Analysis system, analysis device, and analysis program

Abstract

This analysis system comprises an inertial sensor and an analysis device. The inertial sensor detects an inertial value indicating the movement of body surface caused by the movement of the heart of a subject. This analysis device is capable of acquiring an inertial value from the inertial sensor. The analysis device acquires time-series data of the inertial value (S11). The analysis device extracts, from the time-series data of the inertial value, time-series data of the inertial value in a specific period including a period in which an atrium in the heart contracts (S20-S23). The analysis device analyzes the contractility of the atrium on the basis of the time-series data of the inertial value in the specific period (S31-S35).

Description

Analysis system, analysis apparatus, and analysis program

[0001] The present disclosure relates to an analysis system, an analysis apparatus, and an analysis program.

[0002] The analysis system of Patent Document 1 includes an electrocardiogram sensor and an analysis apparatus. The electrocardiogram sensor detects an electrical signal relating to the movement of a subject's heart. The analysis apparatus acquires the electrical signal from the electrocardiogram sensor. The analysis apparatus determines whether arrhythmia occurs in the subject's heart based on the electrical signal.

[0003] Japanese Unexamined Patent Application Publication No. 2014-150826

[0004] In an analysis system like that of Patent Document 1, the state of the subject's heart is analyzed based on an electrical signal relating to the movement of the subject's heart, that is, based on an electrical signal obtained before an actual heart movement occurs. Here, the movement of the heart estimated from the electrical signal may differ from the actual movement of the heart. As a result, there is a risk that an analysis system like that of Patent Document 1 cannot appropriately analyze the state of the heart.

[0005] An analysis system for solving the above problem comprises: an inertial sensor that detects an inertial value, which is a value indicating movement of a body surface caused by movement of a subject's heart; and an analysis apparatus that acquires the inertial value from the inertial sensor, wherein the analysis apparatus executes: acquisition processing for acquiring time-series data of the inertial value; extraction processing for extracting, from the time-series data of the inertial value, time-series data of the inertial value in a specific period including a period during which the atrium of the heart contracts; and analysis processing for analyzing the contractility of the atrium based on the time-series data of the inertial value in the specific period.

[0006] An analysis apparatus for solving the above problem executes: acquisition processing for acquiring time-series data of an inertial value from an inertial sensor that detects the inertial value, which is a value indicating movement of a body surface caused by movement of a subject's heart; extraction processing for extracting, from the time-series data of the inertial value, time-series data of the inertial value in a specific period including a period during which the atrium of the heart contracts; and analysis processing for analyzing the contractility of the atrium based on the time-series data of the inertial value in the specific period.

[0007] The analysis program for solving the above problem is applied to an analysis system comprising an inertial sensor that detects an inertial value, which is a value indicating the movement of the body surface caused by the movement of the subject's heart, and an analysis device that acquires the inertial value from the inertial sensor. The analysis device is made to perform an acquisition process to acquire time-series data of the inertial value, an extraction process to extract time-series data of the inertial value for a specific period that includes the period in which the atria of the heart contract, from the time-series data of the inertial value, and an analysis process to analyze the contractility of the atria based on the time-series data of the inertial value for the specific period.

[0008] The analysis program for solving the above problem causes the analysis device to perform the following processes: an acquisition process to acquire time-series data of inertia values ​​from an inertia sensor that detects inertia values, which are values ​​indicating the movement of the body surface caused by the movement of the subject's heart; an extraction process to extract time-series data of inertia values ​​from the time-series data of inertia values ​​for a specific period that includes the period in which the atria of the heart contract; and an analysis process to analyze the contractile capacity of the atria based on the time-series data of inertia values ​​for the specific period.

[0009] According to the above configuration, the contractile capacity of the atrial region of the heart can be appropriately analyzed based on inertial values ​​corresponding to the actual movement of the heart.

[0010] Figure 1 is a schematic diagram of the analysis system according to the first embodiment. Figure 2 is an explanatory diagram of the preliminary steps for analysis control according to the first embodiment. Figure 3 is an explanatory diagram of the heart. Figure 4 is a time chart of acceleration corresponding to the movement of the heart according to the first embodiment. Figure 5 is a flowchart of the analysis control according to the first embodiment. Figure 6 is an explanatory diagram of the analysis process according to the first embodiment. Figure 7 is an explanatory diagram of the analysis process according to the first embodiment. Figure 8 is a schematic diagram of the analysis system according to the second embodiment. Figure 9 is an explanatory diagram of the preliminary steps for analysis control according to the second embodiment. Figure 10 is a flowchart of the analysis control according to the second embodiment. Figure 11(a) is a time chart showing the change in electrocardiogram signal according to the movement of the heart according to the second embodiment. Figure 11(b) is a time chart showing the change in acceleration according to the movement of the heart according to the second embodiment. Figure 12 is a schematic diagram of the analysis system according to a modified example. Figure 13 is a time chart of acceleration corresponding to the movement of the heart according to a modified example.

[0011] <First Embodiment> <Outline Configuration of the Analysis System> The first embodiment of this disclosure will be described below with reference to Figures 1 to 7. First, the outline configuration of the analysis system SA will be described. The analysis system SA is a system for analyzing the movement of a subject's heart.

[0012] As shown in Figure 1, the analysis system SA includes an acceleration sensor 10, an acquisition device 60, a calculation device 70, an input device 81, and a display 82. The acceleration sensor 10 detects acceleration GA, which is a value indicating the movement of the body surface caused by the movement of the subject's heart. In this embodiment, the acceleration sensor 10 is an example of an inertial sensor. Also, acceleration GA is an example of an inertial value. Here, the body surface movement detected by the inertial sensor is vibration and shock that appear as body surface movement due to the movement of the heart wall, the movement of the heart valves, and the movement of blood flowing inside the heart.

[0013] As shown in Figure 1, the acquisition device 60 can communicate with the acceleration sensor 10. In this embodiment, the acquisition device 60 can communicate with the acceleration sensor 10 via a cable (not shown). Therefore, the acquisition device 60 can acquire information from the acceleration sensor 10.

[0014] The acquisition device 60 comprises an execution device 61 and a storage device 62. An example of the execution device 61 is a CPU. The storage device 62 includes a read-only ROM, a read and write volatile RAM, and a read and write non-volatile storage. The storage device 62 pre-stores various programs and various data. The execution device 61 performs various processes by executing the programs stored in the storage device 62.

[0015] As shown in Figure 1, the arithmetic unit 70 can communicate with the acquisition device 60. The arithmetic unit 70 can communicate with the acquisition device 60 via a cable (not shown). The arithmetic unit 70 includes an execution device 71 and a storage device 72. An example of the execution device 71 is a CPU. The storage device 72 includes a read-only ROM, a read and write volatile RAM, and a read and write non-volatile storage. The storage device 72 pre-stores various programs and various data. Specifically, the storage device 72 pre-stores an analysis program 72A as one of the various programs. The execution device 71 executes various processes described later by executing the analysis program 72A stored in the storage device 72. The storage device 72 also pre-stores specified data DR as one of the various data. Specified data DR is data in which various thresholds used in the analysis control described later are predetermined. The execution device 71 can acquire various information from the acquisition device 60. In other words, the execution device 71 can acquire acceleration GA from the acceleration sensor 10 via the acquisition device 60. The execution device 71 also stores various information acquired via the acquisition device 60 in the storage device 72. An example of the arithmetic unit 70 is a so-called personal computer. In this embodiment, the arithmetic unit 70 is an example of an analysis device.

[0016] As shown in Figure 1, the input device 81 can communicate with the arithmetic unit 70 via a cable (not shown). The input device 81 is a device for inputting various types of information to the arithmetic unit 70. For example, the input device 81 may be a keyboard or a pointing device. The display 82 can also communicate with the arithmetic unit 70 via a cable (not shown). The display 82 is a device for displaying image data output from the arithmetic unit 70.

