Detection device

Abstract

To provide a detection device or the like capable of appropriately detecting worsening of heart failure of a user.SOLUTION: A detection device includes: a biological signal acquisition section for acquiring a biological signal of a user; and a control section. The control section performs: calculating one or more parameter values related to a respiration of the user based on the biological signal acquired from the biological signal acquisition section; and detecting that a symptom of aggravation in a cardiac failure of the user exists when a cumulative value of a difference from the previous day exceeds a predetermined threshold among the calculated parameter values.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a detection device and the like. [Background technology]

[0002] 2. Description of the Related Art There are known inventions that determine whether or not a user has an abnormality based on the user's biometric information values ​​and the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-97828 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a detection device and the like that can appropriately detect signs of worsening heart failure in a user. [Means for solving the problem]

[0005] The detection device of the present disclosure is a detection device that includes a biosignal acquisition unit that acquires a user's biosignal, and a control unit, wherein the control unit calculates one or more parameter values ​​related to the user's respiratory heart rate based on the biosignal acquired from the biosignal acquisition unit, and when the cumulative value of the difference data from the previous day among the calculated parameter values ​​exceeds a predetermined threshold, the control unit detects that the user's heart failure is showing signs of worsening. [Effects of the Invention]

[0006] According to the present disclosure, it is possible to appropriately detect signs of worsening heart failure in a user based on biological information acquired from the user. [Brief explanation of the drawings]

[0007] [Figure 1]FIG. 1 is a diagram for explaining the overall configuration of a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating the functional configuration of hardware in the first embodiment. [Figure 3] FIG. 2A is a diagram for explaining the functional configuration of software in the first embodiment, and FIG. 2B is a diagram showing an example of a threshold value table. [Figure 4] FIG. 4 is a diagram illustrating an example of parameter values ​​in the first embodiment. [Figure 5] 4 is an operational flow for explaining processing in the first embodiment. [Figure 6] 4 is an operational flow for explaining processing in the first embodiment. [Figure 7] 4 is an operational flow for explaining processing in the first embodiment. [Figure 8] FIG. 4 is a diagram illustrating an example of parameter values ​​in the first embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example according to the first embodiment. [Figure 10] FIG. 2 is a diagram illustrating an example according to the first embodiment. [Figure 11] 10 is an operational flow for explaining processing in the second embodiment. [Figure 12] FIG. 10 is a diagram illustrating an example in the second embodiment. [Figure 13] FIG. 10 is a diagram illustrating an example in the second embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example in the second embodiment. [Figure 15] FIG. 10 is a diagram illustrating an example in the third embodiment. [Figure 16] FIG. 10 is a diagram illustrating an example in the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, one embodiment of the present invention will be described with reference to the drawings. Specifically, a case where the abnormality determination device of the present invention is applied will be described, but the scope of application of the present invention is not limited to this embodiment.

[0009] Generally, systems that predict changes in physical condition using sleep monitoring devices are known, and these systems are capable of detecting changes in the physical condition of the subject, not just heart failure.

[0010] In recent years, the number of heart failure patients has increased due to the super-aging population, and a heart failure pandemic is predicted in Japan. Non-specialists are expected to increasingly be examining heart failure patients as their family doctors. With the number of support staff per patient decreasing due to a labor shortage across society, there is a demand for support systems that can monitor patients and detect signs of worsening conditions.

[0011] However, although heart failure patients self-check their symptoms by recording their subjective symptoms and recording their daily blood pressure and weight measurements in a heart failure notebook, it can be difficult to detect signs of worsening heart failure due to variations in the patient's own subjective opinion and the level of their ability to record and manage daily events, as well as the lack of knowledge and experience of non-specialist physicians.

[0012] In order to solve such problems, a system and an apparatus that more appropriately detect and notify a patient user of signs of worsening heart failure will be described using the following embodiments.

