Patent Publication Number: US-2017360314-A1

Title: Method, apparatus and computer program for determining a blood pressure value

Description:
FIELD OF THE INVENTION 
     The present invention concerns a method, apparatus and computer program for determining a blood pressure value. 
     DESCRIPTION OF RELATED ART 
     Blood pressure is the pressure exerted by circulating blood upon the walls of blood vessels. A person&#39;s blood pressure is usually expressed in terms of the systolic pressure (maximum pressure within an artery during the cardiac cycle), diastolic pressure (minimum pressure within an artery during the cardiac cycle) or pulse pressure (the difference between the systolic and the diastolic pressure values). 
     Systolic, diastolic and pulse pressure values are used to detect certain health or fitness states of a body and/or to detect diseases. 
     However, to measure systolic, diastolic or pulse pressure values requires measuring a blood pressure signal via an invasive sensor (sensors placed within an artery). 
     In the recent time, there were many attempts to provide estimates of blood pressure values on the basis of pulsatility signals, instead of invasive blood pressure signals. Pulsatility signals have the advantage that are easy to be measured non-invasively. These attempts have even increased because of the commercialization of low quality sensors that can be used in wearable devices, e.g. like a photoplethysmogram (PPG) implemented in consumer electronics. 
       FIG. 1  shows an example of a pulsatility signal  1  for one heart beat. A pulsatility signal  1  is the superposition of the forward pulsatility signal  2  generated by a forward pressure wave propagating within the artery, and the backward pulsatility signal  3  generated by a pressure wave reflecting back within the artery. 
     Conventionally, blood pressure values are obtained by analyzing the shape and waveforms of such pulsatility signals. Unfortunately, pulsatility signals are signals related to the blood pressure signals, but are not real blood pressure signals. The amplitude and shape distortions of pulsatility signals have been shown to lead to large errors on the estimation of blood pressure values. In particular, the values of DP (diastolic pressure), SP (systolic pressure), P 1  (central initial systolic peak) and ESP (pressure at the end of systole) of a pulsatiliy signal do correspond neither in amplitude nor in timing with the DP, SP, P 1  and ESP values of the underlying blood pressure signal. 
     However, because of the major advantage of using pulsatility signals instead of invasive blood pressure signals, several attempts to derive BP values from pulsatility signals are disclosed in the state of the art: 
     U.S. Pat. No. 5,140,990 discloses a mapping of PPG signal amplitudes into BP values according to a physiological model. 
     JP2000217796 discloses the mapping of several features extracted from the second derivative of a PPG signal into a BP value. 
     U.S. Pat. No. 7,238,159 discloses calculating a BP-related value from the parameters of a mathematical model fitted to a PPG waveform. 
     U.S. Pat. No. 7,326,180 discloses the comparison of a BP value against features calculated from a pulse wave monitor: comparison is done in order to evaluate the cardiovascular status of a patient. 
     US2011/0196244 discloses an apparatus to measure BP by means of processing a PPG signal. The PPG signal is processed by an ARMA filter and a random forest operator. 
     U.S. Pat. No. 8,398,556 discloses the calculation of a BP value from a feature based on the area under a portion of a PPG waveform. 
     However, all those features for the blood pressure value are not sufficiently robust against gain changes due to electronic drifts, skin colors, sensor-skin interface and/or do not provide blood pressure measurements sufficiently accurate. 
     BRIEF SUMMARY OF THE INVENTION 
     The object is to provide a method, an apparatus and a computer program for determining a blood pressure value which yields a robust and accurate value for the blood pressure from the pulsatility signal. 
     The object is solved by the independent claims. 
     The combination of a time-related and an amplitude-related feature to calculate the blood pressure value gives an accurate and robust estimate for the blood pressure value. 
     The combination of a time-related feature and an amplitude-related feature in a single blood pressure function has been found to provide highly reliable blood pressure values. Because of the fact that the information contained in both features is independent and complementary, this combination overcomes the performances of known methods for determining blood pressure values using a pulsatility signal. In addition, the use of a normalized amplitude-related feature (instead of an amplitude-related feature) makes the value even more independent from electronic drifts, skin colors and sensor-skin interface, facilitating the implementation of the method of the invention in low-cost consumer electronic devices. Another advantage is that the method disclosed herein can be applied on very simple pulsatility signals such as photoplethysmogram signals. 