[0017] <Pre-procedure for analysis control> Next, referring to Figure 2, the pre-procedure for executing the analysis control described later will be explained. As shown in Figure 2, first, when executing the analysis control, the user of the analysis system SA places the acceleration sensor 10 in contact with the surface of the subject's body while the subject is lying on their back. Specifically, the user of the analysis system SA places the acceleration sensor 10 near the apex of the subject's heart on the surface of the subject's body. In this state, the computing unit 70 acquires the acceleration GA from the acceleration sensor 10 via the acquisition device 60. Here, an example of a user of the analysis system SA is a medical professional such as a doctor. Note that the up-down and left-right directions shown in Figures 2 and 3 are directions relative to the subject's body.

[0018] <About the structure and movement of the heart> Next, with reference to Figures 3 and 4, the structure and movement of the human heart H will be explained. As shown in Figure 3, the heart H comprises the right atrium H1A, right ventricle H2A, left atrium H3A, and left ventricle H4A. The heart H also comprises the tricuspid valve H1B, pulmonary valve H2B, mitral valve H3B, aortic valve H4B, and apex H5. When the heart H is functioning normally, blood that has flowed through the various parts of the subject's body flows into the right atrium H1A via the superior vena cava. The blood inside the right atrium H1A flows into the right ventricle H2A via the tricuspid valve H1B. The blood inside the right ventricle H2A flows into the lungs via the pulmonary valve H2B and pulmonary artery. The blood inside the lungs flows into the left atrium H3A via the pulmonary vein. The blood inside the left atrium H3A flows into the left ventricle H4A via the mitral valve H3B. Blood inside the left ventricle H4A flows to various parts of the subject's body via the aortic valve H4B and the aorta. The movement of the body surface caused by this movement of the heart H is easily detected in the part of the heart H located in the lower left of the subject, i.e., near the apex H5. Therefore, it is preferable for the user of the analysis system SA to place the acceleration sensor 10 near the apex H5 of the subject's heart H.

[0019] When the accelerometer 10 is positioned near the apex H5 of the subject's heart H and the heart H is functioning normally, the acceleration GA detected by the accelerometer 10 changes as follows. As shown in Figure 4, at time t10, specifically at the closure timing when the aortic valve H4B closes, the acceleration GA detected by the accelerometer 10 increases. Subsequently, from time t20 to time t30, specifically during the period from the start timing when the left atrium H3A begins to contract until the end timing when the left atrium H3A finishes contracting, the acceleration GA detected by the accelerometer 10 changes. At time t40, specifically at the opening timing when the aortic valve H4B opens, the acceleration GA detected by the accelerometer 10 increases. Here, the higher the velocity of the body surface movement caused by the movement of the subject's heart H, from posterior to anterior, the greater the acceleration GA detected by the accelerometer 10. In other words, the higher the velocity of the body surface movement caused by the movement of the subject's heart H, from the dorsal to the ventral side of the subject, the greater the acceleration GA detected by the acceleration sensor 10. In Figure 4, time t50 corresponds to the closure timing when the aortic valve H4B closes. In Figure 4, the period from time t60 to time t70 corresponds to the period from the start timing when the left atrial H3A begins to contract to the end timing when the left atrial H3A ends to contraction. In Figure 4, time t80 corresponds to the opening timing when the aortic valve H4B opens.

[0020] <Analysis Control> Next, with reference to Figure 5, the analysis control performed by the calculation unit 70 of the analysis system SA will be described. This analysis control is for analyzing the contractility of the left atrial H3A. In this embodiment, the execution unit 71 of the calculation unit 70 performs the analysis control when a user of the analysis system SA requests the execution of the analysis control via the input device 81.

[0021] As shown in Figure 5, when the execution device 71 of the arithmetic unit 70 starts analysis control, it executes the process in step S11. In step S11, the execution device 71 acquires acceleration GA data from the time of processing in step S11 up to a predetermined period prior to that time, i.e., time-series data of acceleration GA. Specifically, the execution device 71 acquires time-series data of acceleration GA by accessing the storage device 72. Here, the above predetermined period is predetermined to be, for example, a period longer than two cycles of heartbeats H. An example of a predetermined period is a few seconds to a dozen seconds. In step S11, the execution device 71 identifies the average value of all acquired acceleration GA as the reference zero for acceleration GA. That is, the sign of the acceleration GA value is determined based on the zero identified here. The process in step S11 is an example of the acquisition process. After step S11, the execution device 71 proceeds to step S20.

[0022] In step S20, the execution device 71 identifies the closing timing, which is the timing when the aortic valve H4B closes, and the opening timing, which is the timing when the aortic valve H4B opens, based on the time-series data of acceleration GA. The execution device 71 identifies them, for example, as follows: The execution device 71 identifies the timings when the acceleration GA has a maximum value, based on the time-series data of acceleration GA. Furthermore, the execution device 71 extracts the timings from the identified timings where the maximum value of acceleration GA at that timing is greater than or equal to a predetermined reference value. At this time, the execution device 71 extracts all timings that meet the above requirements. Here, the reference value is, for example, a value that is a certain amount smaller than the acceleration GA expected for the closing timing and the opening timing of the aortic valve H4B. Therefore, the timings extracted by the execution device 71 through this process include multiple closing timings and multiple opening timings for the aortic valve H4B. The reference value is predetermined through experiments and simulations, etc. Next, the execution device 71 identifies the minimum value of acceleration GA one timing point prior to the extracted timing point, based on the time-series data of acceleration GA. At this time, the execution device 71 identifies the minimum value of acceleration GA for each extracted timing point. Furthermore, the execution device 71 determines whether the absolute value of the difference between the maximum value of acceleration GA and the minimum value of acceleration GA corresponding to that maximum value is less than or equal to a predetermined reference change amount. In other words, the execution device 71 determines whether the amount of change in acceleration GA up to the target maximum value is less than or equal to a predetermined reference change amount. At this time, the execution device 71 performs the above determination for each extracted timing point. Here, the reference change amount is a value that is greater than the amount of change in acceleration GA expected when the aortic valve H4B closes, and smaller than the amount of change in acceleration GA expected when the aortic valve H4B opens. The reference change amount is predetermined through experiments and simulations, etc. The execution device 71 then identifies the earliest timing among the extracted timings for which the absolute value is determined to be less than or equal to the reference change amount as the closing timing of the aortic valve H4B.In other words, as shown in Figure 4, the execution device 71 identifies time t10 as the closing timing of the aortic valve H4B. Furthermore, the execution device 71 identifies the earliest timing among the extracted timings for which the absolute value exceeds the reference change amount that is later than the identified closing timing of the aortic valve H4B as the opening timing of the aortic valve H4B. In other words, as shown in Figure 4, the execution device 71 identifies time t40 as the opening timing of the aortic valve H4B. As shown in Figure 5, after step S20, the execution device 71 proceeds to step S21.

[0023] In step S21, the execution device 71 identifies a first timing TA based on the time-series data of acceleration GA. In this embodiment, the first timing TA is the timing after the aortic valve H4B has closed and satisfies a predetermined first condition indicating the end of the change in acceleration GA due to the closure. Specifically, the execution device 71 identifies the first timing TA as follows. First, the execution device 71 acquires time-series data of acceleration GA from the time of aortic valve H4B closure identified in step S20 to the time of aortic valve H4B opening. That is, as shown in Figure 4, the execution device 71 acquires time-series data of acceleration GA from time t10, which is the time of aortic valve H4B closure, to time t40, which is the time of aortic valve H4B opening. Next, as shown in Figure 4, the execution device 71 identifies the timing at which the acceleration GA becomes negative after the closure timing of the aortic valve H4B, and then changes from that negative value to zero, as the first timing TA from the acquired time-series data of acceleration GA. As shown in Figure 5, after step S21, the execution device 71 proceeds to step S22.