[0013] [1. First embodiment] [1.1 Overall System] FIG. 1 is a diagram illustrating an overall overview of a detection system 1 to which the abnormality determination device of the present invention is applied. As shown in FIG. 1, the detection system 1 has a detection device 10 that detects signs of worsening heart failure in, for example, a patient user P. The detection device 10 may be configured to include a detection device 12 placed between the floor of a bed 3 and a mattress 5, and a processing device 14 for processing values ​​output from the detection device 12. Alternatively, the detection device 10 may be configured, for example, as a standalone detection device 12 having the functions of the processing device 14.

[0014] When a user (hereinafter referred to as "user P" as an example) sits on the mattress 5, the detection device 12 detects body vibrations (vibrations emitted from the human body) as a biosignal of the user P. Then, based on the detected vibrations, the bioinformation value of the user P is calculated. In this embodiment, the detection device 10 may output and display the calculated bioinformation values ​​(at least respiratory rate, heart rate, and activity level) as the bioinformation value of the user P. Furthermore, the processing device 14 may be a general-purpose device, and is not limited to an information processing device such as a computer, and may be configured as a device such as a tablet or smartphone.

[0015] The user may be a person with heart failure who is currently undergoing medical treatment or who needs nursing care, or may be an elderly person or a child who does not need nursing care.

[0016] Here, the detection device 12 is formed in a sheet shape so as to be thin. As a result, even if it is placed between the bed 3 and the mattress 5, it can be used without causing discomfort to the user P, and therefore it is possible to measure the biometric information values ​​in bed for a long period of time. In other words, the biometric information values ​​and the like are acquired as the state of the user when the user is lying down and at rest.

[0017] The detection device 12 only needs to acquire biosignals (body movement, respiratory movement, ballistocardiogram, etc.) of the user P. In this embodiment, the heart rate and respiratory rate are calculated based on body vibrations, but it is also possible to use, for example, an infrared sensor for detection, acquire the biosignals of the user P from captured images, or use an actuator with a strain gauge. Furthermore, by using a built-in acceleration sensor, it may be realized by, for example, a smartphone or tablet placed on the bed 3 (or mattress 5).

[0018] The bed 3 is installed in various locations. For example, the bed 3 may be installed in the user's home, in a hospital where the user is hospitalized, or in a facility where the user resides.

[0019] The detection device 10 can also communicate with other devices via a network NW. Of the detection device 10, the detection device 12 may be connected to the network NW via, for example, a processing device 14, or the detection device 12 may be directly connected to the network NW via an access point 30 using a wireless LAN or the like. The detection device 12 may also be directly connected to the network NW by incorporating a communication module capable of communicating with a mobile communication network (LTE / 4G / 5G / 6G, etc.), for example.

[0020] For example, a server device 40 and a terminal device 50 can be connected to the network NW. The server device 40 may store, for example, biometric information values ​​acquired by the detection device 10. The server device 40 may also be, for example, an electronic medical record server that stores disease information of users.

[0021] The terminal device 50 may be, for example, an information processing device such as a smartphone, tablet, or laptop computer used by medical staff such as doctors and nurses. The terminal device 50 may also be an information processing device used by facility staff, family members, etc. The terminal device 50 may also be an information processing device used by the patient himself / herself for self-checking.

[0022] [1.2 Functional Configuration] Next, the functional configuration of the detection device 10 in the detection system 1 will be described with reference to Fig. 2 and Fig. 3. The detection device 10 in this embodiment includes a detection device 12 and a processing device 14, and each functional unit (processing) may be realized by either unit except for the biological signal acquisition unit 400. In other words, the detection device 10 functions by combining these units.

[0023] The detection device 10 may perform a notification (alert) operation after detecting a symptom of worsening heart failure in the user. At this time, the notification may be sent to staff, the patient himself / herself, or a family member. The notification method may be a simple notification (alert) by sound or screen display, or may be sent to a terminal device by email or the like. The notification (notification) may also be sent to another terminal device or the like.

