WO2025173650A1 - Information processing program, information processing method, and information processing device - Google Patents

Abstract

This information processing program causes a computer to execute processing of: acquiring health state information including disease information of a patient; identifying a disease or a constitution on the basis of the health state information; acquiring a heart sound waveform of the patient; setting an extraction condition of a feature point of the heart sound waveform on the basis of the disease or the constitution; and extracting the feature point of the heart sound waveform on the basis of the set extraction condition.

Description

Information processing program, information processing method, and information processing device

The present invention relates to an information processing program, information processing method, and information processing device for extracting characteristic points of a cardiac waveform.

Methods for non-invasively estimating cardiac status have been proposed for purposes such as follow-up of patients with a history of cardiac disease such as heart failure. For example, Patent Document 1 proposes a method for calculating left ventricular end-diastolic pressure (LVEDP) as a function of heartbeat intervals, electrocardiogram (ECG), and phonocardiogram.

US Patent Application Publication No. 2023/131629

However, if a patient has a condition such as valvular disease, it may not be possible to accurately extract feature points from cardiac waveforms such as electrocardiograms and phonocardiograms. As a result, there is a risk that the accuracy of estimating left ventricular end-diastolic pressure will decrease. Patent Document 1 mentions the need to correct left ventricular end-diastolic pressure for patients with valvular disease, but does not disclose specific methods for correction or for extracting feature points.

The present invention was made in light of these circumstances. Its purpose is to provide an information processing program, information processing method, and information processing device that extracts characteristic points of cardiac sound waveforms while taking into account the patient's illness or constitution.

The present invention is an information processing program that (1) causes a computer to execute the following processes: acquire health condition information, including disease information, identify the patient's disease or constitution based on the health condition information; acquire the patient's phonocardiogram waveform; set extraction conditions for feature points of the phonocardiogram waveform based on the disease or constitution; and extract feature points of the phonocardiogram waveform based on the set extraction conditions.

Here, an embodiment of the present invention is
(2) In the information processing program of (1) above, it is preferable that the extraction condition is to recognize that there are two or more peaks in the cardiac sound waveform and select the first peak, or to change the time range for extracting the feature points.

(3) In the information processing program described in (1) or (2) above, it is preferable that the extraction condition changes the order in which the feature points appear or replaces the feature points with other feature points.

(4) In the information processing program described in any one of (1) to (3) above, it is preferable that the extraction condition changes the amplitude threshold for determining noise in the cardiac sound waveform.

(5) It is preferable that the information processing program described in any of (1) to (4) above discards the feature points.

(6) It is preferable that the information processing program described in any of (1) to (5) above obtains the health condition information from an electronic medical record.

(7) In the information processing program described in any one of (1) to (6) above, the disease preferably includes valvular disease or arrhythmia.

(8) It is preferable that the information processing program described in any one of (1) to (7) above estimates intracardiac pressure based on the extracted feature points and outputs the estimated intracardiac pressure.

(9) Preferably, the information processing program described in any of (1) to (8) above acquires an electrocardiogram and a pulse waveform synchronized with a cardiac sound waveform, extracts feature points of the acquired electrocardiogram and pulse waveform, and estimates intracardiac pressure based on the feature points of the cardiac sound waveform and the feature points of the electrocardiogram and the pulse waveform.

(10) In the information processing program described in (9) above, the disease causes an abnormal change in the morphology of the cardiac sound waveform, and the abnormal change includes the splitting of one or two cardiac sounds in the cardiac sound waveform. Preferably, the extraction conditions are set so that the start points of the first sound and the second sound are extracted based on the appearance of two or more waveform peaks due to the splitting, or so that the start points of the first sound and the second sound are extracted from the R wave extracted from the electrocardiogram within a time range that takes the split into account.

(11) It is preferable that the information processing program described in (9) or (10) above estimates intracardiac pressure based on the characteristic points of the phonocardiogram waveform and the characteristic points of the electrocardiogram and pulse waveform that are extracted assuming that there is no disease or predisposition that causes abnormal changes in the morphology of the phonocardiogram waveform, and outputs the estimated intracardiac pressure.

(12) Preferably, the information processing method involves a computer executing a process to acquire health condition information including disease information of a patient, identify a disease or constitution based on the health condition information, acquire the patient's acoustocardiogram, set extraction conditions for feature points of the acoustocardiogram based on the disease or constitution, and extract feature points of the acoustocardiogram based on the set extraction conditions.

(13) Preferably, the information processing device is an information processing device that includes a control unit, and the control unit acquires health condition information including disease information of a patient, identifies a disease or constitution based on the health condition information, acquires the patient's acoustocardiogram, sets extraction conditions for feature points of the acoustocardiogram based on the disease or constitution, and executes processing to extract feature points of the acoustocardiogram based on the set extraction conditions.

In one aspect of the present application, it is possible to extract characteristic points of the cardiac waveform taking into account the patient's illnesses and physical constitution.

FIG. 1 is an explanatory diagram illustrating an example of the configuration of a monitoring system. FIG. 2 is a block diagram illustrating an example of a hardware configuration of a server. FIG. 2 is a block diagram illustrating an example of the hardware configuration of the measurement device. FIG. 2 is a block diagram illustrating an example of a hardware configuration of a communication terminal. FIG. 2 is a block diagram showing an example of the hardware configuration of a medical professional terminal. FIG. 2 is an explanatory diagram illustrating an example of a patient DB. FIG. 10 is an explanatory diagram showing an example of a comorbidity DB. FIG. 2 is an explanatory diagram illustrating an example of a waveform DB. FIG. 10 is an explanatory diagram illustrating an example of a result DB. 1 is a graph showing an electrocardiogram, a cardiac waveform, and a pulse wave, as well as characteristic points of each waveform. FIG. 10 is an explanatory diagram illustrating an example of a training DB. FIG. 1 is an explanatory diagram illustrating a learning model. 10 is a flowchart illustrating an example of a procedure for a generation process. 10 is a flowchart illustrating an example of a monitoring procedure. 10 is a flowchart illustrating an example of a procedure for a measurement process. 10 is a flowchart illustrating an example of a procedure for an estimation process. 10 is a flowchart showing an example of the procedure of intracardiac pressure estimation processing. 10 is a flowchart illustrating an example of a procedure for feature point extraction processing. FIG. 10 is an explanatory diagram showing a correction method. 10 is a flowchart illustrating an example of a procedure for correction processing. FIG. 10 is an explanatory diagram showing an example of a correction method. FIG. 10 is an explanatory diagram showing an example of a correction method. 10 is a flowchart illustrating an example of a procedure for a result reference process. FIG. 10 is an explanatory diagram showing an example of a result reference screen. FIG. 10 is an explanatory diagram showing another example of a learning model.

(Embodiment 1)
An embodiment will be described below with reference to the drawings. FIG. 1 is an explanatory diagram showing an example of the configuration of a monitoring system 100. The monitoring system 100 is a system used for remote monitoring of patients diagnosed with heart failure. The monitoring system 100 includes a server 1, a measuring device 2, a communication terminal 3, and a medical professional terminal 4. The monitoring system 100 may transmit and receive patient-related data with an electronic medical record system 5. Although FIG. 1 shows two measuring devices 2 and two communication terminals 3, this is not limiting. There may be one measuring device 2 and one communication terminal 3, or three or more of each. Furthermore, although only one medical professional terminal 4 is shown in FIG. 1, there may be two or more medical professional terminals 4.