[0024] In step S22, the execution device 71 identifies a second timing TB based on the time-series data of acceleration GA. In this embodiment, the second timing TB is a timing that is before the aortic valve H4B opens and satisfies a predetermined second condition indicating the start of the change in acceleration GA due to the opening. Specifically, the execution device 71 identifies the second timing TB as follows. First, the execution device 71 acquires time-series data of acceleration GA from the time of closure of the aortic valve H4B identified in step S20 to the time of opening of the aortic valve H4B. That is, as shown in Figure 4, the execution device 71 acquires time-series data of acceleration GA from time t10, which is the time of closure of the aortic valve H4B, to time t40, which is the time of opening of the aortic valve H4B. Next, as shown in Figure 4, the execution device 71 identifies the second timing TB as the timing at which the acceleration GA becomes negative and then becomes zero in the time-series data of the acquired acceleration GA before the opening timing of the aortic valve H4B. As shown in Figure 5, after step S22, the execution device 71 proceeds to step S23.

[0025] In step S23, the execution device 71 extracts time-series data of acceleration GA from the time-series data of acceleration GA acquired in step S11, specifically for a specific period PS that includes the period during which the left atrial H3A contracts. Specifically, as shown in Figure 4, the execution device 71 identifies the period from the first timing TA to the second timing TB as the specific period PS. Then, the execution device 71 extracts time-series data of acceleration GA from the time-series data of acceleration GA acquired in step S11 for the specific period PS. In this embodiment, the processing in steps S20 to S23 is an example of the extraction process. As shown in Figure 5, after step S23, the execution device 71 proceeds to step S31.

[0026] In step S31, the execution device 71 identifies the amplitude AG of acceleration GA due to left atrial H3A contraction based on the time-series data of acceleration GA during a specific period PS. The execution device 71 identifies the amplitude AG of acceleration GA as follows, for example: First, the execution device 71 identifies the maximum value of acceleration GA during a specific period PS based on the time-series data of acceleration GA during a specific period PS. The execution device 71 also identifies the minimum value of acceleration GA during a specific period PS based on the time-series data of acceleration GA during a specific period PS. Then, the execution device 71 identifies the absolute value of the difference between the identified maximum and minimum values ​​as the amplitude AG of acceleration GA. As shown in Figure 5, after step S31, the execution device 71 proceeds to step S32.

[0027] In step S32, the execution device 71 identifies the frequency FG of acceleration GA due to left atrial H3A contraction based on the time-series data of acceleration GA during a specific period PS. The execution device 71 identifies the frequency FG of acceleration GA as follows, for example. First, as shown in Figure 6, the execution device 71 generates a graph representing the energy of acceleration GA for each unit frequency, a so-called power spectrum, by performing a Fast Fourier Transform on the time-series data of acceleration GA during a specific period PS. Then, the execution device 71 identifies the frequency with the largest energy in the generated power spectrum as the frequency FG of acceleration GA. As shown in Figure 5, after step S32, the execution device 71 proceeds to step S33.

[0028] In step S33, the execution device 71 identifies the integral value IG of the change in acceleration GA due to left atrial H3A contraction, based on the time-series data of acceleration GA during the specified period PS. The execution device 71 identifies the integral value IG of acceleration GA as follows, for example. First, the execution device 71 identifies the maximum value of acceleration GA during the specified period PS, based on the time-series data of acceleration GA during the specified period PS. Furthermore, the execution device 71 identifies the timing when acceleration GA is zero before the timing of the identified maximum value of acceleration GA, and the timing when acceleration GA is zero after the timing of the identified maximum value of acceleration GA. Then, the execution device 71 calculates the first integral value IGA, which is the value obtained by integrating the absolute value of acceleration GA from after the identified former timing to before the identified latter timing. That is, as shown in Figure 7, the first integral value IGA can be said to represent the area when acceleration GA is large. The execution device 71 also identifies the minimum value of acceleration GA during the specified period PS, based on the time-series data of acceleration GA during the specified period PS. Furthermore, the execution device 71 identifies the timing when acceleration GA is zero before the timing of the specified minimum value of acceleration GA, and the timing when acceleration GA is zero after the timing of the specified minimum value of acceleration GA. The execution device 71 then calculates the second integral value IGB, which is the integral of the absolute value of acceleration GA from after the specified former timing to before the specified latter timing. That is, as shown in Figure 7, the second integral value IGB can be said to represent the area when acceleration GA is small. The execution device 71 then identifies the sum of the first integral value IGA and the second integral value IGB as the integral value IG for the change in acceleration GA due to the contraction of the left atrial H3A. As shown in Figure 5, after step S33, the execution device 71 proceeds to step S34.

[0029] In step S34, the execution device 71 identifies the differential value DG of the change in acceleration GA due to the contraction of the left atrial H3A, based on the time-series data of acceleration GA during the specified period PS. The execution device 71 identifies the differential value DG of acceleration GA as follows, for example. First, the execution device 71 calculates the differential value of acceleration GA at each point in time based on the time-series data of acceleration GA during the specified period PS. The differential value of acceleration GA here is sometimes called jerk. Next, the execution device 71 identifies the maximum value among the absolute values ​​of the calculated differential values. Then, the execution device 71 identifies the identified maximum value as the differential value DG of the change in acceleration GA due to the contraction of the left atrial H3A. That is, the differential value DG can be said to be the maximum value of the change in acceleration GA during the specified period PS. After step S34, the execution device 71 proceeds to step S35.

[0030] In step S35, the execution device 71 determines whether the contractility of the left atrial H3A is normal by comparing the various values ​​identified in steps S31 to S34 with various threshold values ​​predetermined in the specified data DR. In other words, the execution device 71 determines whether there is an abnormality in the contractility of the left atrial H3A by comparing the various values ​​identified in steps S31 to S34 with various threshold values ​​predetermined in the specified data DR. The execution device 71 makes the determination as follows, for example. First, the execution device 71 compares the amplitude AG of the acceleration GA due to the contraction of the left atrial H3A identified in step S31 with a predetermined amplitude predetermined in the specified data DR. The execution device 71 compares the frequency FG of the acceleration GA due to the contraction of the left atrial H3A identified in step S32 with a predetermined frequency predetermined in the specified data DR. The execution device 71 compares the integral value IG of the change in acceleration GA due to left atrial H3A contraction, which was identified in step S33, with a predetermined integral value set in the predetermined data DR. The execution device 71 compares the differential value DG of the change in acceleration GA due to left atrial H3A contraction, which was identified in step S34, with a predetermined differential value set in the predetermined data DR. The execution device 71 then determines that the contractility of the left atrial H3A is normal if all of the following conditions (1) to (4) are met. On the other hand, the execution device 71 determines that the contractility of the left atrial H3A is abnormal if one or more of the following conditions (1) to (4) are not met.

[0031] Condition (1): The amplitude AG of the acceleration GA due to the contraction of the left atrial H3A identified in step S31 is equal to or greater than the specified amplitude predetermined in the specified data DR. Condition (2): The frequency FG of the acceleration GA due to the contraction of the left atrial H3A identified in step S32 is equal to or greater than the specified frequency predetermined in the specified data DR.

[0032] Condition (3): The integral value IG of the change in acceleration GA due to the contraction of left atrial H3A identified in step S33 is greater than or equal to the predetermined integral value set in the predetermined data DR. Condition (4): The differential value DG of the change in acceleration GA due to the contraction of left atrial H3A identified in step S34 is greater than or equal to the predetermined differential value set in the predetermined data DR.

