blood pressure monitor

Abstract

To provide a sphygmomanometer capable of accurately determining presence or absence of atrial fibrillation while reducing a burden on a user when measuring blood pressure.SOLUTION: A sphygmomanometer includes: a blood pressure measuring part for measuring blood pressure of a user on the basis of a pulse wave signal superposed on a cuff pressure signal detected in the process of pressurizing or depressurizing cuff pressure indicating inner pressure of the cuff mounted on a measurement site of the user; a pulse wave number measuring part for measuring the pulse wave number of the user on the basis of the pulse wave signal; an interval calculation part for calculating a data group of pulse wave intervals on the basis of the pulse wave signal; a clustering part for clustering the data group of pulse wave intervals to one or more cluster using a threshold; and determination part for determining whether or not atrial fibrillation has occurred in the user on the basis of an index value showing the size of variations in the data group belonging to the cluster. The clustering part sets a threshold on the basis of the pulse wave number.SELECTED DRAWING: Figure 3

Description

[[Technical Field]]

[0001] The present disclosure relates to a sphygmomanometer, and particularly to a sphygmomanometer having a function of determining atrial fibrillation. [[Background Art]]

[0002] Early detection is desired for atrial fibrillation, which causes heart disease. Conventionally, techniques for estimating atrial fibrillation from pulse wave information acquired by a sphygmomanometer have been proposed. Specifically, by performing multiple blood pressure measurements in one measurement session using a sphygmomanometer, pulse wave intervals, which are intervals between pulse wave signals acquired in each blood pressure measurement, are obtained, and atrial fibrillation is detected based on the pulse wave intervals.

[0003] For example, US Patent Application Publication No. 2016 / 0228017 (Patent Document 1) discloses a blood pressure measurement device capable of indicating the presence or absence of atrial fibrillation. [[Prior Art Documents]] [[Patent Documents]]

[0004] [[Patent Document 1]] US Patent Application Publication No. 2016 / 0228017 Specification [[Summary of the Invention]] [[Problem to be Solved by the Invention]]

[0005] In the device disclosed in Patent Document 1, in order to determine the presence or absence of atrial fibrillation, it is necessary to continuously repeat a sequence defined by a predetermined number of pulse beats or the like a plurality of times (for example, 3 times) in one measurement opportunity. As a result, the time required for measurement becomes longer, and the measurement site is compressed by the cuff, which gives the user a feeling of being constrained and imposes a burden on the user.

[0006] The present disclosure aims to provide a blood pressure monitor that, in certain situations, can accurately determine the presence or absence of atrial fibrillation while reducing the burden on the user during blood pressure measurement. [Means for solving the problem]

[0007] In one example of this disclosure, the blood pressure monitor includes: a blood pressure measurement unit that measures the user's blood pressure based on a pulse wave signal superimposed on a cuff pressure signal detected during the process of increasing or decreasing cuff pressure, which indicates the internal pressure of a cuff attached to the user's measurement site; a pulse wave number measurement unit that measures the user's pulse wave number based on the pulse wave signal; an interval calculation unit that calculates a data set of pulse wave intervals based on the pulse wave signal; a clustering unit that clusters the data set of pulse wave intervals into one or more clusters using a threshold; and a determination unit that determines whether or not atrial fibrillation has occurred in the user based on an index value indicating the magnitude of variability of the data sets belonging to the cluster. The clustering unit sets a threshold based on the pulse wave number.

[0008] With the above configuration, it is possible to accurately determine the presence or absence of atrial fibrillation while reducing the burden on the user during blood pressure measurement.

[0009] In other examples of this disclosure, the clustering unit increases the threshold as the pulse wave number decreases.

[0010] With the above configuration, the threshold is appropriately set according to the pulse wave rate, so that appropriate atrial fibrillation detection can be performed for each user.

[0011] In other examples of this disclosure, the threshold is set to be less than or equal to the mean value of the pulse interval data set.

[0012] The above configuration reduces the possibility of misdiagnosing arrhythmias other than atrial fibrillation (e.g., premature contractions) as atrial fibrillation.

[0013] In another example of the present disclosure, when a data group of pulse wave intervals is clustered into one cluster, the determination unit determines that atrial fibrillation has occurred in the user when a first index value indicating a magnitude of variation of the data group belonging to the one cluster is equal to or greater than a predetermined value.

[0014] According to the above configuration, atrial fibrillation determination can be performed more accurately.

[0015] In another example of the present disclosure, when a data group of pulse wave intervals is clustered into a plurality of clusters, the determination unit determines that atrial fibrillation has occurred in the user when a second index value indicating a magnitude of variation of the data group belonging to the plurality of clusters is equal to or greater than a predetermined value.

[0016] According to the above configuration, atrial fibrillation determination can be performed more accurately.

[0017] In another example of the present disclosure, when the second index value is less than the predetermined value, the determination unit determines that an arrhythmia other than atrial fibrillation has occurred in the user.

[0018] According to the above configuration, the occurrence or non-occurrence of arrhythmias other than atrial fibrillation can also be determined. Effects of the Invention

[0019] According to the present disclosure, during blood pressure measurement, the presence or absence of atrial fibrillation can be accurately determined while reducing the burden imposed on the user. Brief Description of Drawings

[0020] [Figure 1] It is a figure showing the sphygmomanometer according to the present embodiment. [Figure 2] It is a block diagram showing an example of the hardware configuration of the sphygmomanometer. [Figure 3] It is a block diagram showing the functional configuration of the sphygmomanometer. [Figure 4] It is a figure showing an example of the data group of pulse wave intervals. [Figure 5]FIG. 1 is a diagram for explaining a clustering method. [Figure 6] FIG. 1 is a diagram showing a clustering result. [Figure 7] FIG. 1 is a diagram for explaining a threshold setting method. [Figure 8] FIG. 1 is a diagram for explaining an upper limit value of a threshold. [Figure 9] FIG. 1 is a flowchart for explaining a processing procedure executed by a sphygmomanometer. [Figure 10] FIG. 1 is a flowchart showing an example of blood pressure measurement processing of a sphygmomanometer. [Figure 11] FIG. 1 is a flowchart showing another example of blood pressure measurement processing of a sphygmomanometer. DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description, identical components are assigned identical reference numerals. Their names and functions are also identical. Therefore, detailed descriptions thereof will not be repeated.