[0024] [1.2.1 Hardware Configuration] As shown in FIG. 2, the detection device 10 is configured to include one or more of a control unit 100, a memory unit 200 (storage 210, ROM 220, and RAM 230), a biological signal acquisition unit 400, an input unit 600, an output unit 700, an alarm unit 800, and a communication unit 900 as necessary.

[0025] In the case of FIG. 1, the control unit 100, the biological signal acquisition unit 400, and the storage unit are provided in the detection device 12, and the rest may be provided in the processing device .

[0026] The control unit 100 controls the entire detection device 10. The control unit 100 realizes various functions by reading and executing various programs stored in a storage unit 200 (for example, a storage 210 or a ROM 220), which is a storage device. The control unit 100 may be realized by one or more control devices / arithmetic units (CPUs (Central Processing Units), SoCs (System on a Chip)). The control unit 100 may also be configured by a control circuit.

[0027] Various types of information are stored as data in the memory unit 200. The memory unit 200 is generally a device including one or more of a storage 210, a ROM 220, and a RAM 230, and stores data in any of these as needed.

[0028] The storage 210 is a non-volatile storage device capable of storing programs and data. For example, the storage 210 may be configured as a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The storage 210 may also be configured as an externally connectable USB memory or memory card. The storage 210 may also be, for example, a storage area on the cloud.

[0029] The ROM 220 is a non-volatile memory that can retain programs and data even when the power is turned off.

[0030] The RAM 230 is a main memory that is mainly used when the control unit 100 executes processing. The RAM 230 is a rewritable memory that temporarily stores programs read from the storage 210 or the ROM 220, and data including execution results.

[0031] The biosignal acquiring unit 400 acquires biosignals of the user P. In the present embodiment, as an example, body vibration, which is a type of biosignal, is acquired using a sensor that detects pressure changes. The acquired body vibration is then converted by the control unit 100 into bioinformation value data such as respiratory rate, heart rate, and activity level, and output. Furthermore, based on the body vibration data acquired by the biosignal acquiring unit 400, the control unit 100 can acquire the user's lying state (for example, whether the user P is lying down, in bed, out of bed, sitting on the edge of bed, etc.), and can also acquire the sleep state (asleep, awake), as will be described later.

[0032] The biosignal acquiring unit 400 in this embodiment acquires the user's body vibrations using a pressure sensor, and acquires the respiration and heart rate from the body vibrations, but may also acquire the biosignal from a change in the user's center of gravity (body movement) using a load sensor, acquire the biosignal based on the displacement of the body surface or bedding using radar, or acquire the biosignal based on the sound picked up by a microphone. Any sensor may be used as long as it can acquire the user's biosignal.

[0033] That is, the biological signal acquiring section 400 may be connected to a device such as the detection device 12, or may be configured to receive a biological signal from an external device.

[0034] The user inputs various conditions and operations to start measurement through the input unit 600. The input unit 600 may be implemented by any input means such as a hardware key or a software key.

[0035] The output unit 700 is a functional unit for outputting biological information values ​​such as sleep state, heart rate, and respiratory rate, and for notifying abnormalities. The output unit 700 may be a display device such as a display, or may be an alarm device (sound output device) that issues an alarm or the like. It may also be an external storage device that stores data, or a transmission device that transmits data over a communication path, or may be a communication device for reporting to another device.

[0036] The input unit 600 and the output unit 700 may be realized by other devices. For example, they may be realized by using a terminal device (such as a smartphone or tablet used by a user) connected via the communication unit 900. In this case, the terminal device may be capable of executing a program that causes the control unit 100, which will be described later, to realize processing.

[0037] The notification unit 800 issues a notification to the user, etc. For example, the notification unit 800 may be a speaker that outputs sound, or an LED that is a light-emitting device, etc. The notification unit 800 may also issue a notification to another device (for example, a terminal device such as a smartphone of the user, a nurse call, etc.).