[0033] Here, when the contractility of the left atrial H3A is normal, the amplitude AG of the acceleration GA due to left atrial H3A contraction, as identified in step S31, tends to be larger compared to when the contractility of the left atrial H3A is abnormal. When the contractility of the left atrial H3A is normal, the frequency FG of the acceleration GA due to left atrial H3A contraction, as identified in step S32, tends to be higher compared to when the contractility of the left atrial H3A is abnormal. When the contractility of the left atrial H3A is normal, the integral value IG of the change in acceleration GA due to left atrial H3A contraction, as identified in step S33, tends to be larger compared to when the contractility of the left atrial H3A is abnormal. When the contractility of the left atrial H3A is normal, the differential value DG of the change in acceleration GA due to left atrial H3A contraction, as identified in step S34, tends to be larger compared to when the contractility of the left atrial H3A is abnormal. The specified amplitude, specified frequency, specified integral value, and specified derivative value mentioned above are predetermined through experiments and simulations. In this embodiment, the processing in steps S31 to S35 is an example of an analysis process for analyzing the contractility of the left atrial H3A. If the execution device 71 determines in step S35 that the contractility of the left atrial H3A is normal (S35: YES), the execution device 71 proceeds to step S41.

[0034] In step S41, the execution device 71 notifies that the contractility of the left atrial H3A in the subject's heart H is normal. Specifically, the execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 that the contractility of the left atrial H3A in the subject's heart H is normal. The execution device 71 also notifies the results of the four comparisons in step S35. Specifically, the execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the amplitude AG of the acceleration GA due to the contraction of the left atrial H3A identified in step S31, and the predetermined amplitude in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the frequency FG of the acceleration GA due to the contraction of the left atrial H3A identified in step S32, and the predetermined frequency in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the integral value IG of the change in acceleration GA due to the contraction of the left atrial H3A identified in step S33, and a predetermined predetermined integral value in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the differential value DG of the change in acceleration GA due to the contraction of the left atrial H3A identified in step S34, and a predetermined predetermined differential value in the predetermined data DR. After step S41, the execution device 71 terminates the current analysis control.

[0035] On the other hand, if the execution device 71 determines in step S35 above that the contractility of the left atrial H3A is abnormal (S35: NO), the execution device 71 proceeds to step S42.

[0036] In step S42, the execution device 71 notifies that the contractility of the left atrial H3A in the subject's heart H is abnormal. Specifically, the execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 that the contractility of the left atrial H3A in the subject's heart H is abnormal. The execution device 71 also notifies the results of the four comparisons in step S35. Specifically, the execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the amplitude AG of the acceleration GA due to the contraction of the left atrial H3A identified in step S31, and a predetermined amplitude in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the frequency FG of the acceleration GA due to the contraction of the left atrial H3A identified in step S32, and a predetermined frequency in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the integral value IG of the change in acceleration GA due to the contraction of the left atrial H3A identified in step S33, and a predetermined integral value set in the predetermined data DR. The execution device 71 outputs a control signal to the display 82, thereby displaying on the display 82 the differential value DG of the change in acceleration GA due to the contraction of the left atrial H3A identified in step S34, and a predetermined differential value set in the predetermined data DR. In this embodiment, the processing in steps S41 and S42 is an example of output processing that outputs the result of whether or not there is an abnormality in the contractility of the left atrial H3A. After step S42, the execution device 71 terminates the current analysis control.

[0037] <Operation of this Embodiment> As shown in Figure 2, the user of the analysis system SA places the acceleration sensor 10 near the apex H5 of the heart H on the surface of the subject's body while the subject is lying on their back. In this state, the acceleration sensor 10 of the analysis system SA detects acceleration GA, which indicates the movement of the body surface caused by the movement of the subject's heart H. In other words, acceleration GA corresponding to the actual movement of the heart H is detected by the acceleration sensor 10.

[0038] As shown in Figure 5, in step S11, the execution device 71 of the calculation device 70 in the analysis system SA performs an acquisition process to acquire time-series data of acceleration GA. In steps S20 to S23, the execution device 71 performs an extraction process to extract time-series data of acceleration GA for a specific period PS that includes the period in which the left atrial H3A contracts, from the time-series data of acceleration GA acquired in step S11. In steps S31 to S35, the execution device 71 performs an analysis process to analyze the contractility of the left atrial H3A based on the time-series data of acceleration GA for the specific period PS.

[0039] <Effects of this embodiment> (1-1) According to this embodiment, acceleration GA corresponding to the actual movement of the heart H is detected by the acceleration sensor 10. Then, the execution device 71 of the calculation device 70 analyzes the contractility of the left atrial H3A based on the time-series data of acceleration GA in a specific period PS that includes the period in which the left atrial H3A contracts, from the time-series data of acceleration GA detected by the acceleration sensor 10.Therefore, according to this embodiment, the contractility of the left atrial H3A in the heart H can be appropriately analyzed based on acceleration GA corresponding to the actual movement of the heart H.

[0040] (1-2) As shown in Figure 5, in step S21, the execution device 71 identifies a first timing TA based on the time-series data of acceleration GA. Here, as shown in Figure 4, the first timing TA is the timing after the aortic valve H4B has closed and satisfies a predetermined first condition indicating the end of the change in acceleration GA due to the closure. As shown in Figure 5, in step S22, the execution device 71 identifies a second timing TB based on the time-series data of acceleration GA. Here, as shown in Figure 4, the second timing TB is the timing before the aortic valve H4B has opened and satisfies a predetermined second condition indicating the start of the change in acceleration GA due to the opening. As shown in Figure 5, in step S23, the execution device 71 defines the period from the first timing TA to the second timing TB as a specific period PS and extracts time-series data of acceleration GA during the specific period PS.

[0041] According to the above configuration, the period in which the acceleration GA changes due to the opening and closing of the aortic valve H4B is suppressed from being included in the specific period PS. As a result, it is possible to suppress a decrease in the analysis accuracy of the contractility of the left atrium H3A caused by the change in acceleration GA when the aortic valve H4B opens and closes.

[0042] (1-3) In the heart H, as the contractility of the left atrium H3A decreases, specifically, as the left atrium H3A hardens and becomes more difficult to contract, the amplitude AG of the acceleration GA generated by the contraction of the left atrium H3A tends to become smaller.

[0043] In this regard, as shown in FIG. 5, in step S31, the execution device 71 specifies the amplitude AG of the acceleration GA generated by the contraction of the left atrium H3A based on time-series data of the acceleration GA in the specific period PS. In step S35, the execution device 71 analyzes the contractility of the left atrium H3A by comparing the amplitude AG of the acceleration GA generated by the contraction of the left atrium H3A specified in step S31 with a prescribed amplitude predetermined in prescribed data DR. According to the above configuration, the contractility of the left atrium H3A can be analyzed more accurately based on the amplitude AG of the acceleration GA that correlates with the contractility of the left atrium H3A.

[0044] (1-4) In the heart H, as the contractility of the left atrium H3A decreases, specifically, as the left atrium H3A hardens and becomes more difficult to contract, the frequency FG of the acceleration GA generated by the contraction of the left atrium H3A tends to become lower.

[0045] In this regard, as shown in FIG. 5, in step S32, the execution device 71 specifies the frequency FG of the acceleration GA generated by the contraction of the left atrium H3A based on time-series data of the acceleration GA in the specific period PS. In step S35, the execution device 71 analyzes the contractility of the left atrium H3A by comparing the frequency FG of the acceleration GA generated by the contraction of the left atrium H3A specified in step S32 with a prescribed frequency predetermined in prescribed data DR. According to the above configuration, the contractility of the left atrium H3A can be analyzed more accurately based on the frequency FG of the acceleration GA that correlates with the contractility of the left atrium H3A.

[0046] (1-5) In the heart H, when the contractility of the left atrium H3A decreases, specifically, as the left atrium H3A hardens and becomes less likely to contract, the integrated value IG of the change in acceleration GA caused by the contraction of the left atrium H3A tends to decrease.