[0022] [Application Example] An application example of the present invention will be described with reference to FIG. 1. FIG. 1 shows a sphygmomanometer 100 according to the present embodiment.

[0023] Referring to FIG. 1, the sphygmomanometer 100 is an upper-arm sphygmomanometer that measures the blood pressure of a subject who is a user. The sphygmomanometer 100 includes a main body and a cuff (arm band) as main components. The sphygmomanometer 100 may be a wrist-type sphygmomanometer in which the main body and the cuff (arm band) are integrated. The processing content will be described below with reference to FIG. 1.

[0024] Figure 1 illustrates a scenario in which a user measures their blood pressure using a blood pressure monitor 100. The blood pressure monitor 100 begins measuring blood pressure according to the user's instructions (corresponding to (1) in Figure 1). Specifically, the blood pressure monitor 100 extracts the pulse wave signal (variable component) superimposed on the cuff pressure, which indicates the internal pressure of the cuff attached to the user's measurement site (e.g., arm), and calculates the blood pressure value using the oscillometric method based on this pulse wave signal. For example, the blood pressure monitor 100 performs blood pressure measurement using either a pressurization measurement method, which measures blood pressure during the cuff pressurization process, or a depressurization measurement method, which measures blood pressure during the depressurization process after the pressurization process.

[0025] The blood pressure monitor 100 measures (counts) the number of pulse waves (corresponding to (2) in Figure 1) and calculates a set of pulse wave interval data (corresponding to (3) in Figure 1) based on the pulse wave signal obtained during blood pressure measurement (obtained during the pressurization process in the case of pressurization measurement methods, or during the depressurization process in the case of depressurization measurement methods). Typically, the pulse wave interval is the interval between peaks of the pulse wave (or the equivalent interval between bottoms).

[0026] For example, if the pulse wave signal Pa shown in Figure 1 is obtained, the data set of pulse wave intervals ta1 to ta5 is calculated. Similarly, if the pulse wave signal Pb is obtained, the data set of pulse wave intervals tb1 to tb5 is calculated, and if the pulse wave signal Pc is obtained, the data set of pulse wave intervals tc1 to tc5 is calculated.

[0027] Pulse wave signal Pa is an example of a pulse wave signal indicating atrial fibrillation. In pulse wave signal Pa, the pulse wave intervals ta1 to ta5 are generally irregularly scattered, indicating random pulse wave generation. Pulse wave signal Pb is an example of a pulse wave signal indicating normal sinus rhythm. In pulse wave signal Pb, the pulse wave intervals tb1 to tb5 are generally identical, indicating regular pulse wave generation. Pulse wave signal Pc is an example of a pulse wave signal indicating arrhythmia other than atrial fibrillation (e.g., premature contractions). In pulse wave signal Pc, the pulse wave intervals tc1 to tc3 and tc5 are generally identical, but only pulse wave interval tc4 is of a different magnitude, indicating a partial absence of pulse wave.

[0028] In this embodiment, we focus on the fact that the pulse wave intervals in the pulse wave signal indicating atrial fibrillation are generally irregularly varied, and we use an index value called Cstd (Clustered Standard Deviation), which is an example of an index value that indicates the magnitude of the variation (hereinafter also referred to as the "variability index value"), to determine whether or not atrial fibrillation is present.

[0029] The blood pressure monitor 100 clusters the calculated pulse wave interval data set using a threshold Th to generate one or more clusters (corresponding to (4) in Figure 1). For example, the blood pressure monitor 100 clusters the pulse wave interval data set by comparing the difference between each pulse wave interval included in the pulse wave interval data set with the threshold Th.

[0030] For example, the data set of pulse wave intervals ta1 to ta5 is classified into one cluster with large variability between each data point. The data set of pulse wave intervals tb1 to tb5 generates one cluster with small variability between each data point. The data set of pulse wave intervals tc1 to tc5 is classified into two clusters: one to which pulse wave intervals tc1 to tc3 and tc5 belong, with small variability between each data point, and another to which pulse wave interval tc4 belongs.

[0031] The blood pressure monitor 100 determines the presence or absence of atrial fibrillation based on the variability index value of the data group belonging to the cluster (corresponding to (5) in Figure 1). In the data group of pulse wave intervals ta1 to ta5, which corresponds to atrial fibrillation, the variability between each data is large, so the variability index value for this data group is large. On the other hand, in the data group of pulse wave intervals tb1 to tb5, which corresponds to normal sinus rhythm, and the data group of pulse wave intervals tc1 to tc5, which corresponds to premature contractions, the variability between each data belonging to the cluster is small, so the variability index value for this data group is small. Using this, the blood pressure monitor 100 determines that atrial fibrillation has occurred if the variability index value of the data group belonging to the cluster is greater than or equal to a predetermined value.

[0032] The blood pressure monitor 100 then displays the measured blood pressure value and the atrial fibrillation diagnosis result on its display (corresponding to (6) in Figure 1).