Patients who are the monitoring targets using the monitoring system 100 use the measuring device 2 to perform measurements to evaluate cardiac function at their home, workplace, or residence, such as a nursing home. In this specification, the measuring device 2 synchronously measures the electrocardiogram, heart sounds, and pulse waves. When the patient visits a hospital or is hospitalized, the measuring device 2 installed at the medical institution may be used to measure the electrocardiogram, heart sounds, and pulse waves. It is desirable to measure the electrocardiogram, heart sounds, and pulse waves at least once a day.

The measurement data for electrocardiogram, heart sounds, and pulse waves measured by the measurement device 2 is transmitted to and stored by the server 1 via the communication terminal 3 and the network N. The server 1 estimates the patient's intracardiac pressure based on the measurement data. The medical professional terminal 4 acquires and displays the patient's measurement data and estimated intracardiac pressure from the server 1. The communication terminal 3 may also estimate the patient's intracardiac pressure based on the measurement data.

Intracardiac pressure is an indicator of the state of cardiac function. It is known that in heart failure, intracardiac pressure rises before the onset of subjective symptoms. Therefore, by monitoring intracardiac pressure, it is possible to detect the worsening of heart failure at an earlier stage than the onset of subjective symptoms. Heart failure can be caused by myocardial infarction, angina pectoris, arteriosclerosis, hypertension, valvular disease, cardiomyopathy, arrhythmia, etc. Intracardiac pressures include, but are not limited to, left heart pressure, right heart pressure, left ventricular pressure waveform, right ventricular pressure waveform, as well as left ventricular end-diastolic pressure (LVEDP), left atrial pressure (LAP), left ventricular pressure (LVP), pulmonary artery end-diastolic pressure (PAEDP, or PADP), right atrial pressure (RAP), right ventricular pressure (RVP), central venous pressure (CVP), pulmonary artery pressure (PAP), and pulmonary wedge pressure (PWP). Pulmonary artery wedge pressure is also known as PAWP (pulmonary arterial wedge pressure), PCWP (pulmonary capillary wedge pressure), or PAOP (pulmonary artery occlusion pressure). Because intracardiac pressure fluctuates periodically with each heartbeat, it includes systolic pressure, diastolic pressure, and mean pressure.

Doctors refer to the measurement data and intracardiac pressure displayed on the medical professional terminal 4 to understand the patient's condition. If a doctor discovers an out-of-hospital patient suspected of having worsening heart failure, they will recommend that the patient visit a doctor, change their medication prescription, or provide guidance on improving lifestyle habits, and will provide prompt treatment to outpatients or hospitalized patients. Instead of the doctor, paramedical staff under the doctor's instructions can check the measurement data and intracardiac pressure and, if necessary, seek the doctor's judgment.

Figure 2 is a block diagram showing an example of the hardware configuration of server 1. Server 1 includes a control unit 11, a main memory unit 12, an auxiliary memory unit 13, a communication unit 14, and a reading unit 15. Each component is connected via bus B. Server 1 is composed of a server computer, a workstation, a PC (Personal Computer), etc. Server 1 may also be composed of a multi-computer consisting of multiple computers, a virtual machine constructed virtually by software, or a quantum computer. Furthermore, the functions of server 1 may be realized by a cloud service.

The control unit 11 has one or more central processing units (CPUs), micro-processing units (MPUs), graphics processing units (GPUs), and other processing devices. The control unit 11 reads and executes a control program 1P (program, program product) stored in the auxiliary storage unit 13, thereby performing various information processing, control processing, and the like, and realizing various functional units.

The main memory unit 12 is an SRAM (Static Random Access Memory), DRAM (Dynamic Random Access Memory), flash memory, etc. The main memory unit 12 mainly temporarily stores data required by the control unit 11 to perform arithmetic processing.

The auxiliary memory unit 13 is a hard disk or SSD (Solid State Drive), etc., and stores the control program 1P and various DBs (Databases) required for the control unit 11 to execute processing. The auxiliary memory unit 13 stores a patient DB 131, a comorbidity DB 132, a waveform DB 133, and a result DB 134. The auxiliary memory unit 13 also stores a learning model M. The auxiliary memory unit 13 may be separate from the server 1 and may be an external storage device connected externally. The various DBs, etc. stored in the auxiliary memory unit 13 may be stored in a database server or cloud storage different from the server 1.

The communication unit 14 communicates with the communication terminal 3 and medical professional terminal 4 via the network N. The control unit 11 may also use the communication unit 14 to download the control program 1P from another computer via the network N, etc., and store it in the auxiliary storage unit 13.

The reading unit 15 reads portable storage media 1a, including CD (Compact Disc)-ROM and DVD (Digital Versatile Disc)-ROM. The control unit 11 may read the control program 1P from the portable storage medium 1a via the reading unit 15 and store it in the auxiliary storage unit 13. The control unit 11 may also read the control program 1P from the semiconductor memory 1b.

Figure 3 is a block diagram showing an example of the hardware configuration of the measurement device 2. The measurement device 2 includes a sensor unit 21 and a processing unit 22. The sensor unit 21 includes an ECG (Electrocardiogram) sensor 211 that measures an electrocardiogram, a heart sound sensor 212 that measures heart sounds, and a pulse wave sensor 213 that measures pulse waves.

The sensor unit 21 may, for example, house the ECG sensor 211, heart sound sensor 212, and pulse wave sensor 213 in a single housing, or may house the ECG sensor 211 and pulse wave sensor 213 in a single housing and the heart sound sensor 212 in a separate housing. By placing or fixing the sensor unit 21 near the heart in the patient's chest, it is possible to simultaneously measure the electrocardiogram, heart sounds, and pulse waves. If the sensor unit 21 does not have a housing, the patient places or fixes the ECG sensor 211, heart sound sensor 212, and pulse wave sensor 213 in pre-specified positions. The ECG sensor 211, heart sound sensor 212, and pulse wave sensor 213 are well known, so detailed description will be omitted.

The processing unit 22 includes a control unit 221, a main memory unit 222, an auxiliary memory unit 223, and a communication unit 224. Each component is connected via a bus B. The control unit 221 has one or more arithmetic processing devices such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), or a GPU (Graphics Processing Unit). The control unit 221 reads and executes control programs stored in the auxiliary memory unit 223, thereby performing various information processing, control processing, etc., and realizing various functional units.

The main memory unit 222 is an SRAM, DRAM, flash memory, etc. The main memory unit 222 mainly temporarily stores data required by the control unit 221 to execute arithmetic processing.

The auxiliary storage unit 223 is a hard disk or SSD, etc., and stores the control programs and various data required for the control unit 221 to execute processing.

The communication unit 224 mainly communicates with the communication terminal 3. The communication unit 224 transmits measurement data to the communication terminal 3 via short-range wireless communication such as Bluetooth (registered trademark), WiFi ad hoc mode, or ZigBee (registered trademark).