[0047] In this regard, as shown in FIG. 5, in step S33, the execution device 71 specifies the integrated value IG of the change in acceleration GA caused by the contraction of the left atrium H3A based on the time-series data of acceleration GA in the specific period PS. In step S35, the execution device 71 analyzes the contractility of the left atrium H3A by comparing the integrated value IG of the change in acceleration GA caused by the contraction of the left atrium H3A specified in step S33 with a predetermined defined integrated value stored in the defined data DR. According to the above configuration, the contractility of the left atrium H3A can be analyzed more accurately based on the integrated value IG of acceleration GA that correlates with the contractility of the left atrium H3A.

[0048] (1-6) In the heart H, when the contractility of the left atrium H3A decreases, specifically, as the left atrium H3A hardens and becomes less likely to contract, the differential value DG of the change in acceleration GA caused by the contraction of the left atrium H3A tends to decrease.

[0049] In this regard, as shown in FIG. 5, in step S34, the execution device 71 specifies the differential value DG of the change in acceleration GA caused by the contraction of the left atrium H3A based on the time-series data of acceleration GA in the specific period PS. In step S35, the execution device 71 analyzes the contractility of the left atrium H3A by comparing the differential value DG of the change in acceleration GA caused by the contraction of the left atrium H3A specified in step S34 with a predetermined defined differential value stored in the defined data DR. According to the above configuration, the contractility of the left atrium H3A can be analyzed more accurately based on the differential value DG of acceleration GA that correlates with the contractility of the left atrium H3A.

[0050] (1-7) In step S35, the execution device 71 determines whether or not there is an abnormality in the contractility of the left atrial H3A by comparing the various values ​​identified in steps S31 to S34 with various thresholds predetermined in the specified data DR. If the contractility of the left atrial H3A is normal, in step S41, the execution device 71 notifies that the contractility of the left atrial H3A in the subject's heart H is normal. On the other hand, if the contractility of the left atrial H3A is abnormal, in step S42, the execution device 71 notifies that the contractility of the left atrial H3A in the subject's heart H is abnormal. In other words, in steps S41 and S42, the execution device 71 performs output processing to output the result of whether or not there is an abnormality in the contractility of the left atrial H3A. With the above configuration, for example, even a person in charge who is not a doctor with advanced specialized knowledge can understand the state of the contractility of the left atrial H3A by checking the results output in steps S41 and S42.

[0051] <Second Embodiment> The second embodiment of the present disclosure will be described below with reference to Figures 8 to 11. In the second embodiment, some of the configurations of the analysis system SA differ from those of the first embodiment. Specifically, the analysis system SA of the second embodiment is equipped with an electrocardiogram sensor 20. In addition, in the second embodiment, some of the processing of the analysis control differs from those of the first embodiment. Specifically, in the analysis control of the second embodiment, the processing of step S211 is performed instead of the processing of step S11. Also, in the analysis control of the second embodiment, the processing of steps S221 to S223 is performed instead of the processing of steps S20 to S23. In the description of the second embodiment, the differences from the first embodiment will be the main focus, and components similar to those in the first embodiment will be denoted by the same reference numerals, and their descriptions will be omitted or simplified.

[0052] <Outline Configuration of the Analysis System> As shown in Figure 8, the electrocardiogram sensor 20 detects the electrocardiogram signal (ECG), which is an electrical signal of the subject's heart H. The electrocardiogram sensor 20 includes multiple electrodes attached to the subject's body surface. Here, the magnitude of the electrocardiogram signal (ECG) indicates the height of the electrical potential.

[0053] The execution unit 71 of the arithmetic unit 70 can acquire various types of information from the acquisition unit 60. Specifically, the execution unit 71 can acquire not only acceleration GA from the acceleration sensor 10 but also electrocardiogram signals (ECG) from the electrocardiogram sensor 20 via the acquisition unit 60. The execution unit 71 also stores the various types of information acquired via the acquisition unit 60 in the storage device 72. At this time, the execution unit 71 stores the information from the acceleration sensor 10 and the electrocardiogram sensor 20 in association with time information. Therefore, by accessing the storage device 72, the execution unit 71 can acquire, for example, information from the acceleration sensor 10 detected at a certain timing and information from the electrocardiogram sensor 20 detected at the same timing in association. In other words, the execution unit 71 can acquire time-series data of acceleration GA and time-series data of electrocardiogram signals (ECG) linked to the time-series data of acceleration GA.

[0054] <Pre-procedure for analysis control> Next, referring to Figure 9, the pre-procedure for executing the analysis control described later will be explained. As shown in Figure 9, first, when executing the analysis control, the user of the analysis system SA brings the acceleration sensor 10 into contact with the surface of the subject's body while the subject is lying on their back. At this time, the user of the analysis system SA places the acceleration sensor 10 near the apex H5 of the subject's heart H on the surface of the subject's body. The user of the analysis system SA also attaches multiple electrodes of the electrocardiogram sensor 20 to predetermined positions on the surface of the subject's body. In this state, the computing unit 70 acquires the acceleration GA from the acceleration sensor 10 and the electrocardiogram signal ECG from the electrocardiogram sensor 20 via the acquisition device 60. Here, an example of a user of the analysis system SA is a medical professional such as a doctor. Note that the up and down and left and right directions shown in Figure 9 are directions relative to the subject's body.

[0055] <Analysis Control> Next, with reference to Figure 10, the analysis control performed by the calculation unit 70 of the analysis system SA will be described. This analysis control is for analyzing the contractility of the left atrial H3A. In this embodiment, the execution unit 71 of the calculation unit 70 performs the analysis control when a user of the analysis system SA requests the execution of the analysis control via the input device 81.

[0056] As shown in Figure 10, when the execution device 71 of the arithmetic unit 70 starts analysis control, it executes the process in step S211. In step S211, the execution device 71 acquires acceleration GA data from the time of processing in step S211 up to a predetermined period prior, i.e., time-series data of acceleration GA. The execution device 71 also acquires electrocardiogram signal ECG data from the time of processing in step S211 up to a predetermined period prior, i.e., time-series data of electrocardiogram signal ECG. Specifically, the execution device 71 acquires time-series data of acceleration GA, as well as time-series data of electrocardiogram signal ECG associated with the time-series data of acceleration GA, by accessing the storage device 72. Here, the predetermined period is predetermined to be, for example, a period longer than two cycles of heartbeats. An example of a predetermined period is a few seconds to a dozen seconds. In step S211, the execution device 71 identifies the average value of all acquired acceleration GA as the reference zero for acceleration GA. In other words, the sign of the acceleration GA value is determined based on the zero identified here. The process in step S211 is an example of an acquisition process. After step S211, the execution device 71 proceeds to step S221.

[0057] In step S221, the execution device 71 identifies the first timing TA based on the time-series data of the electrocardiogram signal ECG. In this embodiment, the first timing TA is the timing that satisfies a predetermined first condition indicating the start of left atrial H3A contraction. Specifically, the execution device 71 identifies the first timing TA as follows. First, the execution device 71 identifies the timing of the so-called P wave based on the time-series data of the electrocardiogram signal ECG. At this time, the execution device 71 identifies the timing of all P waves included in the time-series data of the electrocardiogram signal ECG acquired in step S211. The timing of the P wave here is the timing at which the electrocardiogram signal ECG is at its greatest among the target P waves. Then, as shown in Figure 11(a), the execution device 71 identifies the earliest timing among the multiple P wave timings as the first timing TA. As shown in Figure 10, after step S221, the execution device 71 proceeds to step S222.