[0033] According to the above application example, blood pressure measurement and atrial fibrillation detection are performed simultaneously in a single measurement opportunity, and multiple blood pressure measurements are not required for atrial fibrillation detection. As a result, both blood pressure measurement and atrial fibrillation detection can be achieved while reducing the burden on the user, such as repeated compression of the measurement site and prolonged blood pressure measurement time. Furthermore, by using a variability index value (e.g., Cstd), it is possible to distinguish between atrial fibrillation and other arrhythmias, thereby improving the accuracy of atrial fibrillation detection.

[0034] [Example Configuration] <Hardware Configuration> Figure 2 is a block diagram showing an example of the hardware configuration of the blood pressure monitor 100. Referring to Figure 2, the blood pressure monitor 100 includes, as its main components, a main body 10 and a cuff 20. The cuff 20 contains a fluid bag 22. The main body 10 includes a processor 110, an air system component 30 for blood pressure measurement, an A / D conversion circuit 310, a pump drive circuit 320, a valve drive circuit 330, a display 50, a memory 51, an operating unit 52, a communication interface 53, and a power supply unit 54.

[0035] The processor 110 is an arithmetic processing unit such as a CPU (Central Processing Unit) or MPU (Multi Processing Unit). The processor 110 reads and executes programs stored in the memory 51 to realize each of the processes (steps) of the blood pressure monitor 100, which will be described later. For example, the processor 110 controls the pump 32 and valve 33 to drive them in response to operation signals from the operation unit 52. The processor 110 also calculates blood pressure values ​​using an algorithm for blood pressure calculation using the oscillometric method and displays them on the display 50.

[0036] Memory 51 is implemented using RAM (Random Access Memory), ROM (Read-Only Memory), flash memory, etc. Memory 51 contains a program for controlling the blood pressure monitor 100, data used to control the blood pressure monitor 100, setting data for setting various functions of the blood pressure monitor 100, and data on blood pressure measurement results, pulse wave rate, pulse wave It stores intervals and other information. Memory 51 is also used as work memory when the program is executed.

[0037] The air system component 30 supplies or discharges air to or from the fluid bag 22 enclosed in the cuff 20 through air piping. The air system component 30 includes a pressure sensor 31 for detecting the pressure inside the fluid bag 22, and a pump 32 and valve 33 as an inflation / deflation mechanism for inflating and deflating the fluid bag 22.

[0038] The pressure sensor 31 detects the pressure (cuff pressure) inside the fluid bag 22 and outputs a signal (cuff pressure signal) corresponding to the detected pressure to the A / D conversion circuit 310. The pressure sensor 31 is, for example, a piezoresistive pressure sensor and is connected via air piping to the pump 32, valve 33, and the fluid bag 22 enclosed in the cuff 20. The pump 32 supplies air as fluid to the fluid bag 22 via the air piping to pressurize the cuff. The valve 33 is opened and closed to control the cuff pressure by discharging air from the fluid bag 22 via the air piping or by sealing air into the fluid bag 22.

[0039] The A / D conversion circuit 310 converts the output value of the pressure sensor 31 (for example, a voltage value corresponding to the change in electrical resistance due to the piezoresistive effect) from an analog signal to a digital signal and outputs it to the processor 110. The processor 110 acquires a signal representing the cuff pressure according to the output value of the A / D conversion circuit 310. The pump drive circuit 320 controls the drive of the pump 32 based on the control signal provided by the processor 110. The valve drive circuit 330 controls the opening and closing of the valve 33 based on the control signal provided by the processor 110.

[0040] The processor 110 performs blood pressure measurement using either a pressurization measurement method, which measures the user's blood pressure based on the pulse wave signal during the pressurization process in which the cuff pressure is increased, or a depressurization measurement method, which measures the user's blood pressure based on the pulse wave signal during the depressurization process in which the cuff pressure is reduced after the pressurization process in which the cuff pressure is increased to a pressure greater than a specified pressure (e.g., "estimated systolic blood pressure" described later).

[0041] For example, when measuring using a reduced pressure measurement method, the following actions are generally performed: A cuff is placed on the user's measurement site (wrist, arm, etc.) in advance. 20 The cuff is wrapped around the arm, and during measurement, the pump 32 and valve 33 are controlled to pressurize the cuff to a level higher than the estimated systolic blood pressure, and then gradually reduce the pressure. During this depressurization process, the cuff pressure is detected by the pressure sensor 31, and fluctuations in arterial volume occurring in the artery at the measurement site are extracted as a pulse wave signal. Based on the changes in the amplitude of the pulse wave signal (mainly the rise and fall) associated with the change in cuff pressure at that time, the systolic blood pressure (maximum blood pressure) and diastolic blood pressure (minimum blood pressure) are calculated.

[0042] The display 50 displays various information, including blood pressure measurement results and atrial fibrillation detection results, based on control signals from the processor 110. The communication interface 53 exchanges various information with external devices. The power supply unit 54 supplies power to the processor 110 and each piece of hardware.

[0043] The control unit 52 inputs an operation signal to the processor 110 in response to user instructions. For example, the control unit 52 includes a measurement switch 52A for receiving a user instruction to start blood pressure measurement.

[0044] (Functional Configuration) Figure 3 is a block diagram showing the functional configuration of the blood pressure monitor 100. Referring to Figure 3, the blood pressure monitor 100 includes, as its main functional configuration, a blood pressure measurement unit 210, a pulse wave rate measurement unit 220, an interval calculation unit 230, a clustering unit 240, a determination unit 250, and an output control unit 260. Each of these functions is realized, for example, by the processor 110 of the blood pressure monitor 100 executing a program stored in the memory 51. Note that some or all of these functions may be configured to be realized in hardware.

[0045] The blood pressure measurement unit 210 controls the cuff pressure according to a measurement start instruction from the user via the operation unit 52 (for example, pressing the measurement switch 52A). Specifically, the blood pressure measurement unit 210 controls the pump 32 via the pump drive circuit 320 and the valve 33 via the valve drive circuit 330. The valve 33 is opened and closed to control the cuff pressure by discharging or sealing air in the fluid bag 22.