The measuring device 2 may be integrated with a health management device such as a blood pressure monitor or a weighing scale. The measuring device 2 may also be integrated with a so-called smart watch that the patient wears on a daily basis.

Figure 4 is a block diagram showing an example of the hardware configuration of the communication terminal 3. The communication terminal 3 is configured as a smartphone, smart display, tablet computer, notebook computer, etc. The communication terminal 3 includes a control unit 31, a main memory unit 32, an auxiliary memory unit 33, a communication unit 34, and a touch panel 35. Each component is connected by a bus B.

The control unit 31 has one or more processing units such as a CPU, MPU, GPU, etc. The control unit 31 reads and executes control programs stored in the auxiliary storage unit 33 to perform various information processing, control processing, etc., and realize various functional units.

The main memory unit 32 is an SRAM, DRAM, flash memory, etc. The main memory unit 32 mainly temporarily stores data required by the control unit 31 to perform arithmetic processing.

The auxiliary storage unit 33 is a hard disk or SSD, etc., and stores the control programs and various data required for the control unit 31 to execute processing.

The communication unit 34 communicates with the measurement device 2 and the server 1. As described above, the communication unit 34 communicates with the measurement device 2 via short-range wireless communication. The communication unit 34 communicates with the server 1 via the network N.

The touch panel 35 includes a display unit 351 and an input unit 352. The display unit 351 includes a liquid crystal display panel or an organic EL (electro-luminescence) display panel. The input unit 352 is layered on the display unit 351. The communication terminal 3 may be provided with a keyboard and a mouse or a trackpad instead of or in addition to the touch panel 35.

Figure 5 is a block diagram showing an example of the hardware configuration of the medical professional terminal 4. The medical professional terminal 4 is configured from a laptop computer, tablet computer, smartphone, smart display, etc. The medical professional terminal 4 includes a control unit 41, a main memory unit 42, an auxiliary memory unit 43, a communication unit 44, a display unit 45, and an input unit 46. Each component is connected by a bus B.

The control unit 41 has one or more processing units such as a CPU, MPU, GPU, etc. The control unit 41 provides various functions by reading and executing a control program 4P (program, program product) stored in the auxiliary storage unit 43.

The main memory unit 42 is an SRAM, DRAM, flash memory, etc. The main memory unit 42 mainly temporarily stores data required by the control unit 41 to perform arithmetic processing.

The auxiliary memory unit 43 is a hard disk or SSD, etc., and stores various data necessary for the control unit 41 to execute processing. The auxiliary memory unit 43 may be a separate unit from the medical professional terminal 4 and an externally connected external storage device. The various DBs, etc. stored in the auxiliary memory unit 43 may be stored on a database server or cloud storage.

Communication with the server 1 is performed via the communication unit 44 and the network N. The control unit 41 may use the communication unit 44 to download the control program 4P from another computer via the network N, etc., and store it in the auxiliary storage unit 43.

The display unit 45 includes a liquid crystal display panel or an organic EL display panel. The input unit 46 is a keyboard and mouse. The display unit 45 displays the intracardiac pressure and other information output by the server 1. The display unit 45 and input unit 46 may be integrated to form a touch panel display. The medical professional terminal 4 may display information on an external display device.

Next, we will explain the databases used by the monitoring system 100. Figure 6 is an explanatory diagram showing an example of the patient DB 131. The patient DB 131 stores information about patients who are the subject of monitoring. The patient DB 131 includes a patient ID column, a name column, an attending physician column, a medical record ID column, a comorbidity column, and a constitution column. The patient ID column stores a patient ID that can uniquely identify a patient. The so-called My Number may be used as the patient ID. The My Number is a 12-digit personal number stipulated in Japan by the Act on the Use of Numbers to Identify Specific Individuals in Administrative Procedures. The name column stores the patient's name. The attending physician column stores the name of the patient's doctor. The medical record ID column stores a medical record ID that can identify the patient's medical record. The comorbidity column stores diseases (disease information) that the patient has other than heart failure. The constitution column stores the patient's constitution. Figure 6 shows that a patient with patient ID P102 has aortic stenosis and hypertension as coexisting diseases and a frail constitution. While the names of diseases are stored in the coexisting diseases column in Figure 6, disease codes may be stored instead. Disease codes may be codes defined in, for example, the ICD10 Standard Disease Name Master (Medical Information Systems Development Center, a general incorporated foundation) or the Illness and Injury Name Master for Electronic Receipt Processing (Social Insurance Medical Fee Payment Fund). Information related to coexisting diseases and constitution is an example of health condition information. That is, in Figure 6, aortic stenosis, hypertension, and a frail constitution are examples of health condition information.

The information stored in the patient DB 131 may be obtained from the patient medical records stored in the electronic medical record system 5. Alternatively, the server 1 etc. may obtain patient information from the electronic medical record system 5 without providing the patient DB 131.

。 Figure 7 is an explanatory diagram showing an example of comorbidity DB132. Comorbidity DB132 stores information about abnormal changes in the morphology of the cardiac sound waveform when the patient being monitored has the corresponding comorbidity. Comorbidity DB132 includes a code string, a name string, a severity string, and a detection target string. The code string stores the disease code (in Figure 7, the master illness name for electronic prescription processing). The name string stores the disease name. The severity string stores the severity of the disease. The detection target string stores information about phenomena that should be detected in the cardiac sound waveform, etc. Figure 7 stores detection target codes assigned to phenomena that should be detected. Detection target codes will be described later. Abnormal changes in the morphology of a patient's cardiac sound waveform are not limited to diseases; the patient's constitution may also be a cause. Comorbidity DB132 may store information about constitutions that cause abnormal changes in the morphology of the cardiac sound waveform. When storing constitutions, the code column stores codes assigned in a system that does not overlap with disease codes. The name column stores the name of the constitution. The contents stored in the detection target column are the same as for diseases.

Figure 8 is an explanatory diagram showing an example of waveform DB 133. Waveform DB 133 stores waveform data from the measurement data obtained by measurement device 2. Waveform data is, for example, waveform data of heart sounds, waveform data of electrocardiograms, and waveform data of pulse waves. Waveform DB 133 includes a measurement ID column, a patient ID column, a measurement date and time column, a type column, and a measurement data column. The measurement ID column stores a measurement ID that can identify each measurement. The measurement ID makes it possible to associate multiple measurement data measured simultaneously. The patient ID column stores the patient ID of the patient who was the subject of the measurement. The measurement date and time column stores the measurement date and time and the date and time the measurement started. In addition to the start date and time, the date and time the measurement ended may also be stored. The type column stores the type of measurement data. For example, HS indicates heart sounds, ECG indicates electrocardiograms, and PW indicates pulse waves. The measurement data column stores waveform data obtained from measurements. It is desirable for the waveform data to be in a general-purpose format such as CSV (Comma Separated Values) or MFER (Medical Waveform Format Encoding Rule) format. The data for each point that makes up the waveform data is associated with time information, allowing synchronization between multiple waveforms.