[0058] In step S222, the execution device 71 identifies the second timing TB based on the time-series data of the electrocardiogram signal ECG. In this embodiment, the second timing TB is the timing that satisfies a predetermined second condition indicating the end of left atrial H3A contraction. Specifically, the execution device 71 identifies the second timing TB as follows. First, the execution device 71 identifies the timing of the so-called R wave based on the time-series data of the electrocardiogram signal ECG. At this time, the execution device 71 identifies the timing of all R waves included in the time-series data of the electrocardiogram signal ECG acquired in step S211. The timing of the R wave here is the timing when the electrocardiogram signal ECG is at its largest among the target R waves. Then, as shown in Figure 11(a), the execution device 71 identifies the earliest timing among the multiple R wave timings that is after the first timing TA as the second timing TB. As shown in Figure 10, after step S222, the execution device 71 proceeds to step S223.

[0059] In step S223, the execution device 71 extracts time-series data of acceleration GA from the time-series data of acceleration GA acquired in step S211, specifically for a specific period PS that includes the period during which the left atrial H3A contracts. Specifically, as shown in Figures 11(a) and 11(b), the execution device 71 identifies the period from the first timing TA to the second timing TB as the specific period PS. Then, the execution device 71 extracts time-series data of acceleration GA from the time-series data of acceleration GA acquired in step S211 for the specific period PS. In this embodiment, the processing in steps S221 to S223 is an example of the extraction process. As shown in Figure 10, after step S223, the execution device 71 proceeds to step S31. Then, the execution device 71 executes the processing from step S31 onward. Note that the processing from step S31 onward is the same as in the first embodiment, so the explanation is omitted.

[0060] <Effects of this embodiment> In this embodiment, in addition to the effects of (1-1), (1-3) to (1-7) described above, the following effects of (2-1) and (2-2) are achieved.

[0061] (2-1) As shown in Figure 8, the analysis system SA is equipped with an electrocardiogram sensor 20 that detects an electrocardiogram signal ECG, which is an electrical signal of the subject's heart H. As shown in Figure 10, in step S211, the execution device 71 of the calculation device 70 acquires time-series data of acceleration GA, as well as time-series data of the electrocardiogram signal ECG associated with the time-series data of acceleration GA. In steps S221 to S223, the execution device 71 extracts time-series data of acceleration GA for a specific period PS that includes the period in which the left atrial H3A contracts, based on the time-series data of the electrocardiogram signal ECG.

[0062] According to the above configuration, by incorporating time-series data of the electrocardiogram signal (ECG), it becomes easier to identify a specific period of PS that includes the period during which the left atrial H3A contracts, compared to, for example, identifying a specific period of PS based solely on time-series data of acceleration GA. This makes it easier to extract time-series data of acceleration GA during the specific period of PS necessary for analyzing the contractility of the left atrial H3A.

[0063] (2-2) As shown in Figure 10, in step S221, the execution device 71 identifies a first timing TA based on the time-series data of the electrocardiogram signal ECG. Here, as shown in Figure 11, the first timing TA is the timing that satisfies a predetermined first condition indicating the start of left atrial H3A contraction. As shown in Figure 10, in step S222, the execution device 71 identifies a second timing TB based on the time-series data of the electrocardiogram signal ECG. Here, as shown in Figure 11, the second timing TB is the timing that satisfies a predetermined second condition indicating the end of left atrial H3A contraction. As shown in Figure 10, in step S223, the execution device 71 defines the period from the first timing TA to the second timing TB as a specific period PS and extracts time-series data of acceleration GA during the specific period PS.

[0064] According to the above configuration, time-series data of acceleration GA during a specific PS period related to left atrial H3A contractility can be extracted more appropriately. This prevents a decrease in the accuracy of analysis regarding left atrial H3A contractility caused, for example, by the inclusion of time-series data of acceleration GA unrelated to left atrial H3A contractility in the time-series data of acceleration GA during a specific PS period.

[0065] <Examples of Modifications> The first and second embodiments described above can be implemented with the following modifications. The first and second embodiments described above, and the following examples of modifications, can be combined with each other to the extent that they do not contradict the technical standards.

[0066] In the first embodiment described above, the analysis control may be modified. For example, in step S21, the method of specifying the first timing TA may be modified. Specifically, the execution device 71 may specify the timing after a predetermined reference period with respect to the closure timing of the aortic valve H4B as the first timing TA. In other words, the predetermined first condition indicating the end of the change in acceleration GA due to the closure of the aortic valve H4B can be modified.

[0067] For example, in step S22, the method of specifying the second timing TB may be changed. Specifically, the execution device 71 may specify the timing before a predetermined reference period with respect to the opening timing of the aortic valve H4B as the second timing TB. In other words, the predetermined second condition indicating the start of the change in acceleration GA due to the opening of the aortic valve H4B can be changed.

[0068] ・For example, the processing in steps S21 to S23 may be changed. As a specific example, the execution device 71 may extract time-series data of acceleration GA during a specific period PS as follows. The execution device 71 acquires time-series data of acceleration GA from the aortic valve H4B closure timing identified in step S20 to the aortic valve H4B opening timing. That is, as a specific example, as shown in Figure 13, the execution device 71 acquires time-series data of acceleration GA from time t10, which is the aortic valve H4B closure timing, to time t40, which is the aortic valve H4B opening timing. Next, the execution device 71 identifies the timing at which acceleration GA becomes a negative value after the aortic valve H4B closure timing, and then changes from that negative value to zero, as the first reference timing from the acquired time-series data of acceleration GA. Furthermore, the execution device 71 identifies the timing at which acceleration GA becomes a negative value before the opening timing of the aortic valve H4B, and then becomes zero, as the second reference timing from the acquired time-series data of acceleration GA. Subsequently, the execution device 71 identifies the maximum and minimum values ​​of acceleration GA based on the time-series data of acceleration GA during the period from the first reference timing to the second reference timing. Furthermore, the execution device 71 identifies the timing at which acceleration GA becomes zero before the timing of the identified minimum value of acceleration GA as the first timing TA. Furthermore, the execution device 71 identifies the timing at which acceleration GA becomes zero after the timing of the identified maximum value of acceleration GA as the second timing TB. Furthermore, the execution device 71 identifies the period from the first timing TA to the second timing TB as the specified period PS. Then, the execution device 71 extracts the time-series data of acceleration GA during the specified period PS from the time-series data of acceleration GA acquired in step S11. According to the above configuration, it is possible to identify a specific PS period during which the change in acceleration GA in response to left atrial H3A contraction is particularly large. In other words, it is possible to identify a specific PS period that is particularly highly related to left atrial H3A contraction. As a result, the accuracy of the analysis of left atrial H3A contractility can be improved.

[0069] For example, some of the processing in steps S31 to S34 may be omitted. In this case, conditions (1) to (4) in step S35 should be changed to match the above change. That is, in step S35, the execution device 71 can analyze the contractility of the left atrial H3A based on the values ​​identified in one or more of the processing in steps S31 to S34.

[0070] - For example, all processing in steps S31 to S34 may be omitted. Even in this case, in step S35, the execution device 71 can analyze the contractility of the left atrial H3A based on the time-series data of acceleration GA during the specified PS period. As a specific example, the execution device 71 may output a result showing the contractility of the left atrial H3A by inputting the time-series data of acceleration GA during the specified PS period into predetermined model data. An example of the model data here is data in which the relationship between the time-series data of acceleration GA during the specified PS period and the analysis result of the contractility of the left atrial H3A is predetermined.

[0071] For example, in step S35, the conditions for determining whether the contractility of the left atrial H3A is normal may be changed. Specifically, the execution device 71 may determine that the contractility of the left atrial H3A is normal if one or more of conditions (1) to (4) are met.

[0072] - For example, in step S35, it is not necessary to determine whether the contractility of the left atrial H3A is normal or not. Specifically, in step S35, the execution device 71 may determine a numerical value indicating the degree of abnormality in the contractility of the left atrial H3A based on the various values ​​identified in steps S31 to S34. Also, specifically, in step S35, the execution device 71 may determine a numerical value indicating the stiffness of the left atrial H3A, or so-called left atrial strain, which is an indicator of the contractility of the left atrial H3A, based on the various values ​​identified in steps S31 to S34. In the above case, instead of steps S41 and S42, the execution device 71 may perform output processing after step S35 to output the numerical value identified above.