[0046] The blood pressure measurement unit 210 receives the cuff pressure signal detected by the pressure sensor 31 and extracts a pulse wave signal representing the pulse wave of the area being measured, which is superimposed on the cuff pressure signal. In other words, the blood pressure measurement unit 210 detects the pulse wave, which is the pressure component superimposed on the cuff pressure signal in synchronization with the user's heartbeat, from the cuff pressure signal.

[0047] The blood pressure measurement unit 210 measures the user's blood pressure based on a pulse wave signal superimposed on a cuff pressure signal detected during the process of increasing or decreasing cuff pressure. Specifically, the blood pressure measurement unit 210 measures the user's blood pressure using either an oscillometric method, either an increasing or decreasing measurement method. For example, if a decreasing measurement method is employed, in which the pulse wave is detected when the fluid bag 22 is depressurized, the blood pressure measurement unit 210 calculates the systolic blood pressure based on the cuff pressure when the amplitude of the pulse wave signal rapidly increases (at the rise) and the diastolic blood pressure based on the cuff pressure when it rapidly decreases (at the fall). The blood pressure measurement unit 210 may also employ a so-called increasing measurement method, in which the pulse wave is detected when the fluid bag 22 is pressurized.

[0048] The pulse wave frequency measurement unit 220 measures the user's pulse wave frequency N based on the pulse wave signal obtained when blood pressure is measured by the blood pressure measurement unit 210. Specifically, when blood pressure is measured using the pressurization method, the pulse wave frequency measurement unit 220 measures the pulse wave frequency N based on the pulse wave signal during the cuff pressure pressurization process. When blood pressure is measured using the depressurization method, the pulse wave frequency measurement unit 220 measures the pulse wave frequency N based on the pulse wave signal during the cuff pressure depressurization process.

[0049] The interval calculation unit 230 calculates a data set of pulse wave intervals based on the pulse wave signal. Specifically, when blood pressure is measured using the pressurization method, the interval calculation unit 230 calculates a data set of pulse wave intervals indicated by the pulse wave signal based on the pulse wave signal during the cuff pressure pressurization process. When blood pressure is measured using the depressurization method, the interval calculation unit 230 calculates a data set of pulse wave intervals indicated by the pulse wave signal based on the pulse wave signal during the cuff pressure depressurization process. For example, if the pulse wave signal Pa in Figure 1 is obtained, the data set of pulse wave intervals is pulse wave intervals ta1 to ta5.

[0050] The clustering unit 240 clusters the pulse wave interval data set into one or more clusters using a threshold Th. Specifically, the clustering unit 240 sorts the pulse wave interval data set in ascending or descending order, and clusters the pulse wave interval data set by comparing the difference between the preceding and succeeding data (pulse wave interval) with the threshold Th, thereby generating one or more clusters.

[0051] Furthermore, the clustering unit 240 sets a threshold Th based on the pulse wave number N. Specifically, the clustering unit 240 increases the threshold Th as the pulse wave number N decreases. However, the threshold Th is set to be less than or equal to the average value of the pulse interval data set. Details of the clustering method for the pulse interval data set will be described later.

[0052] The determination unit 250 receives cluster information input from the clustering unit 240. The cluster information includes the number of clusters, information indicating the data groups belonging to each cluster, etc. Based on the cluster information, the determination unit 250 calculates a variability index value for the data groups belonging to the clusters, and determines whether or not atrial fibrillation has occurred in the user based on the variability index value.

[0053] In a certain scenario, when a group of pulse wave interval data is clustered into a single cluster, the determination unit 250 determines that atrial fibrillation has occurred in the user if the variability index value of the data group belonging to that single cluster is greater than or equal to a predetermined value.

[0054] When there is only one cluster, one of the following can be used as the variability index: standard deviation, variance, mean absolute deviation, or median absolute deviation. Specifically, the determination unit 250 calculates one of the following as the variability index for the data group belonging to that single cluster: standard deviation, variance, mean absolute deviation, or median absolute deviation.

[0055] In other situations, when the pulse wave interval data sets are clustered into multiple clusters, the determination unit 250 determines that atrial fibrillation has occurred in the user if the variability index value of the data sets belonging to the multiple clusters is greater than or equal to a predetermined value. Furthermore, if the variability index value is less than the predetermined value, the determination unit 250 determines that an arrhythmia other than atrial fibrillation has occurred in the user.

[0056] When there are multiple clusters, one of the following can be used as the variability index: Cstd, "Clustered Variance" (also referred to as "CVar" for convenience), "Clustered Absolute Deviation" (also referred to as "CAD" for convenience), the mean of standard deviations, the mean of variances, the mean of mean absolute deviations, or the mean of median absolute deviations. Specifically, the judgment unit 250 calculates one of the following as the variability index for data sets belonging to multiple clusters: Cstd, CVar, CAD, the mean of standard deviations, the mean of variances, the mean of mean absolute deviations, or the mean of median absolute deviations.

[0057] Assume that Cstd is used as the variability index. In this case, the determination unit 250 calculates the sum of squared deviations of the data groups belonging to each cluster, and calculates Cstd as the square root of the value obtained by dividing the sum of each sum of squared deviations by the total number of data points in the pulse wave interval. More specifically, let N be the total number of data points in the pulse wave interval, m be the number of clusters, and x be the average value of the data groups within each cluster. av n is the number of data points in the data group, and x is the number of data points included in the data group. i (where i=1 to n), the sum of squared deviations of the data set is S. k (However, k = 1 to m). In this case, Cstd is calculated using the following equations (1) and (2).

