastolic blood pressure) are less likely and should have a near-zero or low probability of occurring. Similarly, certain values ​​of systolic and diastolic blood pressure, e.g., values ​​within the common physiological range, are more likely and may have a probability in the PDF that increases this likelihood. Certain values ​​of pulse pressure are also more likely, and the PDF should be constructed accordingly. With this prior information taken into account in the PDF, the likelihood of a subject's systolic and diastolic blood pressure being considered outside the normal physiological range is relatively low unless strongly supported by a measured envelope dataset.

[0079] In some embodiments, the prior information also or alternatively includes dynamic information, i.e., changing or rapidly changing information. Dynamic information may include one or more previous values ​​of the subject's systolic and diastolic blood pressure. This dynamic information can be used to generate a PDF such that new values ​​of systolic and diastolic blood pressure detected using the envelope dataset are more likely to be close to the subject's previous values ​​of systolic and diastolic blood pressure. The exact impact of the subject's previous values ​​on the PDF may depend on how recent the previous values ​​are, since blood pressure changes relatively slowly. Therefore, more recent previous values ​​are more likely to represent the current values ​​of systolic and diastolic blood pressure than older previous values.

[0080] While the strict mathematical definition of a PDF requires that its integral over the entire range be 1, it will be appreciated that the above method can be implemented using a PDF scaled by some PDF scaling factor such that its integral over the entire range is greater than (or less than) 1. It will also be appreciated that step 103 uses a set of PDFs accordingly. That is, step 103 includes using a set of PDFs, i.e., using a set of PDFs whose probability axes are scaled by a scaling factor. For example, with reference to FIG. 4 , the probability density axis can be scaled so that the highest probability density provided by the set of PDFs is 3. The PDF scaling factor may be any value, e.g., 2, 3, etc., or may be selected to improve the performance of the algorithm implementing steps 103 and 105.

[0081] Thus, a technique is provided for determining systolic and diastolic blood pressure from an envelope data set that is less dependent on a particular measurement point or a single measurement point in the envelope data set than conventional techniques.

[0082] Variations to the disclosed embodiments can be understood and produced by those skilled in the art, from a study of the drawings, the disclosure, and the appended claims, in practicing the principles and techniques described herein. In the claims, the word "comprising" does not exclude other elements or steps, nor does the word "a" or "an" exclude a plurality. A single processor or other unit fulfills the functions of several items recited in the claims. The mere fact that certain means are recited in mutually different dependent claims does not indicate that a combination of these means cannot be used to advantage. A computer program may be stored or distributed on a suitable medium, such as an optical storage medium or a solid-state medium, provided integrally with or as part of other hardware, or distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. 1. A computer-implemented method for determining blood pressure in a subject, the method comprising: receiving an envelope data set for the subject comprising measurements of blood flow in the body part of the subject for each of different degrees of restriction applied to the body part; using a set of probability density functions to determine the likelihood of obtaining the measurements in the envelope data set for each of various pairs of systolic and diastolic blood pressure values; determining the systolic and diastolic blood pressure values ​​for the subject as being the pair of systolic and diastolic blood pressure values ​​that yields the highest likelihood for the envelope data set; A method comprising:

2. 2. The method of claim 1, wherein using the set of probability density functions comprises determining a likelihood for obtaining the measurements in the envelope data set for each of all possible pairs of the systolic and diastolic blood pressure values.

3. 2. The method of claim 1, wherein using the set of probability density functions and determining the systolic and diastolic blood pressures comprises using an iterative optimization algorithm to determine the maximum likelihood for the envelope data set.

4. using the set of probability density functions determining a blood flow scaling factor based on one or more highest measurements of blood flow in the envelope data set; using a blood flow scaling factor to align the blood flow values ​​of the set of probability density functions with the measurements of blood flow in the envelope data set.

2. The method of claim 1, comprising:

5. 5. The method of claim 4, wherein determining the blood flow scaling factor comprises determining the blood flow scaling factor based on a function representing a median of the highest measurements of blood flow in the envelope data set.

6. The method of claim 1 , further comprising generating the set of probability density functions from a prior distribution for systolic and diastolic blood pressure.

7. The prior distribution is Information regarding physiologically probable values ​​of systolic and diastolic blood pressure; information about physiologically possible differences between systolic and diastolic blood pressure values; one or more previous values ​​of the subject's systolic blood pressure and diastolic blood pressure; one or more characteristics of the subject; One or more characteristics of the sensor used to measure blood flow; and one or more characteristics of a device used to apply the restriction to the body part 7. The method of claim 6, comprising or based on one or more of:

8. A computer readable medium having computer readable code embodied thereon, said computer readable code, when executed by a suitable computer or processing unit, causing said computer or processing unit to perform the method of any one of claims 1 to 7.

9. 1. An apparatus for determining blood pressure of a subject, the apparatus comprising: a processing unit; receiving an envelope data set for the subject comprising measurements of blood flow in the body part of the subject for each of various degrees of restriction applied to the body part; using a set of probability density functions to determine the likelihood of obtaining the measurements in the envelope data set for each of various pairs of systolic and diastolic blood pressure values; The apparatus determines the systolic and diastolic blood pressures for the subject as being the pair of systolic and diastolic blood pressure values ​​that yields the highest likelihood for the envelope data set.

10. 10. The apparatus of claim 9, wherein the processing unit uses the set of probability density functions to determine a likelihood for obtaining the measurements in the envelope data set for each of all possible pairs of the systolic and diastolic blood pressure values.

11. 10. The apparatus of claim 9, wherein the processing unit uses the set of probability density functions to determine systolic and diastolic blood pressures by using an iterative optimization algorithm to determine the maximum likelihood of the envelope data set.

12. The processing unit determining a blood flow scaling factor based on one or more highest measurements of blood flow in the envelope data set; using the blood flow scaling factor to align the blood flow values ​​of the set of probability density functions with the measurements of blood flow of the envelope data set; The apparatus of claim 9 , wherein the set of probability density functions is used by:

13. The apparatus of claim 12 , wherein the processing unit determines the blood flow scaling factor based on a function representing a median of the highest measurements of blood flow in the envelope data set.

14. the processing unit further comprising:

14. The apparatus of claim 9, wherein the set of probability density functions is generated from a prior distribution for systolic and diastolic blood pressure.

15. The prior distribution is information about physiologically probable values ​​of systolic and diastolic blood pressure; information about physiologically possible differences between systolic and diastolic blood pressure values; one or more previous values ​​of the subject's systolic blood pressure and diastolic blood pressure; one or more characteristics of the subject; One or more characteristics of the sensor used to measure blood flow; and 15. The apparatus of claim 14, comprising or based on one or more of one or more characteristics of a device used to apply the restriction to the body part.

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