     The dependent claims refer to advantageous embodiments of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention will be better understood with the aid of the description of an embodiment given by way of example and illustrated by the figures, in which: 
         FIG. 1  shows an example of a typical pulsatility signal. 
         FIG. 2  shows an embodiment of the apparatus for determining a blood pressure value. 
         FIG. 3  shows a wrist device with a reflective PPG pulsatility sensor, according to an embodiment. 
         FIG. 4  shows a smartphone with a reflective PPG pulsatility sensor, according to an embodiment. 
         FIG. 5  shows a fingertip device with a transmission PPG pulsatility sensor, according to an embodiment. 
         FIG. 6  shows an ear device with a transmission PPG pulsatility sensor, according to an embodiment. 
         FIG. 7  shows a chest device with an impedance pulsatility sensor, according to an embodiment. 
         FIG. 8  shows an arm device with an impedance pulsatility sensor, according to an embodiment. 
         FIG. 9  shows a wrist device with an impedance pulsatility sensor, according to an embodiment. 
         FIG. 10  shows an implanted accelerometer pulsatility sensor, according to an embodiment. 
         FIG. 11  shows an implanted PPG pulsatility sensor, according to an embodiment. 
         FIG. 12  shows an implanted impedance pulsatility sensor, according to an embodiment. 
         FIG. 13  shows the ejection area under the pulsatility signal. 
         FIG. 14  shows the total area under the pulsatility signal. 
         FIG. 15  shows an embodiment of the method for determining a blood pressure value. 
     
    
    
     DETAILED DESCRIPTION OF POSSIBLE EMBODIMENTS OF THE INVENTION 
       FIG. 2  shows an embodiment of the apparatus  10  for determining a blood pressure value. The apparatus  10  comprises a pulsatility signal section  11 , a pulse selection section  12 , a calculation section  13  and a user feature section  18 . 
     The pulsatility signal section  11  is configured to provide a pulsatility signal. In one embodiment, the pulsatility signal section  11  could be simply an interface configured to receive data representing a pulsatility signal. In another embodiment, the pulsatility signal section  11  could be or comprise a pulsatility sensor for measuring the pulsatility signal of a user. A pulsatility signal can be defined as a signal containing information on the periodic variation of blood flow and arterial diameter of a given segment of the arterial tree. The periodic variations are typically generated by the arrival of a pressure pulse at the given segment of the arterial tree. 
     In one embodiment, the pulsatility sensor is a PPG sensor. The PPG sensor can be transmission-based or reflective.  FIG. 3  shows an example apparatus  10  with a reflective PPG sensor  4 . The apparatus  10  is realized in a wrist device with a light source  19  and a light detector  20  on the side of the wrist device being in contact with the arm skin  21  of a user. Due to the pulsatility of blood flow through the tissue in the subcutaneous vasculature  22 , the reflective index of the skin  21  changes. The pulsatility signal of the user can be measured on the basis of the reflective index of the skin  21 . The wrist device can comprise also the sections  12  and/or  13  and/or  18  forming the complete apparatus  10 . Alternatively, the sections  12  and/or  13  and/or  18  can be arranged in another device of the apparatus  10  connected with the wrist device (e.g. by a cable or a wireless connection). The wrist device  10  can be for example a wrist device like a watch connected to a smart phone.  FIG. 4  shows another embodiment of the apparatus  10  realized in a smartphone with the light source  19  and the light detector  20 . The light source  19  can for example be realized by a flash of the smart phone  10 . The light detector  20  can for example be realized by a camera (e.g. CCD) of the smartphone  10 .  FIG. 5  shows another embodiment of the apparatus  10  comprising a fingertip device with a transmission PPG sensor  4  with the light source  19  and the light detector  20 . The fingertip device can comprise also the sections  12  and/or  13  and/or  18  forming the complete apparatus  10 . Alternatively, the sections  12  and/or  13  and/or  18  can be arranged in another device of the apparatus  10  connected with the fingertip device. An alternative transmission PPG sensor  4  can be arranged at the ear. Such a transmission PPG sensor  4  can for example be included in a hearing aid device. 