[0073] Furthermore, as a specific example, in step S35, the execution device 71 may only compare the various values ​​identified in steps S31 to S34 with the various threshold values ​​predetermined in the specified data DR. In this case, instead of steps S41 and S42, it is preferable that the execution device 71 performs output processing after step S35 to output the results of the above comparison. In other words, it is preferable that the execution device 71 notifies the results of the four comparisons in step S35. As a specific example, the execution device 71 may output a control signal to the display 82, thereby displaying on the display 82 the amplitude AG of the acceleration GA due to the contraction of the left atrial H3A identified in step S31, and the predetermined amplitude predetermined in the specified data DR. With the above configuration, for example, a doctor can determine the state of the contractility of the left atrial H3A by checking the results output in the above output processing.

[0074] For example, in step S35, the object of analysis can be changed. As a premise, in heart H, the period during which the left atrium H3A contracts and the period during which the right atrium H1A contracts overlap. Therefore, in step S35, the execution device 71 can determine whether the contractility of the right atrium H1A is normal by comparing the various values ​​identified in steps S31 to S34 with various thresholds predetermined in the specified data DR.

[0075] For example, in step S41, the results of the four comparisons may be transmitted externally. Specifically, the execution device 71 of the arithmetic unit 70 may transmit information such as the results of the four comparisons in step S35 to an external source, such as a server, outside the analysis system SA. In this case, the server may generate new specified data DR based on the information collected from the analysis system SA. The server may then update the specified data DR by transmitting the new specified data DR to the arithmetic unit 70 of the analysis system SA. With this configuration, various threshold values ​​defined in the specified data DR can be updated to more appropriate values. In this respect, the same changes can also be made in step S42.

[0076] For example, the configuration for notifying that the contractility of the left atrial H3A is abnormal in step S42 can be changed. Specifically, the execution device 71 may output a control signal to a speaker (not shown) and use the sound output from the speaker to notify that the contractility of the left atrial H3A is abnormal. In this respect, the configuration for step S41 can also be changed in the same way as above.

[0077] ・For example, the position of the subject when performing analysis control may be changed. Specifically, the subject's position does not have to be supine; it may be a lateral position such as left lateral or right lateral decubitus. In this regard, the inventors have confirmed that when the subject is in a lateral decubitus position, the sensitivity of acceleration GA detected by the acceleration sensor 10, that is, acceleration GA which is a value indicating the movement of the body surface caused by the movement of the subject's heart, increases. Therefore, from the viewpoint of increasing the sensitivity of acceleration GA detected by the acceleration sensor 10, it is preferable that the subject is in a lateral decubitus position. Furthermore, from the viewpoint of increasing the sensitivity of acceleration GA detected by the acceleration sensor 10, it is particularly preferable that the subject is in a left lateral decubitus position.

[0078] For example, in analysis control, it may be determined whether the subject's body position is a predetermined specified position. In this case, the acceleration sensor 10 as an inertial sensor may be a so-called three-axis sensor. Here, the first axis, second axis, and third axis are mutually orthogonal axes. The acceleration sensor 10 detects the first acceleration, which is the acceleration in the direction along the first axis, the second acceleration, which is the acceleration in the direction along the second axis, and the acceleration GA, which is the acceleration in the direction along the third axis. When executing analysis control, the user of the analysis system SA attaches the acceleration sensor 10 to the subject's body surface near the apex H5 of the subject's heart H in a predetermined orientation. As an example, the user of the analysis system SA attaches the acceleration sensor 10 by sticking it to the subject's body surface. As a specific example, the execution device 71 of the calculation device 70 may perform analysis control as follows. In the acquisition process, the execution device 71 acquires time-series data of the first acceleration, time-series data of the second acceleration, and time-series data of acceleration GA. Furthermore, the execution device 71 performs a posture determination process to determine whether the subject's posture is a predetermined specified posture based on the time-series data of the first acceleration, time-series data of the second acceleration, and time-series data of acceleration GA. As an example, the execution device 71 calculates the angle between the direction of gravity identified from the first acceleration, second acceleration, and acceleration GA, and a specified direction indicating the direction of gravity when the subject's posture is the specified posture. The execution device 71 then determines that the subject's posture is the specified posture if the calculated angle is within a predetermined specified angle. If the execution device 71 determines that the subject's posture is the specified posture in the posture determination process, in the extraction process, it extracts the time-series data of acceleration GA for a specific period PS from the time-series data of acceleration GA when the subject's posture is the specified posture. According to the above configuration, time-series data of acceleration GA during a specific period of PS is extracted from time-series data of acceleration GA when the subject's body position is a predetermined specified position. Then, the contractility of the left atrial H3A is analyzed based on the time-series data of acceleration GA during that specific period of PS.Therefore, for example, it is possible to suppress the decrease in the accuracy of left atrial H3A contractility analysis caused by analyzing left atrial H3A contractility based on time-series data of acceleration GA when the subject's body position is not the prescribed position.

[0079] Regarding the above configuration, the preferred position is the left lateral decubitus position. The left lateral decubitus position here is an example of a lateral decubitus position. As mentioned above, from the viewpoint of increasing the sensitivity of the acceleration GA detected by the acceleration sensor 10, it is preferable that the subject's position is the lateral decubitus position. Therefore, with the above configuration, the accuracy of the analysis of the contractility of the left atrial H3A can be improved by increasing the sensitivity of the acceleration GA detected by the acceleration sensor 10.

[0080] In the first embodiment described above, the analysis system SA may be modified. For example, the analysis device is not limited to the calculation device 70. Specifically, the acquisition device 60 may function as the analysis device.

[0081] For example, the configuration of the inertial sensor may be changed. Specifically, the acceleration sensor 10 as an inertial sensor may be a so-called three-axis sensor. Also, specifically, the analysis system SA may be equipped with an angular velocity sensor as an inertial sensor in place of or in addition to the acceleration sensor 10. In other words, the inertial sensor can be any sensor that detects the inertial value indicating the movement of the body surface caused by the movement of the subject's heart H.

[0082] ・For example, the analysis system SA may be equipped with other sensors. As a specific example, as shown in Figure 12, the analysis system SA may be equipped with a heart sound sensor 30 that detects heart sounds HB, which are sounds produced by the movement of the subject's heart H. In this case, the execution device 71 of the calculation device 70 may perform the following processing in the analysis control. First, in the acquisition process, the execution device 71 acquires time-series data of acceleration GA, as well as time-series data of heart sounds HB linked to the time-series data of acceleration GA. Furthermore, in the extraction process, the execution device 71 extracts time-series data of acceleration GA for a specific period PS that includes the period when the left atrial H3A contracts, based on the time-series data of heart sounds HB. Here, as a premise, the heart sounds HB detected by the heart sound sensor 30 tend to become louder at the closure timing when the aortic valve H4B closes, or at the closure timing when the mitral valve H3B closes. Furthermore, the period during which the left atrial H3A contracts is from the time the aortic valve H4B closes until the time the mitral valve H3B closes. Therefore, with the above configuration, by incorporating the time-series data of heart sound HB, it is easier to identify a specific period PS that includes the period during which the left atrial H3A contracts, compared to, for example, identifying a specific period PS based solely on the time-series data of acceleration GA. This makes it easier to extract the time-series data of acceleration GA in the specific period PS necessary for analyzing the contractility of the left atrial H3A.

[0083] For example, the method of communication in the analysis system SA may be changed. Specifically, the acquisition device 60 may be able to communicate with the acceleration sensor 10 via wireless communication. Also, specifically, the calculation device 70 may be able to communicate with the acquisition device 60 via wireless communication.