[0038] The communication unit 900 communicates with other devices. For example, if the communication unit 900 is a short-range device, the communication unit 900 provides communication using a method such as wireless LAN (or wired LAN) or Bluetooth (registered trademark). The communication unit 900 may also be a device that provides short-range wireless communication such as NFC. The communication unit 900 may also provide communication using a method that allows mobile communication such as 4G / LTE / 5G / 6G. The communication unit 900 may also be an interface (for example, USB) for communicating with other devices.

[0039] [1.2.2 Software Configuration] The software configuration will be described with reference to Fig. 3(a). For example, the control unit 100 realizes each function by executing programs and applications stored in the storage unit 200 (for example, the storage 210, the ROM 220, and the RAM 230).

[0040] The bioinformation value calculation unit 110 calculates bioinformation values ​​(such as respiratory rate, heart rate, and activity level) of the user P. In this embodiment, the bioinformation value calculation unit 110 may extract respiratory components and heart rate components from the body movement acquired by the biosignal acquisition unit 400, and calculate the respiratory rate and heart rate based on the respiratory interval and heart rate interval. Alternatively, the bioinformation value calculation unit 110 may analyze the periodicity of the body movement (by Fourier transform, etc.) and calculate the respiratory rate and heart rate from the peak frequency. The bioinformation value calculation unit 110 may also calculate the activity level. Specifically, the bioinformation value calculation unit 110 may detect body vibrations per sampling unit time from the biosignal acquisition unit 400 and calculate the activity level based on the number of detected body vibrations. The bioinformation value calculation unit 110 may also calculate the activity level from changes in the user's sleeping posture and movement.

[0041] Specifically, the bioinformation value calculation unit 110 continuously measures the output value of the sensor at a sampling period of, for example, 16 times per second (960 times per minute). Predetermined measurement thresholds (upper and lower limits) are set for the sensor measurement values. The bioinformation value calculation unit 110 sequentially outputs the aggregated activity amount (0 to 960). The activity amount is a numerical value (0 to 960) that indicates how much the user P moved their body in bed. The activity amount is related to the frequency and intensity of the user P's body movements. If the activity amount is large, it means that the user P moved their body frequently and vigorously in bed.

[0042] The sleep state determination unit 120 determines the sleep state of the user. For example, the sleep state determination unit 120 determines the sleep state of the user based on the biological signal acquired by the biological signal acquisition unit 400. The sleep state determination unit 120 may determine two sleep states, "awake" and "sleep." Furthermore, the sleep state determination unit 120 may further determine the "sleep" state as "REM sleep" or "non-REM sleep," and may further determine multiple levels (depth of sleep) of the "sleep" state.

[0043] Furthermore, the sleep state determination unit 120 may determine whether the user P is in a sleep state or an awake state based on the magnitude of the amount of activity or the state of change in the amount of activity over time. For example, the sleep state determination unit 120 may not determine the user P as being in an awake state even if there is a temporary body movement. The sleep state determination unit 120 may determine the user P as being in an awake state when the body movement of the user P continues to a certain extent.

[0044] The user status acquisition unit 130 acquires the status of the user. The user status is a general status of the user, and for example, by using a load sensor or the like provided in the bed 3, the status of whether the user is out of bed or in bed is acquired. The user status acquisition unit 130 may also acquire the user's sleeping posture and sleeping position when the user is in bed. In addition to the load sensor or the like, the user status acquisition unit 130 may acquire the user's status based on, for example, a biological signal acquired by the biological signal acquisition unit 400 as described above. The user status may also include whether the user is asleep or awake based on the user's sleep state determined by the sleep state determination unit 120. The user status acquisition unit 130 may acquire whether the user is out of bed or in bed based on the amount of activity.

[0045] The user state detection unit 140 detects the state of the user from parameters such as biometric information values. When the user state detection unit 140 detects that the state of the user is in a predetermined state, the notification unit 800 may output (notify) an alert.

[0046] The user state detection unit 140 in this embodiment can detect whether the user has signs of worsening heart failure from the values ​​of parameters obtained from biological information.