     However, a pulsatility sensor is not restricted to PPG sensors. Also bioimpedance pulsatility sensors are possible as shown in  FIGS. 7 to 9 . Such a bioimpedance sensor  23  comprises two electrodes  5  for injecting a current  6  and two further electrodes  7  for measuring a voltage  8 . This allows also to measure the pulsatility signal.  FIG. 7  shows a chest device with an impedance pulsatility sensor.  FIG. 8  shows an arm device with an impedance pulsatility sensor  23 .  FIG. 9  shows a wrist device with an impedance pulsatility sensor  23 . The pulsatility sensors where non-invasive sensors. Also implantable pulsatility sensors are possible. The pulsatility signal can for example be measured by an implanted accelerometer sensor  3  implanted in the body tissue  2  in the vicinity of an artery  9  (see  FIG. 10 ). The pulsatility signal can for example be measured by an implanted PPG sensor  4  implanted in vicinity of an artery (see  FIG. 11 ). The pulsatility signal can for example be measured by an implanted impedance sensor  23  implanted in vicinity of an artery  9  (see  FIG. 12 ). The pulsatility signal can for example be measured by an invasive arterial sensor implanted within an artery (not shown). 
     The pulsatility signal  1  of a user is a signal representing the pulsatility amplitude of blood flow through the tissue over the time of the user as shown in  FIG. 1 . The pulsatility signal is a periodic signal with the period given by the heartbeat of the user. The pulsatility signal consists therefore of a sequence of pulses.  FIG. 1  shows one exemplary pulse of the pulsatility signal  1 . 
     The pulse selection section  12  determines at least one pulse in the pulsatility signal  1  of the user. In a preferred embodiment, the pulse selection section  12  selects a plurality of pulses which are given to the calculation section  13 . The plurality of pulses can be a fix number of consecutive pulses. Preferably, N consecutive pulses of the pulsatility signal are identified and M&lt;N pulses of the N pulses are selected to be given to the calculation section  13 . The M pulses can be selected on the basis of a quality criterion. The quality criterion can be defined to identify the presence of a measurement artifact. For example, because measurement artifacts tend to be associated to very large amplitude signals, the (M-N) pulses with the largest amplitudes (e.g. largest SP or largest total surface under the pulse) can be removed. In another embodiment, only one pulse can be selected and given to the calculation section  13 . This is in particular advantageous for high quality pulsatility sensor and/or for high time resolution applications. In one embodiment, the start and end time of each pulse can be detected on the basis of signal analysis. Alternatively, the start and/or end point of each pulse can be detected on the basis of a second measurement. The second measurement should depend also on the heartbeat. This second measurement can be for example an electrocardiogram (ECG) signal from which the start time of each pulse (corresponding to the end time of the previous pulse) can be retrieved. The ECG signal can therefore be used as trigger signal for finding the start of each pulse of the pulsatility signal  1  of the user. The second measurement can for example also be a PPG signal, a bioimpedance signal or any suitable pulsatility signal. 
     The user feature section  18  is configured to provide user feature UF about the user related to the pulsatility signal  1 . The user feature UF can be used in the calculation section  13 . The user feature section  18  can comprise a storage section for storing the user feature(s). The user feature section  18  can alternatively or additionally comprise a user interface for entering the user feature(s). Examples for user features UF are one or any combination of gender, age, height, body mass index, . . . . Gender can be mathematically expressed by a binary value, e.g. by 1 for female and 0 for male or vice versa. However, the user feature section  18  is optional and is not necessary, if the blood pressure value calculated in the calculation section  13  is independent of a user feature. 
     The calculation section  13  comprises a time feature section  14 , an amplitude feature section  15 , a weight factor section  16  and a blood pressure section  17 . The calculation section  13  is configured to calculate a blood pressure value on the basis of the pulse(s) received from the pulse selection section  12 . In one embodiment, the calculation section  13  calculates one blood pressure value on request. In another embodiment, the calculation section  13  calculates continuously new blood pressure values, wherein for each calculation period one blood pressure value is calculated. In the latter case, the pulse selection section  12  gives within each calculation period one pulse or M pulses of the pulsatility signal  1  used for the calculation of a blood pressure value for this calculation period. In the following, the functioning of the calculation section  13  and its components is described for the calculation of one blood pressure value. For the calculation of a plurality of blood pressure values, this functioning is simply repeated. 