Figure 9 is an explanatory diagram showing an example of the result DB 134. The result DB 134 stores feature points extracted from waveform data and intracardiac pressure estimated based on the feature points.

Feature points are described below. Feature points are points in waveform data that require attention in order to understand the movement of the heart. FIG. 10 is a graph showing an electrocardiogram, a cardiac waveform, and a pulse wave, as well as feature points of each waveform. Feature points of a cardiac waveform include the onset of the first sound, the peak of the first sound, the onset of the second sound, the _IIA point, and the _IIP point. The first sound occurs during ventricular contraction and is primarily the mitral valve closing sound and the aortic opening sound. The second sound occurs at the beginning of ventricular diastole and is primarily the aortic valve closing sound ( _IIA ) and the pulmonary valve closing sound ( _IIP ). In this specification, I represents the Roman numeral 1, and II represents the Roman numeral 2. The first sound may also be referred to as the first sound or S1, and the second sound may also be referred to as the second sound or S2.

On an electrocardiogram, waveforms known as P waves, QRS waves, T waves, and U waves are observed with each heartbeat. P waves are waveforms that represent atrial excitation (contraction). The first half of the P wave represents excitation of the right atrium, and the second half represents excitation of the left atrium; these combine to form the P wave. QRS waves are waveforms that represent ventricular excitation. The first downward waveform to appear is called the Q wave, and indicates the start of ventricular excitation. The following upward waveform is called the R wave, and indicates the point at which the ventricles are most contracting. The final downward waveform is called the S wave, and indicates the end of ventricular excitation. T waves are waveforms that represent the disappearance of ventricular excitation and a return to normal. T waves are characterized by their mountain-shaped appearance. U waves are small waveforms that appear after T waves. U waves are sometimes not seen normally. The points of interest here are the Q point and the R point. The Q point indicates the start of the Q wave. The R point indicates the peak point of the R wave.

The pulse wave is composed of components such as the ejection wave from the heart (PW: Percussion Wave), the reflected wave from blood vessel walls (TW: Tidal Wave), the dicrotic notch (DN: Dicrotic Notch), and the dicrotic wave (DW: Dicrotic Wave). Characteristic points on the pulse wave include the US (Up-Stroke) point, peak, and DN point. The US point is the point where the pulse wave rises. The peak is the point where the pulse wave is at its maximum. The DN point is the location of the notch.

The values indicating characteristic points of the cardiac sound waveform, characteristic points of the electrocardiogram, and characteristic points of the pulse wave are relative times indicating the elapsed time up to the characteristic point, assuming that the start of the waveform is time 0. If it is possible to calculate the time length between two characteristic points, the values indicating each characteristic point may be absolute times.

Returning to FIG. 9 , the result DB 134 includes a measurement ID sequence, a beat number sequence, a feature point sequence, and an intracardiac pressure sequence. The measurement ID sequence stores the measurement ID. The beat number sequence stores a sequential number assigned to data for one beat, which is obtained by dividing the measurement data into beats. Measurements using the measurement device 2 acquire waveforms for multiple beats. Waveform data for one beat is used to estimate intracardiac pressure. Therefore, data for each beat is stored in the result DB 134. The feature point sequence stores information on feature points extracted from the waveform data. The feature point sequence includes a first note onset sequence, a first note peak sequence, a second note onset sequence, a second _A note sequence, a second _P note sequence, a Q sequence, an R sequence, a US sequence, a peak sequence, and a DN sequence. Each sequence stores the time at which each feature point was measured. The first note onset sequence through the second _P note sequence store three types of values: an uncorrected value, a corrected value, and a flag indicating whether or not the feature point was selected for removal (removal flag). The intracardiac pressure column stores intracardiac pressures estimated based on feature points. The intracardiac pressure column includes a none column, a correction column, and a removal column. In this embodiment, intracardiac pressure is estimated using three methods. The intracardiac pressure column stores values estimated by each method. The none column stores intracardiac pressure values estimated without performing feature point correction and removal processes. The correction column stores intracardiac pressure values estimated by performing only feature point correction processes. The removal column stores intracardiac pressure values estimated by performing only feature point removal processes.

Next, the generation of the learning model M will be described. The learning model M is generated before the monitoring system 100 begins operation. FIG. 11 is an explanatory diagram showing an example of a training DB. The training DB stores training data used when generating the learning model M. The training DB includes a feature point sequence and an intracardiac pressure sequence. The feature point sequence stores the value of each feature point. The feature point sequence includes an I onset sequence, an I peak sequence, a II onset sequence, a II _A sequence, a II _P sequence, a Q sequence, an R sequence, a US sequence, a peak sequence, and a DN sequence. The values stored in each sequence are the same as the sequences with the same names in the result DB 134, so description thereof will be omitted. The intracardiac pressure sequence stores the measured intracardiac pressure values together with the feature point values. The intracardiac pressure values here are measured during cardiac catheterization.

The learning model M for estimating intracardiac pressure will now be described. In this embodiment, the learning model M estimates intracardiac pressure using only feature points extracted from the phonocardiogram waveform. Figure 12 is an explanatory diagram showing the learning model M. The learning model M accepts feature points of the phonocardiogram waveform as input, and estimates and outputs an estimated value of intracardiac pressure based on the accepted feature points. The learning model M outputs, for example, an estimated value of the representative value of intracardiac pressure in one heartbeat. The representative value is, for example, the diastolic intracardiac pressure, the systolic intracardiac pressure, and the average value of the intracardiac pressure. The diastolic intracardiac pressure is the minimum value of intracardiac pressure. The systolic intracardiac pressure is the maximum value of intracardiac pressure. A learning model M that outputs an estimated value of the diastolic intracardiac pressure, a learning model M that outputs an estimated value of the systolic intracardiac pressure, and a learning model M that outputs an estimated value of the average value of intracardiac pressure may be prepared. The learning model M may also estimate the time course of intracardiac pressure and output it as a waveform.

Figure 13 is a flowchart showing an example of the steps of the generation process. The generation process is a process of generating a learning model M using a training DB. As mentioned above, since the generation of the learning model M is assumed to be performed before the start of operation, it is assumed that a computer other than server 1 performs the generation process. Here, the description will be given assuming that it is performed by the generation computer. The generation computer obtains one record of training data from the training DB (step S1). The generation computer inputs the values of the feature points contained in the training data into the learning model M and obtains the intracardiac pressure output by the learning model M (step S2). The generation computer compares the intracardiac pressure contained in the training data with the intracardiac pressure output by the learning model M and adjusts parameters such as the weights between the neurons that make up the learning model M so that the difference between the two values is small (step S3). A method such as backpropagation is used to adjust the parameters. The generation computer determines whether there is unprocessed training data (step S4). If the generation computer determines that there is unprocessed training data (YES in step S4), the process returns to step S1 and processes the unprocessed training data. If the generation computer determines that there is no unprocessed training data (NO in step S4), it stores the adjusted parameters (step S5) and terminates the process. The parameters characterizing the learning model M are stored in the auxiliary memory unit 13 of the server 1.