[0058]

number

[0059] Assume that CVar is used as the variability index. In this case, the determination unit 250 calculates the sum of squared deviations of the data groups belonging to each cluster, and divides the sum of each sum of squared deviations by the total number of pulse wave interval data to obtain the value C V It is calculated as ar. More specifically, C V ar is calculated using equation (1) and equation (3) below.

[0060]

number

[0061] Assume that CAD is used as the variability index. In this case, the determination unit 250 calculates the sum of absolute deviations of the data groups belonging to each cluster, and calculates the value obtained by dividing the sum of each absolute deviation by the total number of pulse wave interval data. More specifically, the sum of absolute deviations of the data groups within a cluster is calculated as T k (However, assuming k = 1 to m), the CAD is calculated using the following equations (4) and (5). Note that other variables (for example, x) av The same applies to the variables used in equation (1).

[0062]

number

[0063] The mean of standard deviation, mean of variance, mean of mean absolute deviation, and mean of median absolute deviation, which are used as variability indicators, are calculated as follows. Specifically, the determination unit 250 calculates the standard deviation of the data group belonging to each cluster and calculates the "mean of standard deviation" by dividing the sum of each standard deviation by the number of clusters m. The determination unit 250 also calculates the variance of the data group belonging to each cluster and calculates the "mean of variance" by dividing the sum of each variance by the number of clusters m.

[0064] The determination unit 250 calculates the mean absolute deviation (the sum of absolute deviations divided by the number of data points n) of the data groups belonging to each cluster, and calculates the "average value of mean absolute deviations" by dividing the sum of each mean absolute deviation by the number of clusters m. The determination unit 250 also calculates the median absolute deviation of the data groups belonging to each cluster, and calculates the "average value of median absolute deviations" by dividing the sum of each median absolute deviation by the number of clusters m. Details of the atrial fibrillation determination method will be described later.

[0065] The output control unit 260 displays the measurement results from the blood pressure measurement unit 210 (e.g., systolic blood pressure and diastolic blood pressure values) and the determination results from the determination unit 250 (e.g., the determination result of whether or not atrial fibrillation has occurred) on the display 50. The output control unit 260 may also transmit the measurement results and determination results to an external device via the communication interface 53, or it may be configured to output audio via a speaker (not shown).

[0066] (Clustering and atrial fibrillation diagnosis) Figure 4 shows an example of a data set of pulse wave intervals. Specifically, Figure 4(a) is an example of a data set of pulse wave intervals in a pulse wave signal indicating atrial fibrillation. Figure 4(b) is an example of a data set of pulse wave intervals in a pulse wave signal indicating normal sinus rhythm. Figure 4(c) is an example of a data set of pulse wave intervals in a pulse wave signal indicating premature contractions. In Figures 4(a) to 4(c), the vertical axis shows the normalized value of the pulse wave interval, and the horizontal axis shows the order in which the pulses occurred.

[0067] The values ​​shown on the vertical axis in Figure 4 are normalized values ​​obtained by dividing the value of each pulse wave interval included in the pulse wave interval data set by the average value of each pulse wave interval. Therefore, if the pulse wave interval T is the same as the average value, the normalized value of the pulse wave interval T will be "1".

[0068] Referring to Figure 4(a), the pulse wave interval data set is irregularly scattered between approximately 0.6 and 1.5. This shows a similar trend to the pulse wave interval data set ta1 to ta5 in the pulse wave signal Pa in Figure 1.

[0069] Referring to Figure 4(b), there is no variation in the pulse wave interval data set, and each data point is close to 1.0. This is a similar trend to the pulse wave interval data set tb1 to tb5 in the pulse wave signal Pb in Figure 1.

[0070] Referring to Figure 4(c), some data points in the pulse wave interval data set exhibit variability. Specifically, while many data points are close to 1.0, some data points (e.g., the 2nd, 3rd, 15th, 16th, 22nd, and 23rd data points) have slightly different values. This trend is similar to that of the pulse wave interval data sets tc1 to tc5 in the pulse wave signal Pc in Figure 1.

[0071] The blood pressure monitor 100 (clustering unit 240) clusters the pulse wave interval data set shown in Figure 4 to generate one or more clusters. First, the clustering method will be explained using Figure 5.

[0072] Figure 5 is a diagram illustrating the clustering method. Referring to Figure 5, we can imagine a scenario where data sets D1 to D15 are clustered. Data sets D1 to D15 are data sets of pulse wave intervals in the pulse wave signal, sorted in descending order. That is, data D1 is the largest, and data D15 is the smallest.

[0073] Clustering is performed by comparing the difference between each data point and a threshold Th. For example, the blood pressure monitor 100 (e.g., the clustering unit 240) determines whether the difference between data D1 and the subsequent (adjacent) data D2 is greater than or equal to the threshold Th. Since the difference is greater than or equal to the threshold Th, data D1 is classified into a different cluster from data D2. In the example in Figure 5, data D1 belongs to cluster C1.

[0074] Similarly, the clustering unit 240 classifies data D2 and data D3 into the same cluster because the difference between them is less than the threshold Th. Subsequently, the clustering unit 240 classifies data D3 into a different cluster from data D4 because the difference between data D3 and data D4 is greater than or equal to the threshold Th. Therefore, in the example in Figure 5, data D2 and data D3 belong to cluster C2.

[0075] By repeating the above process, data D1 belongs to cluster C1, data D2 and D3 belong to cluster C2, data D4 to D6 belong to cluster C3, and data D7 to D15 belong to cluster C4. In the example in Figure 5, the clustering unit 240 clusters the pulse wave interval data group D1 to D15 into four clusters C1 to C4 using a threshold Th.