     The time feature section  14  is configured to determine at least one time-related feature TRF on the basis of the pulse(s) received from the pulse selection section  12 . Preferably, the at least one time-related feature TRF i  feature is computed from each pulse i of the M pulses of the pulsatility signal  1  received from the pulse selection section  12  so that for each time-related feature M values will be calculated. However, it is also possible to compute a time related feature TRF on the basis of M pulses, e.g. the heart rate. If only one pulse is received from the pulse selection section  12  for one blood pressure calculation period, the at least one time related feature TRF is computed only for this pulse. A time-related feature is preferably any time duration within a pulse, the inverse of such a time duration, or any other value computed from such a time duration, e.g. the average of a time duration or its inverse of a plurality of peaks. Examples for such a time-related feature are the time to first peak T 1  (duration between the start time of the pulse and its first peak or shoulder of pulse), time to second peak T 2  (duration between start time of pulse and second peak or shoulder of pulse), inverse time to first peak  1 /T 1 , inverse of time to second peak  1 /T 2 , time between first and second peak T 2 -T 1 , time to reflection Tr (duration between start time of pulse and arrival time of the reflected (backward) wave), ejection duration ED (duration between start time of pulse and time of closure of the aortic valve), heart rate. The detection and/or calculation of these time-related features TRF is well-known in the state of the art and is not described here in more detail. The at least one time-related feature TRF comprises L equal one, two, or more distinct time-related features TRF j . In the case that for each pulse i of the M pulses, L distinct time-related features TRF i,j  are calculated, for each calculation period, M*L time-related features TRF i,j  are calculated. 
     The amplitude feature section  15  is configured to determine at least one normalized amplitude-related feature NAF on the basis of the pulse(s) received from the pulse selection section  12 . Preferably, at least one normalized amplitude-related NAF i  feature is computed from each pulse i of the M pulses of the pulse pressure signal  1  received from the pulse selection section  12 . However, it is also possible to compute a normalized amplitude-related feature NAF on the basis of the average of the M pulses. If only one pulse is received from the pulse selection section  12  (e.g. for one/each calculation period), the at least one normalized amplitude-related feature NAF is computed only for this pulse. An amplitude-related feature AF is preferably any value based on an amplitude value of the pulsatility signal  1 . A normalized amplitude-related feature NAF is an amplitude-related feature normalized by another amplitude-related feature. Normalization is preferably performed by a ratio of two amplitude-related features NAF=AF 1 /AF 2 . In one embodiment, such a normalized amplitude-related feature NAF is a normalized end-systolic pressure nESP=(ESP−DP)/PP calculated by the difference of the absolute end-systolic pressure ESP and the diastolic pressure DP divided or normalized by the pulse pressure PP. In one embodiment, such a normalized amplitude-related feature NAF is a first augmentation index Alx=(P 2 −P 1 )/PP calculated by the difference of the pressure amplitude of the second peak P 2  and the pressure amplitude of the first peak P 1  divided or normalized by the pulse pressure PP. In one embodiment, such a normalized amplitude-related feature NAF is a second augmentation index Alp=(P 2 −DP)/(P 1 −DP) calculated by the difference of the pressure amplitude of the second peak P 2  and the diastolic pressure amplitude DP divided or normalized by the difference of the pressure amplitude of the first peak P 1  and the diastolic pressure amplitude DP. In one embodiment, such a normalized amplitude-related feature NAF is a normalized ejection area nEjecA (see  FIG. 13 ). The normalized ejection area is calculated as the surface under the pulsatility signal  1  for the ejection duration ED divided or normalized by the area under the pulsatility signal  1  for the duration of this pulse (see  FIG. 14 ). The surface under the pulsatility signal  1  for the ejection duration ED does preferably not include the surface DP*ED below the diastolic pressure DP as shown in  FIG. 13 , but it is also possible to use the surface under the pulsatility signal  1  for the ejection duration ED with the surface DP*ED below the diastolic pressure DP. The surface under the pulsatility signal  1  for the duration T of the pulse does preferably not include the surface DP*T below the diastolic pressure DP as shown in  FIG. 14 , but it is also possible to use the surface under the pulsatility signal  1  for the pulse duration T with the surface DP*T below the diastolic pressure DP, if the surface DP*ED below the diastolic pressure is also considered for the ejection duration. The detection and/or calculation of the mentioned normalized amplitude-related features NAF are well-known and are therefore not described in more detail here. The at least one normalized amplitude-related features NAF comprises K equal one, two, or more distinct normalized amplitude-related features NAF j . In the case that for each pulse i of the M pulses, K distinct normalized amplitude-related features NAF i,j  are calculated so that, for each calculation period, M*K normalized amplitude-related features NAF i,j  are calculated. 