Next, patient monitoring using the monitoring system 100 will be described. Figure 14 is a flowchart showing an example of the monitoring procedure. The patient uses the measuring device 2 to measure their electrocardiogram, heart sounds, and pulse wave (step S11). The waveform obtained as the measurement result is sent to the server 1 and stored in the waveform DB 133. The control unit 11 of the server 1 extracts feature points from the heart sound waveform (step S12). The control unit 11 estimates the intracardiac pressure (step S13). The control unit 11 inputs the extracted feature points into the learning model M and obtains the estimated value of intracardiac pressure output by the learning model M. The control unit 11 associates the estimated value with the feature points and stores it in the result DB 134. At the doctor's instruction, the medical professional terminal 4 displays the heart sound waveform, feature points, and estimated intracardiac pressure. The doctor refers to the displayed content and takes the necessary measures (step S14). The control unit 11 stores the measures, etc. (step S15). Monitoring then ends. Measures that doctors can take include, for example, sending a message to the patient encouraging them to visit a doctor and undergo detailed examinations, or changing the medications they have prescribed for the patient.

Figure 15 is a flowchart showing an example of the measurement process procedure. The measurement process is initiated when a measurement command is issued after the sensor unit 21 of the measuring device 2 has been placed or fixed on the patient's body. The control unit 221 of the measuring device 2 synchronously measures the electrocardiogram, heart sounds, and pulse waves via the sensor unit 21 (step S21). The control unit 221 determines whether the measurement has ended normally (step S22). The control unit 221 may determine that the measurement has not ended normally, for example, in the following cases: when the sensor unit 21 has become detached from the body during measurement, or when a predetermined percentage (e.g., 90%) or more of the measurement data has not been obtained. Note that the function of determining whether a measurement has ended normally in medical measuring devices such as electrocardiographs is well known, so detailed explanation of the determination method will be omitted.

If the control unit 221 determines that the measurement has completed successfully (YES in step S22), it transmits the measurement data to the communication terminal 3 (step S23). The control unit 221 then terminates processing. The control unit 31 of the communication terminal 3 receives the measurement data (step S24). The control unit 31 adds information such as the patient ID and measurement date and time to the measurement data and transmits it to the server 1 (step S25). The server 1 stores the received information in the waveform DB 133.

If the control unit 221 determines that the measurement did not end normally (NO in step S22), it determines whether or not to perform re-measurement (step S26). For example, if the measurement has already been repeated a predetermined number of times or more, the control unit 221 determines not to perform re-measurement. If the control unit 221 determines that re-measurement should be performed (YES in step S26), it returns the process to step S21.

If the control unit 221 determines not to perform remeasurement (NO in step S26), it sends a notification to the communication terminal 3 that the measurement was not successful (step S27). The control unit 221 then terminates the process. The control unit 31 of the communication terminal 3 receives the notification (step S28). The control unit 31 displays the notification on the display unit 351 (step S29). The control unit 221 of the measurement device 2 may also determine whether the measurement is being performed successfully for each heartbeat, and send a notification to the communication terminal 3 if it determines that the measurement is not being performed successfully.

Figure 16 is a flowchart showing an example of the estimation process procedure. The estimation process is a process for estimating intracardiac pressure from the measurement results of the measuring device 2. The control unit 11 of the server 1 acquires one piece of unprocessed waveform data from the waveform DB 133 (step S41). The control unit 11 divides the waveform data into pieces for each heartbeat (step S42). The control unit 11 estimates intracardiac pressure using the data for each beat (step S43). The control unit 11 determines whether to end the process (step S44). If the control unit 11 determines not to end the process (NO in step S44), the process returns to step S41. If the control unit 11 determines to end the process (YES in step S44), the control unit 11 stores the estimated intracardiac pressure value, etc. in the result DB 134 (step S45) and ends the process.

Figure 17 is a flowchart showing an example of the procedure for intracardiac pressure estimation processing. The intracardiac pressure estimation processing corresponds to step S43 in Figure 16. The control unit 11 of the server 1 determines whether there is a measurement abnormality in the waveforms of the electrocardiogram, heart sounds, and pulse wave for one heartbeat (step S61). A measurement abnormality in the waveform means an abnormality caused by the measurement state. For example, the control unit 11 performs pattern matching between the actually measured waveform and each of the abnormal waveform samples measured when various abnormalities occur in the measurement, and determines that there is an abnormality if the waveform is similar to any of the abnormal waveform samples. If the control unit 11 determines that there is no abnormality in any of the waveforms (NO in step S61), it extracts feature points (step S62). The control unit 11 inputs the feature points into the learning model M and obtains the intracardiac pressure output from the learning model M (step S63). The control unit 11 stores the intracardiac pressure in the result DB 134 (step S64). If the control unit 11 determines that there is a measurement abnormality (YES in step S61), it stores the measurement abnormality in the result DB 134 (step S66). After step S64 or step S66 is completed, it determines whether processing of the data acquired in one measurement has finished (step S65). If the control unit 11 determines that processing has not finished (NO in step S65), it returns the processing to step S61. If the control unit 11 determines that processing has finished (YES in step S65), it returns the processing to the caller.

Figure 18 is a flowchart showing an example of the procedure for feature point extraction processing. The feature point extraction processing corresponds to step S62 in Figure 17. The control unit 11 extracts feature points from the waveforms of the electrocardiogram, heart sounds, and pulse wave to be processed, and stores the values of the extracted feature points in the result DB 134 (step S81). The control unit 11 acquires information on comorbid diseases of the patient to be processed from the patient DB 131 (step S82). The control unit 11 compares the acquired comorbid disease information with the comorbid disease DB 132 and determines whether a phenomenon to be detected exists (step S83). If the control unit 11 determines that a phenomenon to be detected exists (YES in step S83), it selects a detection target stored in the detection target column of the comorbid disease DB 132 (step S84). The control unit 11 determines whether correction is necessary (step S85). If the control unit 11 determines that correction is not necessary (NO in step S85), the process proceeds to step S88. If the control unit 11 determines that correction is necessary (YES in step S85), it performs the correction (step S86). The control unit 11 extracts feature points based on the correction results and stores the extraction results in the result DB 134 (step S87). The control unit 11 removes the feature points (step S88). The control unit 11 sets a flag in the result DB 134 for the feature points to be removed. The control unit 11 determines whether there are any unprocessed detection targets (step S89). If the control unit 11 determines that there are any unprocessed detection targets (YES in step S89), it returns the process to step S84. If the control unit 11 determines that there are no phenomena to be detected (NO in step S83) or that there are no unprocessed detection targets (NO in step S89), it returns the process to the caller.

Next, we will explain how to correct the extraction conditions and remove feature points for each detection target. The detection targets are the following eight types: accentuated I (1A), attenuated I (1B), ejected I (split) (1C), accentuated II (2A), attenuated II (2B), split II (2C), paradoxical split II (reversed) (2D), outlier I-I interval or II-II interval, or accentuated I (3). The code combination of numbers and letters in parentheses at the end of each, or the numeric code, corresponds to the detection target code stored in the detection target column of comorbidity DB 132 shown in Figure 7.