[0076] Figure 6 shows the clustering results. Specifically, it shows the results of clustering each of the pulse wave interval data sets shown in Figures 4(a) to 4(c) using a threshold Th. Using the clustering method explained in Figure 5, the pulse wave interval data set related to atrial fibrillation shown in Figure 4(a) is clustered into one cluster X1. The pulse wave interval data set related to normal sinus rhythm shown in Figure 4(b) is clustered into one cluster Y1. The pulse wave interval data set related to premature contractions shown in Figure 4(c) is clustered into three clusters Z1 to Z3.

[0077] The pulse interval data for atrial fibrillation is clustered into one cluster X1, and therefore, one of the four index values ​​(i.e., standard deviation, variance, mean absolute deviation, and median absolute deviation) is used as the variability index Sdx for this data group. As shown in Figure 6, the data group belonging to cluster X1 has high variability, so the variability index Sdx for this data group is large. Similarly, the pulse interval data for normal sinus rhythm is clustered into one cluster Y1, and therefore, one of the four index values ​​mentioned above is used as the variability index Sdy for this data group. However, the same type of index value is used for the variability index values ​​Sdx and Sdy. As shown in Figure 6, the data group belonging to cluster Y1 has low variability, so the variability index Sdy for this data group is small.

[0078] The data set of pulse wave intervals corresponding to premature contractions is clustered into three clusters, Z1 to Z3. Therefore, the variability index Sdz for this data set can be one of the following: Cstd, CVar, CAD, mean standard deviation, mean variance, mean absolute deviation, or mean absolute deviation. As shown in Figure 6, the data sets belonging to each cluster, Z1 to Z3, have low variability. Consequently, the variability index Sdz for the data set of pulse wave intervals corresponding to premature contractions is small.

[0079] Therefore, if atrial fibrillation is occurring, the variability index value calculated using the above method will be large. Accordingly, the blood pressure monitor 100 (determination unit 250) calculates the variability index value of the data group belonging to the clustered cluster, and if the variability index value is greater than a predetermined value, it determines that atrial fibrillation has occurred.

[0080] Furthermore, if the pulse wave interval data set is clustered into multiple clusters, the blood pressure monitor 100 (determination unit 250) determines that atrial fibrillation has occurred if the variability index value of the data sets belonging to the multiple clusters is greater than or equal to a predetermined value. This is because, although multiple clusters are generated, the variability of the data sets belonging to each cluster is large, and the pulse wave interval data set as a whole is considered to be irregularly variable (i.e., the variability index value is large).

[0081] On the other hand, if the variability index value of data sets belonging to multiple clusters is less than a predetermined value, the blood pressure monitor 100 (judgment unit 250) determines that an arrhythmia other than atrial fibrillation (for example, a premature contraction) has occurred.

[0082] Furthermore, if the pulse wave interval data sets are clustered into a single cluster, and the variability index value of the data sets belonging to that cluster is less than a predetermined value, the blood pressure monitor 100 (determination unit 250) may determine that the user's pulse is normal (for example, showing normal sinus rhythm).

[0083] As described above, in order to properly determine the presence or absence of atrial fibrillation, it is necessary to perform clustering using an appropriate threshold Th. The method for setting the threshold Th is described below.

[0084] Figure 7 is a diagram illustrating the threshold setting method. Both Figure 7(a) and Figure 7(b) show the results of clustering pulse wave interval data related to atrial fibrillation using the threshold Th. However, Figure 7(a) shows the clustering results when the pulse wave number is high, and Figure 7(b) shows the clustering results when the pulse wave number is low.

[0085] In Figure 7(a), one cluster Ca is generated, and the pulse wave interval data belonging to cluster Ca are highly variable. Therefore, the variability index values ​​(e.g., standard deviation, variance, mean absolute deviation, or median absolute deviation) for this data group are large. As a result, atrial fibrillation is correctly detected.

[0086] On the other hand, in Figure 7(b), three clusters Cb1 to Cb3 are generated, and the variability of the pulse wave interval data sets belonging to each of the clusters Cb1 to Cb3 is small. Therefore, the variability index values ​​of the data sets in multiple clusters Cb1 to Cb3 (e.g., Cstd, CVar, CAD, mean standard deviation, mean variance, mean mean absolute deviation, or mean median absolute deviation) are also small. As a result, arrhythmias other than atrial fibrillation may be misidentified.

[0087] To prevent the misdiagnosis described above, the blood pressure monitor 100 (clustering unit 240) changes the threshold Th according to the pulse wave rate. Specifically, the clustering unit 240 increases the threshold Th as the pulse wave rate decreases. With this configuration, as shown in Figure 7(b), when the pulse wave rate is low, the threshold Th becomes large, so the clustering unit 240 generates one cluster Cb instead of three clusters Cb1 to Cb3. The data group of pulse wave intervals belonging to cluster Cb is highly variable, so the variability index value of that data group becomes large. Therefore, it is correctly determined that atrial fibrillation has occurred.

[0088] Furthermore, using Figure 8, the upper limit of the threshold Th can be considered as follows.

[0089] Figure 8 is a diagram illustrating the upper limit of the threshold. Referring to Figure 8, an example is shown in the pulse wave signal Pd where one pulse is missing. As mentioned above, if the pulse wave interval is the same as the average value of the pulse wave interval data set, the normalized value of that pulse wave interval will be "1". In the example in Figure 8, the pulse wave intervals Ta,Tc are "1". Next, when one pulse is missing, the pulse wave intervals Tb before and after it become "2". Therefore, the difference between adjacent pulse wave intervals is "1" (i.e., 2-1=1), which matches the average value of the pulse wave interval data set.

[0090] The phenomenon of skipped beats is frequently observed in arrhythmias other than atrial fibrillation (e.g., premature contractions). Therefore, if the threshold Th is set to "1" or higher, there is a possibility that the data group related to premature contractions will be clustered as a single cluster instead of multiple clusters. In this case, there is a possibility of misidentifying arrhythmias other than atrial fibrillation as "atrial fibrillation." For this reason, the upper limit of the threshold Th is set to the average value of the pulse wave interval data group.