     The weight factor section  16  is optional and only necessary, if the calculation section  13  calculates a weighted average of the time-related feature(s) TRF and/or the normalized amplitude-related feature(s) NAF over a plurality of pulses with weights depending on the plurality of pulses. The weight factor section  16  is configured to calculate a weighting factor WF i  for each pulse i of the plurality M of pulses received from the pulse selection section. The weighting factor WF i  is preferably calculated on the basis of a quality criterion of the pulse. This could be for example the error of the pulse i relative to the average of the M pulses. Such an error can be calculated by a normalized accumulation of the errors between the points of the average pulse and the corresponding points of the respective pulse i. The errors can be the absolute errors, the quadratic errors, or any other measure for the error. 
     In one embodiment, the weighting factor WF i  is based on a morphological error. A morphological error can be defined as a value describing the morphology of a particular pulse. Typical morphologies have been defined in the literature (Nichols et al, “McDonald&#39;s blood flow in arteries”, Oxford University Press 2005, ISBN 0 340 80941 8) and describe the timing and amplitude relationships between P 1 , P 2 , T 1  and T 2  (see  FIG. 1 ). These morphologies may change among subjects depending on their age and cardiovascular status. Typical morphologies are Type C (associated to young subjects), Type B (associated to mid-age subjects), and Type A and D (associated to elderly and ill subjects). Other sub-morphologies can be defined as well. In a preferred embodiment, the weighting factor WF i  of the pulse i depends on how much the pulsatility signal i matches to a typical morphology. This could be for example the error of the pulse i relative to the most likely subject pulse morphology according to his age and health status. 
     In a yet preferred embodiment, the weighting factor WF i  of the pulse i depends on the morphological error of the pulse i and the error of the pulse i relative to the average of the M pulses. In one embodiment, the calculated quality criterion of each pulse i, e.g. the mentioned error and/or the morphological error, is compared to a quality threshold. Based on the comparison (above or below) the weight factor WF i  is set to zero (0) for bad quality and one (1) for a good quality. According to this binary weighting procedure only the pulses with a certain quality are used. 
     The pulsatility signal ( 1 ) can comprise a further plurality of pulses being larger than the plurality of pulses, wherein the plurality of pulses is selected among the further plurality of pulses by removing the pulses with the largest amplitude. 