The following explains how to correct the extraction conditions for a split I sound ejection sound (1C) or a split II sound (2C). Correction is made to the start of I sound for 1C, and to the start of II sound for 2C. Figure 19 is an explanatory diagram showing the correction method. Hereinafter, the area above the horizontal line in Figure 19 will be referred to as the upper section, and the area below this line will be referred to as the lower section. The upper section of Figure 19 shows an example of a normal cardiac sound waveform. The method for detecting the start of I sound in a normal waveform is as follows: Step 1: Detect the R wave on the electrocardiogram. Step 2: Detect the peak of the cardiac sound waveform (I sound peak) a predetermined time after the R wave is detected (for example, within x milliseconds). Step 3: Count back in time from the peak and determine the start of the amplitude as the I sound start point. In Figure 19, the position of the I sound start point is indicated by a solid line that spans the waveform vertically, and the I sound peak is indicated by a dotted line that spans the waveform vertically. The detection of the II sound start point is similar to the I sound start point.

The bottom row of Figure 19 shows a cardiac waveform indicating an ejection sound I (split). Three waveforms are shown, but they are identical. The top waveform shows an example where the start of I is incorrectly determined when detecting the start of I using the algorithm described above. Because I is split, the start of I is searched for from the latter peak that appears after the true I peak (the falsely detected I peak, indicated by the dotted line straddling the waveform above and below), so the start of the amplitude between the true I peak and the latter peak is incorrectly detected as the start of I (the falsely detected I peak, indicated by the solid line straddling the waveform above and below). A similar incorrect detection occurs for the start of II. The middle waveform shows correction method 1. A search is performed under the assumption that splitting will result in the appearance of two or more waveform peaks (a waveform peak (second peak) in addition to the true first sound peak (first peak)), and the correct position can be selected by setting the amplitude start point before the first peak (true first sound peak) as the first sound start point (true first sound start point, indicated by the solid line straddling the waveform above and below). The waveform below shows correction method 2. By setting the time range for peak detection to y milliseconds, which is shorter than x milliseconds, it is possible to capture the first peak (true first sound peak) and correctly detect the first sound start point (true first sound start point, indicated by the solid line straddling the waveform above and below). The second sound start point can also be correctly extracted using a method similar to that used for the first sound start point.

Correction of extraction conditions for paradoxical split second heart sound (reversal) (2D) will be described. In paradoxical split second heart sound (reversal), the order in which II _A and II _P appear is reversed, and the onset of II A is delayed during inspiration. This is taken into consideration when making corrections. FIG. 20 is a flowchart showing an example of the correction process. The control unit 11 of the server 1 analyzes the waveform of the pulse wave and determines whether a notch can be obtained (step S101). If the control unit 11 determines that a notch can be obtained (YES in step S101), it determines whether the notch and II _A match (step S102). If the control unit 11 determines that the notch and II _A do not match (NO in step S102), it replaces II _A with the notch (step S103) and terminates processing. If the control unit 11 determines that the notch and II _A match (YES in step S102), it terminates processing. If the control unit 11 determines that a notch cannot be acquired (NO in step S101), it determines whether respiration detection is possible (step S104). If the measurement device 2 is equipped with a respiration sensor and can acquire a respiration waveform, or if respiratory variations can be acquired by electrocardiogram analysis, the control unit 11 determines that respiration detection is possible. If the control unit 11 determines that respiration detection is possible (YES in step S104), it determines whether the start of the II sound is delayed by only inspiration (step S105). If the control unit 11 determines that the start of the II sound is delayed by only inspiration (YES in step S105), it reverses the order of appearance of the II _P and II _A (step S106) and ends the process. If the control unit 11 determines that respiration detection is impossible (NO in step S104) or if it determines that the start of the II sound is not delayed by only inspiration (NO in step S105), it ends the process.

The following explains how to correct the extraction conditions for an accentuated I sound (1A) and an accentuated II sound (2A). Figure 21 is an explanatory diagram showing an example of the correction method. In the case of an accentuated I sound and an accentuated II sound, the threshold for determining noise is key. In Figure 21, the phonocardiogram waveform shown in the upper row of the column labeled "Normal" is an example of a waveform measured without noise or patient movement. The phonocardiogram waveform shown in the lower row is an example of a waveform with noise due to external disturbances or patient movement. The two dotted lines indicate the threshold for determining noise. The portion of the waveform whose amplitude exceeds the upper line is determined to be noise due to movement, etc. The portion of the waveform whose amplitude exceeds the lower line is also determined to be noise. The middle column of Figure 21 (the column labeled "Accent I Sound" and "Accent II Sound") is an example of a phonocardiogram waveform for an accentuated I sound and an accentuated II sound. In the case of an accentuated I sound and an accentuated II sound, even if there is no noise, the amplitude is large and the waveform will be determined to be noise. Therefore, as shown in the right column of Figure 21 (the column labeled "Correction Method"), the noise judgment threshold is set broadly to prevent parts of the waveform from being identified as noise, and then feature points are extracted.

The following explains the correction of extraction conditions for a weakened I sound (1B) and a weakened II sound (2B). Figure 22 is an explanatory diagram showing an example of a correction method. In the case of a weakened I sound and a weakened II sound, the threshold for determining noise is also key. In Figure 22, the heart sound waveform shown in the top row of the column labeled "normal" is an example of a waveform measured without baseline noise or environmental sound. The heart sound waveform shown in the bottom row is an example of a waveform containing baseline noise and environmental sound. The two dotted lines indicate the threshold for determining noise. A waveform whose amplitude is sandwiched between the two lines (falls between the two lines) is determined to be noise. The middle column of Figure 22 (the column labeled "weak I sound" and "weak II sound") is an example of a heart sound waveform in the case of a weakened I sound and a weakened II sound. In the case of a weakened I sound and a weakened II sound, the amplitude is small and the waveform is determined to be noise. Therefore, as shown in the right column of Figure 22 (the column labeled "Correction Method"), the noise judgment threshold width is set narrow to prevent the waveform from being judged as noise, and then feature points are extracted.

If the I-I interval or II-II interval is an outlier, or if the I sound is accentuated (3), no correction is performed, and only feature points are removed. An I-I interval or II-II interval is an outlier means that if some of the beats being measured contain a beat that is not in sinus rhythm due to arrhythmia and is out of time, that beat is compared with the intervals of other beats and detected as an outlier beat. Furthermore, for beats that are not in sinus rhythm due to arrhythmia, the I sound of the following beat may be accentuated.

The removal of feature points will now be described. In the cases of accentuated I (1A), attenuated I (1B), and split I (ejection sound) (1C), feature points related to I, i.e., the onset and peak of I, are removed. In the cases of accentuated II (2A), attenuated II (2B), split II (2C), and paradoxical split II (reversed) (2D), feature points related to II, i.e., the onset of II, point II _A , and point II _P, are removed. In the case of accentuated I (3), outliers in the I-I interval or II-II interval, or accentuated I (3), outliers in the I-I interval and the II-II interval, as well as the accentuated I beat, are removed. Removal means that feature points flagged for removal in the DB 134 are not used in the intracardiac pressure estimation process. Therefore, the values of the removed feature points are not used as input to the learning model M in the intracardiac pressure estimation process.