[0084] In the second embodiment described above, the analysis control may be modified. For example, in step S221, the method of identifying the first timing TA may be modified. Specifically, first, the execution device 71 identifies the earliest timing among the timings of multiple P waves. Then, the execution device 71 may identify the timing after a predetermined reference period from the identified timing as the first timing TA. In other words, the predetermined first condition indicating the start of left atrial H3A contraction can be modified.

[0085] For example, the first condition in step S221 is not limited to a condition indicating the start of left atrial H3A contraction. Specifically, the execution device 71 first identifies the earliest timing among multiple P wave timings. The execution device 71 may then identify a timing prior to the identified timing, which is a predetermined reference period before that timing, as the first timing TA. In other words, the first timing TA can be set to an acceptable time earlier than the actual timing of the start of left atrial H3A contraction.

[0086] For example, in step S222, the method of identifying the second timing TB may be changed. Specifically, first, the execution device 71 identifies the earliest timing among multiple R wave timings that is later than the first timing TA. Then, the execution device 71 may identify the timing after a predetermined reference period relative to the identified timing, or the timing before a predetermined reference period relative to the identified timing, as the second timing TB. In other words, the predetermined second condition indicating the end of left atrial H3A contraction can be changed.

[0087] For example, the second condition in step S222 is not limited to a condition indicating the end of left atrial H3A contraction. Specifically, the execution device 71 may identify the second timing TB by using the timing of the so-called S wave instead of the R wave. In other words, the second timing TB can be delayed to an acceptable extent relative to the actual timing of the end of left atrial H3A contraction.

[0088] SA...Analysis system 10...Accelerometer 20...Electrocardiogram sensor 60...Acquisition device 61...Execution device 62...Storage device 70...Calculation device 71...Execution device 72...Storage device 72A...Analysis program DR...Specified data 81...Input device 82...Display

Claims

1. An analysis system comprising: an inertial sensor that detects an inertial value which is a value indicating the movement of the body surface caused by the movement of the subject's heart; and an analysis device that acquires the inertial value from the inertial sensor, wherein the analysis device performs an acquisition process to acquire time-series data of the inertial value; an extraction process to extract time-series data of the inertial value from the time-series data of the inertial value for a specific period including the period in which the atria of the heart contract; and an analysis process to analyze the contractility of the atria based on the time-series data of the inertial value for the specific period.

2. The analysis system according to claim 1, wherein the analysis device, in the extraction process, identifies a first timing based on the time-series data of the inertia value, after the aortic valve in the heart has closed and satisfies a predetermined first condition indicating the end of the change in the inertia value due to the closure; identifies a second timing based on the time-series data of the inertia value, before the aortic valve opens and satisfies a predetermined second condition indicating the start of the change in the inertia value due to the opening; and extracts time-series data of the inertia value during the specified period, with the period from the first timing to the second timing being the specified period.

3. The analysis system according to claim 1, comprising an electrocardiogram sensor for detecting an electrocardiogram signal, which is an electrical signal of the heart, wherein the analysis device, in the acquisition process, acquires time-series data of the electrocardiogram signal linked to the time-series data of the inertia value in addition to the time-series data of 4. The analysis system according to claim 3, wherein in the extraction process, a first timing is identified based on the time-series data of the electrocardiogram signal that satisfies a predetermined first condition indicating the start of atrial contraction, a second timing is identified based on the time-series data of the electrocardiogram signal that satisfies a predetermined second condition indicating the end of atrial contraction, and the period from the first timing to the second timing is defined as the specified period, and time-series data of the inertia value during the specified period is extracted.

5. The analysis system according to claim 1, comprising a heart sound sensor for detecting heart sounds, which are sounds produced by the movement of the heart, wherein the analysis device, in the acquisition process, acquires time-series data of heart sounds linked to the time-series data of inertia values ​​in addition to the time-series data of said time-series data, and in the extraction process, extracts time-series data of inertia values ​​for a specific period based on the time-series data of heart sounds.

6. The analysis system according to any one of claims 1 to 5, wherein the analysis device, in the analysis process, identifies the amplitude of the inertia due to atrial contraction based on time-series data of the inertia during the specified period, and analyzes the contractility of the atrial by comparing the identified amplitude with a predetermined specified amplitude.

7. The analysis system according to any one of claims 1 to 6, wherein the analysis device, in the analysis process, identifies the frequency of the inertia due to atrial contraction based on time-series data of the inertia during the specified period, and analyzes the contractility of the atrial by comparing the identified frequency with a predetermined frequency.

8. The analysis system according to any one of claims 1 to 7, wherein the analysis device, in the analysis process, identifies an integral value for the change in the inertia value due to atrial contraction based on time-series data of the inertia value during the specified period, and analyzes the contractility of the atrial by comparing the identified integral value with a predetermined integral value.

9. The analysis system according to any one of claims 1 to 8, wherein the analysis device, in the analysis process, identifies a differential value for the change in the inertia value due to atrial contraction based on time-series data of the inertia value during the specified period, and analyzes the contractility of the atrial by comparing the identified differential value with a predetermined specified differential value.

10. The analysis system according to any one of claims 6 to 9, wherein the analysis device performs output processing to output the results of the comparison.

11. The analysis system according to any one of claims 6 to 9, wherein the analysis device determines whether or not there is an abnormality in the contractile function of the atrial heart by the comparison in the analysis process, and performs an output process to output the result of whether or not there is an abnormality in the contractile function of the atrial heart.

12. When the first axis, second axis, and third axis are mutually orthogonal axes, the inertial sensor detects a first acceleration which is acceleration in the direction along the first axis, a second acceleration which is acceleration in the direction along the second axis, and the inertial value which is acceleration in the direction along the third axis; the analysis device acquires time-series data of the first acceleration, time-series data of the second acceleration, and time-series data of the inertial value in the acquisition process; performs a body position determination process to determine whether the body position of the subject is a predetermined prescribed body position based on the time-series data of the first acceleration, time-series data of the second acceleration, and time-series data of the inertial value; and when the body position determination process determines that the body position of the subject is the prescribed body position, the extraction process extracts the time-series data of the inertial value for a specific period from the time-series data of the inertial value when the body position of the subject is the prescribed body position. The analysis system according to any one of claims 1 to 11.

13. The analysis system according to claim 12, wherein the prescribed position is the lateral recumbent position.

14. An analysis device that performs the following: an acquisition process to acquire time-series data of inertia values ​​from an inertia sensor that detects inertia values, which are values ​​indicating the movement of the body surface caused by the movement of the subject's heart; an extraction process to extract time-series data of inertia values ​​from the time-series data of inertia values ​​for a specific period that includes the period in which the atria of the heart contract; and an analysis process to analyze the contractile capacity of the atria based on the time-series data of inertia values ​​for the specific period.

15. An analysis program applied to an analysis system comprising: an inertial sensor that detects an inertial value which is a value indicating the movement of the body surface caused by the movement of the subject's heart; and an analysis device that acquires the inertial value from the inertial sensor, wherein the analysis program causes the analysis device to execute: an acquisition process that acquires time-series data of the inertial value; an extraction process that extracts time-series data of the inertial value from the time-series data of the inertial value for a specific period that includes the period during which the atria of the heart contract; and an analysis process that analyzes the contractile capacity of the atria based on the time-series data of the inertial value for the specific period.

16. An analysis program that causes the analysis device to execute: an acquisition process to acquire time-series data of inertia values ​​from an inertia sensor that detects inertia values, which are values ​​indicating the movement of the body surface caused by the movement of the subject's heart; an extraction process to extract time-series data of inertia values ​​from the time-series data of inertia values ​​for a specific period that includes the period in which the atria of the heart contract; and an analysis process to analyze the contractility of the atria based on the time-series data of inertia values ​​for the specific period.

Images