[0047] The disease information acquisition unit 150 acquires disease information, which is information related to the user's disease. The disease information acquisition unit 150 may, for example, connect to an electronic medical record server and acquire the user's disease information. The disease information acquisition unit 150 may also acquire disease information by referring to information input by a doctor or the like. In this embodiment, the disease information acquisition unit 150 acquires, as disease information, whether the user has a disease related to heart failure.

[0048] The storage 210 also stores biometric information data 202, user status data 204, and a threshold table 206, and reserves an area for a parameter buffer area 208.

[0049] The bioinformation data 202 stores, for example, respiratory rate and heart rate as information related to the bioinformation value calculated by the control unit 100 from the acquired biosignal (body movement). In this embodiment, the respiratory rate, heart rate, and body movement are stored as information related to the bioinformation value, but it is sufficient to store at least one of them. Furthermore, other information (for example, a respiratory event index based on the variation in respiratory amplitude, or a periodic body movement index based on the periodicity of body movement) may also be stored as long as it is a bioinformation value that can be calculated by the bioinformation value calculation unit 110. Furthermore, it is preferable that the bioinformation data 202 is stored in chronological order at predetermined time intervals.

[0050] The user status data 204 stores the status of the user. The user status acquired by the user status acquisition unit 130 is stored as whether the user is "in bed" or "out of bed." Furthermore, the user status data 204 may also store the sleep state determined by the sleep state determination unit 120 as the user status. For example, when the user status acquisition unit 130 determines that the user is "in bed," the sleep state determined by the sleep state determination unit 120 may be stored. Furthermore, it is preferable that the user status data 204 be stored in chronological order at predetermined time intervals.

[0051] The threshold value table 206 is a table that stores threshold values ​​that are used by the control unit 100 to compare with parameters in order to detect whether there are signs of worsening heart failure. An example of the threshold value table 206 is shown in FIG.

[0052] In this embodiment, the parameters used to determine signs of worsening heart failure in a user include the average respiratory rate, respiratory rate variability, average heart rate, heart rate variability, and heart rate non-calculation rate. Biometric information is used to calculate the values ​​of each parameter. In this embodiment, biological information related to respiration and heart rate is used, and the respiratory rate and heart rate are used as biological information values. In this embodiment, the control unit 100 uses data output every minute for the respiratory rate and heart rate, but it is sufficient to be able to calculate the average respiratory rate and heart rate over an evaluation period, and it is sufficient for the variability in the respiratory rate and heart rate to have a resolution that allows for calculation of variability over a period of at least about 10 minutes.

[0053] Furthermore, the interval for calculating the parameter values ​​(calculation interval, evaluation interval) may be, for example, the following interval 1, which corresponds to one time (one day).

[0054] The time from when the user goes to bed to when they wake up. For example, the time the user is in bed 3. The time the user's biometric information is collected.

[0055] The time during which the user is asleep. For example, the time during which the user's biometric information is detected when the user is asleep. Note that this may include or exclude the time during which the user wakes up during the night.

[0056] - A predetermined interval such as 10 minutes or 1 hour. In this case, data can be acquired at any time interval, and once measurements for one day (for example, 12 hours or 24 hours including night) have been completed, the interval can be re-determined as overnight data, and the parameters can be recalculated.

[0057] - Predetermined time periods, such as from 11pm to 6am, or from 12pm to 12am the next day.

[0058] The parameters calculated by the control unit 100 are, for example, the following parameters. (1) Average value This is the average value of the user's biometric information. In this embodiment, the average value of the user's calculated biometric information for one day is used. The biometric information values ​​used are the respiratory rate and heart rate.

[0059] (2) Variation The variation in the user's biometric information values ​​is used. The biometric information values ​​used are the variation in respiratory rate and heart rate.

[0060] Here, the variation is calculated using the following formula (Equation 1).