Next, the process of result reference by medical professionals will be explained. Figure 23 is a flowchart showing an example of the procedure for result reference processing. At a predetermined timing, the doctor operates the medical professional terminal 4 to start referencing the estimated intracardiac pressure results. The control unit 41 of the medical professional terminal 4 sends a request for a list of patients to be monitored to the server 1 (step S121). The control unit 11 of the server 1 receives the request (step S122). The control unit 11 creates a patient list by referencing the patient DB 131 and sends it to the medical professional terminal 4 (step S123). The control unit 41 of the medical professional terminal 4 receives the patient list and displays it on the display unit 45 (step S124). The doctor selects a patient to reference from the patient list. The control unit 41 accepts the selection (step S125). The control unit 41 sends the selection information, for example the patient ID of the selected patient, to the server 1 (step S126). The control unit 11 of the server 1 receives the selection information (step S127). The control unit 11 obtains the waveform of the measurement results for the selected patient from the waveform DB 133, and the feature points and estimated intracardiac pressure values from the result DB 134 (step S128). The control unit 11 creates a reference screen (step S129). The control unit 11 sends the created reference screen to the medical professional terminal 4 (step S130). The control unit 41 of the medical professional terminal 4 receives the reference screen (step S131). The control unit 41 displays the reference screen on the display unit 45 (step S132), and the process ends.

Figure 24 is an explanatory diagram showing an example of a result reference screen. The result reference screen d01 includes patient information d011, phonocardiogram waveform d012, and intracardiac pressure d013. The patient information d011 includes the patient ID, patient name, coexisting diseases, and measurement date and time. The phonocardiogram waveform d012 and intracardiac pressure d013 each include three display patterns. The phonocardiogram waveform d012 itself is the same for all patterns, but the position of the superimposed feature points and the display of feature points removed during intracardiac pressure estimation differ. The top row shows the results when feature points are not corrected or removed. The middle row shows the results when feature points are corrected. The bottom row shows the results when feature points are removed. Although not shown, a recommended ranking of the three patterns of intracardiac pressure d013 may also be displayed. The recommended ranking indicates the priority order of which estimated value of intracardiac pressure d013 is most likely to be closest to the true value. The recommendation order is determined based on empirical rules from past data on which method tends to be closest to the true value for each disease or constitution. For example, if the estimated value with correction for aortic stenosis is often closest to the true value, then the recommendation order for the corrected value will be displayed higher. The doctor references the three patterns of results on the results reference screen, checks the position of the feature points superimposed on the cardiac sound waveform, and determines which pattern is most likely. After that, the doctor references the estimated intracardiac pressure and assesses the patient's condition. Then, if necessary, the doctor will take action such as notifying the patient to visit a doctor or changing the medication prescription. The doctor may refer to the recommendation order described above when determining which pattern is most likely.

This embodiment has the following advantages: When extracting feature points from the phonocardiogram waveform, the extraction conditions are changed, such as by correction or removal, based on the type of cause (comorbidity or constitution) that causes abnormal changes in the phonocardiogram waveform, enabling appropriate extraction.

Furthermore, when estimating intracardiac pressure using feature points, intracardiac pressure is estimated using feature points extracted without changing the extraction conditions (no correction or removal), and feature points extracted after changing the extraction conditions (with correction and removal). The cardiac sound waveform, feature points, and intracardiac pressure are then displayed in association with each other for each case. This allows doctors to confirm the most likely of the multiple cardiac sound waveform and intracardiac pressure estimation results, enabling them to accurately assess the patient's condition.

Ultimately, this means that appropriate treatment can be administered before patients experience any noticeable symptoms, preventing the onset of acute heart failure. Furthermore, because patients can receive contact from their doctor before they experience any noticeable symptoms, they can live their daily lives with peace of mind, improving their quality of life (QOL).

(Embodiment 2)
In the first embodiment, the input to the learning model M for estimating intracardiac pressure was limited to the feature points of the cardiac sound waveform. In the present embodiment, indices obtained from the electrocardiogram and pulse waveform are added as input to the learning model M. In the following explanation, the same content as in the first embodiment will be omitted, and differences from the first embodiment will be mainly explained.

14 is performed as follows in this embodiment. The patient uses the measurement device 2 to synchronously measure an electrocardiogram, heart sounds, and pulse waves (step S11). The measurement data for the electrocardiogram, heart sounds, and pulse waves measured by the measurement device 2 is transmitted to the server 1 via the communication terminal 3 and the network N and stored in the waveform DB 133. The control unit 11 of the server 1 extracts feature points (step S12). In this embodiment, in addition to the feature points of the cardiac sound waveform shown in embodiment 1, the Q point of the electrocardiogram and the US point and DN point of the pulse wave are extracted. The control unit 11 estimates the intracardiac pressure (step S13). The control unit 11 inputs the extracted feature points and index values calculated from the feature points of the electrocardiogram and pulse wave into the learning model M, and obtains an estimated value of intracardiac pressure output by the learning model M. The index values will be described later. The control unit 11 associates the estimated values with the feature points and stores them in the result DB 134. The index values may also be stored in the result DB 134. The rest of this process is the same as in embodiment 1, so a detailed explanation will be omitted.

Figure 25 is an explanatory diagram showing another example of learning model M. In addition to the feature points of the cardiac sound waveform, learning model M accepts three indices, PEP, PTT, and STI, as input. Learning model M estimates and outputs an estimated value of intracardiac pressure based on the received feature points and indices. Note that the indices used to estimate intracardiac pressure are not limited to those shown here. Other indices that can derive feature points of the electrocardiogram, cardiac sounds, and pulse wave may also be used.

The three indices are as shown in Figure 10. PEP (Pre-Ejection Period) is the elapsed time from point Q on the electrocardiogram to the peak of the first sound (S1) on the phonocardiogram waveform. PTT (Pulse transition time) is the elapsed time from point Q to point US. STI (Systolic time) is the elapsed time from point US to point DN. The indices indicate the temporal relationship (time difference) between characteristic points of at least two of the following data: electrocardiogram data, phonocardiogram data, and pulse wave data, or the temporal relationship (time difference) between characteristic points of one of the following data: electrocardiogram data, phonocardiogram data, and pulse wave data. All of these indices are commonly used in the field of cardiovascular medicine, so an explanation of their medical meaning will be omitted.

The generation process of the learning model M used in this embodiment will be described with reference to Figure 13. The generation computer 11 obtains one record of training data from the training DB (step S1). The generation computer inputs the values of the feature points contained in the training data, as well as the values of the indicators PEP, PTT, and STI, into the learning model M and obtains the intracardiac pressure output by the learning model M (step S2). The generation computer compares the intracardiac pressure contained in the training data with the intracardiac pressure output by the learning model M and adjusts parameters such as the weights between the neurons that make up the learning model M so that the difference between the two values is small (step S3). The generation computer determines whether there is unprocessed training data (step S4). If the generation computer determines that there is unprocessed training data (YES in step S4), it returns the process to step S1 and processes the unprocessed training data. If the generation computer determines that there is no unprocessed training data (NO in step S4), it stores the adjusted parameters (step S5) and ends the process. Note that the training DB shown in Figure 11 does not store the values of the indicators PEP, PTT, and STI, but these values may be calculated when the data is created and stored in the training DB.