     The blood pressure section  17  is configured to calculate a blood pressure value on the basis of the at least one time-related feature TRF and the at least one amplitude-related feature NAF. In one (optional) embodiment, a blood pressure function for calculating the blood pressure value depends additionally on the at least one user feature. In one embodiment, a blood pressure function for calculating the blood pressure value depends linearly on the at least one time-related feature TRF and the at least one normalized amplitude-related feature NAF. In one embodiment, a blood pressure function for calculating the blood pressure value depends linearly on the used feature(s) (TRF, NAF and/or UF). Preferably, each feature is weighted relative to the other features by linear feature coefficients, like the linear time coefficient(s) kt, the linear amplitude coefficient(s) ka and the linear user coefficient ku. The linear time coefficient(s) kt and the linear amplitude coefficient(s) ka and, if user feature(s) UF is/are considered, a linear user coefficient ku fix the relative influence of the time-related feature(s) TRF and the normalized amplitude-related feature(s) and, if user features UF are considered, the user feature UF on the blood pressure value. If one or more of the features are calculated for a plurality of pulses, the feature coefficient k remains the same for the same feature for all pulses. In one embodiment, the blood pressure function depends on the at least one time-related feature TRF i  and the at least one amplitude-related feature NAF i  of a plurality of pulses i. In this case, the function for the blood pressure value BP is direct proportional to 
     
       
         
           
             
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     with M pulses, L time-related features TRF and K normalized amplitude-related features NAF with L being one, two or more and with K being one, two or more. If M=1 only one pulse is considered. If L=1, only one time-related feature TRF is considered. If K=1, only one normalized amplitude-related feature is considered. Therefore, the linear feature coefficient might be different for each feature, but is the same for the same feature of different pulses. Considering also user features UF, the function for the blood pressure value BP is direct proportional to 
     
       
         
           
             
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     with M pulses, L time-related features TRF, K normalized amplitude-related features NAF and U user features with L being one, two or more, with K being one, two or more and U being one, two or more. If U=1, only one user feature is considered. Obviously, the term of the user features could be taken out of the sum over the pulses, if the weight factors WF sum up to one or if the user coefficients are adapted accordingly. 
     In one embodiment, the feature coefficients kt, ka, ku are predetermined and stored in a storage section of the blood pressure section  17 . In one embodiment, the feature coefficients kt, ka, ku are configurable. The feature coefficients kt, ka, ku can be set by a user or an administrator. The features coefficients kt, ka, ku can also be determined by an automatic process, e.g. a calibration process. The calibration process comprises the step of measuring the blood pressure of the user with an independent sensor and minimizing the error of the blood pressure value calculated by the blood pressure section on the basis of the feature coefficients kt, ka, ku. In medical applications, the apparatus can include a user interface for entering once or periodically blood pressure values measured independently. This allows an online monitoring of the blood pressure of the user which reaches almost the preciseness of traditional cuff measurements, if the independent measurements are measured by cuff measurements of the blood pressure. 
     The calibration process can further include also the selection of the best time-related features and/or normalized amplitude-related features and/or user-related features among O potential features. This can be performed by performing the above described calibration process for all O! (factorical of O) potential combination of features and select the best combination of feature including at least one time-related feature and at least one normalized amplitude-related feature. 
     Even if the blood pressure value is calculated without having any absolute pressure amplitude value, the inventive method yields a robust blood pressure value. If the method is used without a calibration on the particular user, the results are still good enough to robustly detect hypotension (low blood pressure), normal blood pressure and hypertension (high blood pressure). If the method is used with a calibration on the particular user, the results yield a good estimate of the blood pressure value which is even good enough for medical applications. Such an algorithm could be used with pulse oximetry fingertip sensors including a PPG transmission sensor as shown in  FIG. 5  or for other medical sensors shown for example in  FIGS. 6 to 9  and described above. This would allow an online monitoring of the blood pressure with high preciseness instead of the state of the art cuff measurements which can only be repeated in fixed time intervals. 
       FIG. 15  shows an embodiment of the method for determining a blood pressure value. 
     In step S 1 , a pulsatility signal of the user is provided as described in more detail with respect to the pulsatility signal section  11 . In step S 2 , a pulse or a number of pulses are selected from the provided blood pressure signal of Si. In step S 3 , at least one time-related feature is determined for the one pulse or for each of the number of pulses as described in more detail with respect to the time feature section  14 . In step S 4 , at least one normalized amplitude-related feature is determined for the one pulse or for each of the number of pulses as described in more detail with respect to the amplitude feature section  15 . In optional step S 5 , at least one user feature is determined as described in more detail with respect to the user feature section  18 . In optional step S 6 , a weighting factor WF i  is determined for each of the number of pulses as described in more detail with respect to the weight factor section  16 . In step S 7 , the blood pressure value is calculated on the basis of the at least one time-related feature TRF and the at least one normalized amplitude-related feature NAF and optionally on the weighting factors WF and the at least one user feature(s) as described in more detail with respect to the blood pressure section  17 .