[0091] (Processing procedure) Figure 9 is a flowchart illustrating the processing procedure performed by the blood pressure monitor 100. Referring to Figure 9, at the start of this process, the cuff 20 is attached to the user's measurement site.

[0092] Referring to Figure 9, the processor 110 of the blood pressure monitor 100 receives an operation signal from the operation unit 52 based on user operation of the measurement switch 52A (step S10). In response to the operation signal, the processor 110 starts the blood pressure measurement process (step S20). In the blood pressure measurement process, a data set of blood pressure value, pulse wave number N, and pulse wave interval is calculated based on the pulse wave signal. Details of the blood pressure measurement process will be described later.

[0093] The processor 110 performs atrial fibrillation determination processing based on the pulse wave number N and pulse wave interval data set (step S30). Specifically, the processor 110 sets a threshold Th to be used for clustering based on the pulse wave number N, and clusters the pulse wave interval data set into one or more clusters using the threshold Th. Subsequently, the processor 110 determines whether or not atrial fibrillation has occurred based on the variability index value of the data set belonging to the cluster.

[0094] The processor 110 displays the blood pressure measurement results (e.g., systolic blood pressure and diastolic blood pressure) and the results of the atrial fibrillation detection process on the display 50 (step S40).

[0095] Figure 10 is a flowchart showing an example of the blood pressure measurement process of blood pressure monitor 100. The blood pressure measurement process shown in Figure 10 (corresponding to step S20 in Figure 9) is a process for measuring blood pressure using a pressurized measurement method.

[0096] Referring to Figure 10, the processor 110 of the blood pressure monitor 100 initializes the pressure sensor 31 (step S102). Specifically, the processor 110 initializes the processing memory area, turns off (stops) the pump 32, and with the valve 33 open, adjusts the pressure sensor 31 to 0 mmHg (sets atmospheric pressure to 0 mmHg).

[0097] Next, the processor 110 closes the valve 33 via the valve drive circuit 330 (step S104) and turns on the pump 32 via the pump drive circuit 320 to start pressurizing the cuff 20 (fluid bag 22) (step S106). At this time, the processor 110 supplies air to the fluid bag 22 from the pump 32 through the air piping and controls the rate at which the cuff pressure, which is the pressure inside the fluid bag 22, is pressurized based on the output of the pressure sensor 31. This starts the pressurization process.

[0098] Next, the processor 110 extracts a pulse wave signal from the cuff pressure signal detected by the pressure sensor 31, attempts to calculate the systolic blood pressure and diastolic blood pressure based on the pulse wave signal, and determines whether or not the blood pressure calculation is complete (step S108).

[0099] If blood pressure calculation cannot be completed due to insufficient data (NO in step S108), the processor 110 repeats the processes in steps S106 and S108, unless the cuff pressure has reached a predetermined upper pressure limit (e.g., 300 mmHg). If blood pressure calculation is completed (YES in step S108), the processor 110 stops the pump 32 (i.e., stops the pressurization process) (step S110), opens the valve 33 (step S112), and controls the exhaust of air from the cuff 20.

[0100] The processor 110 calculates a set of data for pulse wave number N and pulse wave interval based on the pulse wave signal obtained during the pressurization process (step S114). The processor 110 stores the calculated data for pulse wave number N and pulse wave interval in the memory 51.

[0101] Figure 11 is a flowchart showing another example of the blood pressure measurement process of blood pressure monitor 100. The blood pressure measurement process shown in Figure 11 (corresponding to step S20 in Figure 9) is a process for measuring blood pressure using a decompression measurement method.

[0102] Referring to Figure 11, the processes in steps S122 to S126 are the same as those in steps S102 to S106 in Figure 10, so a detailed explanation will not be provided.

[0103] The processor 110 estimates the systolic blood pressure based on the pulse wave signal obtained during pressurization (step S128). The processor 110 determines whether the cuff pressure has reached or exceeded pressure P (step S130). Typically, pressure P is set to a fixed value (e.g., 40 mmHg) higher than the estimated systolic blood pressure value.

[0104] If the cuff pressure is less than pressure P (NO in step S130), the processor 110 returns to step S126. If the cuff pressure is greater than or equal to pressure P (YES in step S130), the processor 110 stops the pump 32 (step S132) and controls the valve 33 to gradually open (step S134). This transitions the process from pressurization to depressurization (i.e., the depressurization process begins), and the cuff pressure gradually decreases.

[0105] During this decompression process, the processor 110 extracts a pulse wave signal from the cuff pressure signal detected by the pressure sensor 31, attempts to calculate the systolic and diastolic blood pressure based on the pulse wave signal, and determines whether the blood pressure calculation is complete (step S136). If the blood pressure calculation is not complete (NO in step S136), the processor 110 repeats the processes in steps S134 and S136. If the blood pressure calculation is complete (YES in step S136), the processor 110 controls the valve 33 to open fully (step S138) and rapidly exhaust the air from the cuff 20.

[0106] The processor 110 calculates a set of data for pulse wave number N and pulse wave interval based on the pulse wave signal obtained during the decompression process (step S140). The processor 110 stores the calculated data set for pulse wave number N and pulse wave interval in the memory 51.

[0107] <Other embodiments> (1) In the above-described embodiment, a program can be provided that causes the computer to function and execute the control described in the flowchart above. Such a program can be recorded on a non-temporary computer-readable recording medium such as a flexible disk, CD-ROM (Compact Disk Read Only Memory), secondary storage device, main memory, and memory card attached to the computer, and provided as a program product. Alternatively, the program can be recorded on a recording medium such as a hard disk built into the computer and provided. Furthermore, the program can be provided by download via a network.