The measurement process and estimation process in this embodiment are similar to those described with reference to Figures 15 and 16 in Embodiment 1, and therefore will not be described again.

The intracardiac pressure estimation process in this embodiment will be described with reference to Figure 17. The control unit 11 of the server 1 determines whether there is a measurement abnormality in the synchronized electrocardiogram, heart sounds, and pulse wave waveforms for one heartbeat (step S61). If the control unit 11 determines that there is no abnormality in any of the waveforms (NO in step S61), it extracts feature points (step S62). The control unit 11 inputs the values of the feature points of the heart sound waveform and the values of the indicators PEP, PTT, and STI into the learning model M to obtain the intracardiac pressure output from the learning model M (step S63). The control unit 11 stores the intracardiac pressure in the result DB 134 (step S64). If the control unit 11 determines that there is a measurement abnormality (YES in step S61), it stores the measurement abnormality in the result DB 134 (step S66). After step S64 or step S66 is completed, it determines whether processing of the data obtained in one measurement has been completed (step S65). If the control unit 11 determines that the process has not ended (NO in step S65), it returns the process to step S61. If the control unit 11 determines that the process has ended (YES in step S65), it returns the process to the caller.

The correction of extraction conditions and removal of feature points are the same as in embodiment 1, but in this embodiment, the following processing is added. If the value of the first sound peak point changes due to a correction of the feature point extraction conditions, the PEP value also changes. Furthermore, if the first sound peak point is removed during feature point removal, the PEP value becomes indefinite and is not input to the learning model M.

In addition to the effects of embodiment 1, this embodiment also provides the following effect: By using the indices PEP, PTT, and STI in addition to feature points obtained from the cardiac sound waveform to estimate intracardiac pressure, it is possible to improve the accuracy of intracardiac pressure estimation.

In the above embodiment, the learning model M is provided by the server 1, but it may also be provided by the communications terminal 3 or medical professional terminal 4. In this case, the communications terminal 3 or medical professional terminal 4 reads out the waveform data and performs the estimation process. At this time, the estimation process may be incorporated into the result reference process, and the communications terminal 3 or medical professional terminal 4 may perform the estimation process for a patient selected by the doctor and display the results on the screen.

The measuring device 2 may also be equipped with a learning model M. In this case, after measuring an electrocardiogram, heart sounds, and pulse rate, the measuring device 2 performs estimation processing. The measuring device 2 transmits waveform data and estimation results to the server 1 via the communication terminal 3. Alternatively, the communication terminal 3 may also be equipped with a learning model M. In this case, after measuring an electrocardiogram, heart sounds, and pulse rate, the communication terminal 3 performs estimation processing. The communication terminal 3 transmits waveform data and estimation results to the server 1.

The technical features (constituent elements) described in each embodiment can be combined with each other, and by combining them, new technical features can be formed.
The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.
In addition, the claims are written in a format in which a claim cites two or more other claims (multiple claim format), but this is not limited to this. Multiple claims that cite at least one other multiple claim (multi-multi claim format) may also be written.

100 Monitoring system 1 Server 11 Control unit 12 Main memory unit 13 Auxiliary memory unit 131 Patient DB
132 Comorbidity Database
133 Waveform DB
134 Results DB
M Learning model 14 Communication unit 15 Reading unit 1P Control program 1a Portable storage medium 1b Semiconductor memory 2 Measuring device 21 Sensor unit 211 ECG sensor 212 Heart sound sensor 213 Pulse wave sensor 22 Processing unit 221 Control unit 222 Main memory unit 223 Auxiliary memory unit 224 Communication unit 3 Communication terminal 31 Control unit 32 Main memory unit 33 Auxiliary memory unit 34 Communication unit 35 Touch panel 351 Display unit 352 Input unit 4 Medical professional terminal 41 Control unit 42 Main memory unit 43 Auxiliary memory unit 44 Communication unit 45 Display unit 46 Input unit 4P Control program 5 Electronic medical record system B Bus N Network

Claims (13)

Obtaining health status information, including disease information, of a patient;
Identifying a disease or predisposition based on the health condition information;
acquiring a cardiac waveform of the patient;
setting conditions for extracting feature points of the phonocardiogram waveform based on the disease or constitution;
An information processing program that causes a computer to execute a process of extracting feature points of the cardiac sound waveform based on the set extraction conditions. The information processing program according to claim 1 , wherein the extraction condition is to recognize that there are two or more peaks in the cardiac sound waveform and select the first peak, or to change the time range for extracting the feature points. The information processing program according to claim 1 , wherein the extraction condition is to change the order in which the feature points appear or to replace the feature points with other feature points. The information processing program according to claim 1 , wherein the extraction condition changes a threshold value of an amplitude at which the cardiac waveform is determined to be noise. The information processing program according to claim 1 , wherein the feature points are discarded. The information processing program according to claim 1 , wherein the health condition information is obtained from an electronic medical record. The information processing program according to claim 1 , wherein the disease includes valvular disease or arrhythmia. Estimating intracardiac pressure based on the extracted feature points;
The information processing program according to claim 1 , further comprising: outputting the estimated intracardiac pressure. Acquire an electrocardiogram and pulse waveforms synchronized with the cardiac sound waveform;
extracting characteristic points from the acquired electrocardiogram and pulse waveform;
The information processing program according to claim 1 , further comprising estimating an intracardiac pressure based on the characteristic points of the phonocardiogram waveform and the characteristic points of the electrocardiogram and the pulse waveform. 10. The information processing program according to claim 9, wherein the disease causes an abnormal change in the morphology of the phonocardiogram waveform, the abnormal change including the splitting of one or two heart sounds in the phonocardiogram waveform, and the extraction conditions are set so as to extract the start points of the first sound and the second sound on the premise that two or more waveform peaks appear due to the splitting, or to extract the start points of the first sound and the second sound from an R wave extracted from the electrocardiogram within a time range that takes the split into consideration. estimating intracardiac pressure based on the characteristic points of the phonocardiogram waveform and the characteristic points of the electrocardiogram and pulse waveform, which are extracted assuming that there is no disease or predisposition that causes an abnormal change in the morphology of the phonocardiogram waveform;
The information processing program according to claim 9 , further comprising: outputting the estimated intracardiac pressure. Obtaining health status information, including disease information, of a patient;
Identifying a disease or predisposition based on the health condition information;
acquiring a cardiac waveform of the patient;
setting conditions for extracting feature points of the phonocardiogram waveform based on the disease or constitution;
An information processing method in which a computer executes a process of extracting feature points of the cardiac sound waveform based on the set extraction conditions. An information processing device including a control unit,
The control unit
Obtaining health status information, including disease information, of a patient;
Identifying a disease or predisposition based on the health condition information;
acquiring a cardiac waveform of the patient;
setting conditions for extracting feature points of the phonocardiogram waveform based on the disease or constitution;
An information processing device that executes a process of extracting feature points of the cardiac sound waveform based on the set extraction conditions.