[0061]

number

[0062] N: a number (integer value, decimal places omitted) obtained by dividing the calculation interval by the calculated number of epochs into which the calculation interval is divided. For example, in this embodiment, this is set to the user's bedtime divided by 10 minutes (1 epoch = 10 minutes). Pn max: The maximum value of the parameter in the nth epoch. Pn min: The minimum value of the parameter in the nth epoch.

[0063] This allows the variation in heart rate and respiratory rate over a day (for example, while in bed) to be calculated. In addition to the above, a value of variation calculated using the standard deviation or standard error over the evaluation time may also be used.

[0064] (3) Heart rate non-calculation rate The heart rate non-calculation rate is the percentage of times within a predetermined period when the reliability of the heart rate calculated using a measuring device was evaluated to be low and the heart rate could not be calculated. The control unit 100 determines whether the reliability of the calculated heart rate is high (step S108). Whether the calculated heart rate is high or low may be determined using the reliability evaluation method described in, for example, Japanese Patent Application Laid-Open No. 2017-47211 (filing date: September 1, 2016, invention title: bioinformation output device, bioinformation output method, and program) or Japanese Patent Application Laid-Open No. 2017-42386 (filing date: November 30, 2017, invention title: abnormality determination device and program used therefor). One example of a method for calculating the heart rate non-calculation rate is calculated using the formula shown in the following (Equation 2).

[0065]

number

[0066] The control unit 100 does not need to calculate all of these parameters. However, it is preferable that the control unit 100 considers the average respiratory rate as an essential parameter. By combining multiple parameters, it is possible to increase the accuracy of detecting signs of worsening heart failure.

[0067] The control unit 100 may also weight these parameters. For example, the control unit 100 may set a coefficient for each parameter and use the sum of the parameters multiplied by the coefficient to detect signs of worsening heart failure in the user.

[0068] The parameter buffer area 208 is an area where data is temporarily stored when determining whether heart failure is worsening based on parameters. While the biological information data 202 is stored as daily data in chronological order, the buffer area temporarily stores parameters used in processing. For example, FIG. 4 is a diagram showing an example of the parameter buffer area 208. The parameter value of a certain parameter on the current day, as well as the parameter values ​​one day prior, two days prior, three days prior, and four days prior are stored. The parameter buffer area 208 also stores the parameter value and the cumulative difference from the previous day, the difference two days prior, and the difference from the previous day. The parameter buffer will be described in detail later.

[0069] [1.3 Processing flow] The process of detecting signs of worsening heart failure in a user in this embodiment will be described.

[0070] [1.3.1 Overall flow] (1) Main processing 5 is an operational flow illustrating the process executed by the control unit 100 when estimating the user's condition. First, the control unit 100 executes a disease information acquisition process to acquire disease information of the user (S102). Here, the control unit 100 preferably executes this process for a user who has heart failure.

[0071] The control unit 100 acquires the biometric information value (S104). Note that although the control unit 100 acquires the biometric information value using the detection device 12, it may also acquire the biometric information value using, for example, another wearable terminal device.

[0072] Next, the control unit 100 executes parameter output processing at a predetermined timing (S106). The timing at which the control unit 100 calculates the parameter value has been described above, but the parameters may be output at a predetermined timing, for example, every 24 hours, or at a predetermined timing, such as when the user wakes up (parameter value calculated based on the bedtime).

[0073] Then, when the control unit 100 determines that the user has heart failure based on the acquired disease information of the user (S108; Yes), it executes an alert determination process (S110).

[0074] (2) Alert determination process, cumulative value calculation process The alert determination process is executed with reference to Fig. 6. The control unit 100 executes the cumulative value calculation process to calculate the cumulative value for each parameter (S122).

[0075] The cumulative value calculation process will now be described with reference to Fig. 7. Specific calculation of values ​​will be described using the values ​​stored in the buffer in Fig. 4 as an example.