[0108] (2) The configurations illustrated above as embodiments are examples of the present invention and can be combined with other known technologies, and can be modified, such as by omitting parts, without departing from the spirit of the present invention. Furthermore, in the embodiments described above, processes and configurations described in other embodiments may be appropriately adopted and implemented.

[0109] [Note] As described above, this embodiment includes the following disclosures.

[0110] [Configuration 1] The blood pressure measurement unit (210) measures the user's blood pressure based on a pulse wave signal superimposed on a cuff pressure signal detected during the process of increasing or decreasing cuff pressure, which indicates the internal pressure of a cuff (20) attached to the user's measurement site; a pulse wave number measurement unit (220) measures the user's pulse wave number based on the pulse wave signal; an interval calculation unit (230) calculates a data set of pulse wave intervals based on the pulse wave signal; a clustering unit (240) clusters the data set of pulse wave intervals into one or more clusters using a threshold; and a determination unit (250) determines whether or not atrial fibrillation has occurred in the user based on an index value indicating the magnitude of variability of the data sets belonging to the cluster. (240)A blood pressure monitor (100) sets the threshold based on the pulse wave frequency.

[0111] [Configuration 2] The clustering unit (240) increases the threshold as the pulse wave frequency decreases, as described in configuration 1, for the blood pressure monitor (100).

[0112] [Configuration 3] The blood pressure monitor (100) according to configuration 1 or 2, wherein the threshold is set to be less than or equal to the average value of the pulse wave interval data set.

[0113] [Structure 4] A blood pressure monitor (100) according to any one of configurations 1 to 3, wherein when the pulse wave interval data set is clustered into one cluster, the determination unit (250) determines that atrial fibrillation has occurred in the user if a first index value indicating the magnitude of variability of the data set belonging to the one cluster is greater than or equal to a predetermined value.

[0114] [Composition 5] A blood pressure monitor (100) according to any one of configurations 1 to 4, wherein when the pulse wave interval data set is clustered into multiple clusters, the determination unit (250) determines that atrial fibrillation has occurred in the user if a second index value indicating the magnitude of variation of the data sets belonging to the multiple clusters is greater than or equal to a predetermined value.

[0115] [Composition 6] The blood pressure monitor (100) according to configuration 5, wherein the determination unit (250) determines that an arrhythmia other than atrial fibrillation has occurred in the user when the second indicator value is less than the predetermined value.

[0116] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the foregoing description, and all modifications within the meaning and scope equivalent to the claims are intended to be included. [Explanation of symbols]

[0117] 10 Main unit, 20 Cuff, 22 Fluid bag, 30 Air system components, 31 Pressure sensor, 32 Pump, 33 Valve, 50 Display, 51 Memory, 52 Control panel, 52A Measurement switch, 53 Communication interface, 54 Power supply unit, 100 Blood pressure monitor, 110 Processor, 210 Blood pressure measurement unit, 220 Pulse wave rate measurement unit, 230 Interval calculation unit, 240 Clustering unit, 250 Judgment unit, 260 Output control unit, 310 A / D conversion circuit, 320 Pump drive circuit, 330 Valve drive circuit.

Claims

1. A blood pressure measurement unit measures the user's blood pressure based on a pulse wave signal superimposed on a cuff pressure signal detected during the process of increasing or decreasing the cuff pressure, which indicates the internal pressure of a cuff attached to the user's body part to be measured. A pulse wave frequency measuring unit measures the number of pulse waves of the user based on the pulse wave signal, An interval calculation unit calculates a data set of pulse wave intervals based on the pulse wave signal, A clustering unit that clusters the pulse wave interval data set into one or more clusters using a threshold, The system includes a determination unit that determines whether or not atrial fibrillation occurred in the user based on an index value indicating the magnitude of variation in the data group belonging to the cluster, The clustering unit sets the threshold based on the pulse wave frequency, A blood pressure monitor in which, when the pulse wave interval data set is clustered into multiple clusters, the determination unit determines that atrial fibrillation has occurred in the user if a second index value indicating the magnitude of variation of the data sets belonging to the multiple clusters is greater than or equal to a predetermined value.

2. The blood pressure monitor according to claim 1, wherein the clustering unit increases the threshold as the pulse wave frequency decreases.

3. The blood pressure monitor according to claim 1 or 2, wherein the threshold is set to be less than or equal to the average value of the pulse wave interval data set.

4. The blood pressure monitor according to claim 1 or 2, wherein when the pulse wave interval data set is clustered into one cluster, the determination unit determines that atrial fibrillation has occurred in the user if a first index value indicating the magnitude of variability of the data set belonging to the one cluster is greater than or equal to a predetermined value.

5. The blood pressure monitor according to claim 1, wherein the determination unit determines that an arrhythmia other than atrial fibrillation has occurred in the user if the second index value is less than the predetermined value.

6. A blood pressure measuring unit that measures the user's blood pressure based on a pulse wave signal superimposed on a cuff pressure signal detected during the process of increasing or decreasing the cuff pressure, which indicates the internal pressure of a cuff attached to the user's body part to be measured, A pulse wave frequency measuring unit measures the number of pulse waves of the user based on the pulse wave signal, An interval calculation unit calculates a data set of pulse wave intervals based on the pulse wave signal, A clustering unit sorts the pulse wave interval data set in ascending or descending order, and then, in the sorted pulse wave interval data set, compares the difference between two adjacent pulse wave intervals with a threshold value to cluster the pulse wave interval data set into one or more clusters. The system includes a determination unit that determines whether or not atrial fibrillation occurred in the user based on an index value indicating the magnitude of variation in the data group belonging to the cluster, The clustering unit is a blood pressure monitor that sets the threshold based on the pulse wave frequency.

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