[0076] As shown in the buffer of Fig. 4, parameter values ​​are first calculated and stored. For example, the control unit 100 calculates parameter values ​​for the current day (based on the calculation time, including the day on which the calculation was made, or the days up to 24 hours before the calculation time, or the days including the time spent in bed from waking up on the calculation day until going to bed the day before) and stores them on the current day. The control unit 100 also stores the parameter values ​​stored on the current day one day before. Similarly, parameter values ​​for up to four days before are stored in the buffer for each parameter.

[0077] For example, the control unit 100 defines the day of interest as the target of the calculation (based on the calculation time, a day that includes the calculated day, or a day up to 24 hours before the calculation time, or a day that includes the time spent in bed from waking up on the calculated day until going to bed the day before) as the "current day." The control unit 100 calculates parameter values ​​related to the "current day" and stores them as "current day data." At that time, the control unit 100 re-stores the parameter values ​​originally stored as "current day data" as "data one day ago."

[0078] As described above, the parameter values ​​are calculated for the average respiratory rate, the respiratory rate variability, the average heart rate, the heart rate variability, and the heart rate daily calculation rate. Fig. 4 shows the value of one of these parameters. The following explanation uses the value of one parameter as an example.

[0079] The control unit 100 calculates the difference between each parameter and the previous day for each parameter (S142). For example, if the current day is 4 / 5, the control unit 100 calculates the difference between the parameter value "18.5" as the current day data and the parameter value "18.1" as the data from one day ago calculated on 4 / 4 as the difference data for the previous day of "+0.4".

[0080] Alternatively, the control unit 100 may calculate the difference two days ago. For example, if the current day is 4 / 5, the control unit 100 calculates the difference between the parameter value "18.5" on the current day and the parameter value "18.4" on 4 / 3, two days ago, as the difference data two days ago of "+0.1."

[0081] As shown in Fig. 4, the control unit 100 sequentially calculates the difference data for the previous day and the difference data for two days prior. The control unit 100 may calculate the difference data for the previous day and the difference data for two days prior at any time. For example, the control unit 100 may calculate the values ​​when needed in the following processing, or may calculate them all at once in S142.

[0082] Returning to FIG. 7, the control unit 100 determines whether the difference data for the previous day calculated for the current day being evaluated is equal in sign to the cumulative value of the difference data for the previous day (one day before) calculated with the current day being the target of evaluation (S144). Here, "equal in sign" refers to, for example, the positive and negative signs of the values ​​being equal, such as "+" and "+" or "-" and "-". In other words, the control unit 100 determines whether the parameter value is monotonically increasing (or monotonically decreasing). Furthermore, in this embodiment, "equal in sign" refers to any state other than when the positive and negative signs are different (for example, from positive to negative, or from negative to positive). Therefore, a value of zero is considered to be included in the cases where the positive and negative signs are equal.

[0083] Then, when the positive and negative signs of the previous day difference data calculated for the current day are equal to the cumulative value of the previous day difference data (one day ago) that has already been calculated for one day ago, the control unit 100 stores the value obtained by adding the previous day difference data for the current day to the cumulative value of the previous day difference data (one day ago) that has already been calculated for one day ago as the cumulative value of the previous day difference data for the current day (S146).

[0084] If the positive and negative signs of the difference data for the current day are different from the cumulative value of the difference data for the day before (S144; No), the control unit 100 may make the following corrections to detect signs of long-term worsening heart failure, which gradually worsens with repeated ups and downs, earlier or longer: It determines whether the difference between the current day data of interest and the data recorded two days before is positive or negative and whether the cumulative value of the difference data for the day before calculated two days before is the same (S148). That is, the control unit 100 compares the parameter values ​​from two days before with those at the time to understand the trend of change, and determines whether the data from one day before was an outlier (irregular value) that stands out from the trend of change. If the data from one day before is determined to be an outlier, it modifies the parameter value.

[0085] When the control unit 100 determines that correction is necessary because the condition of S148 is met (S148; Yes), it rewrites the cumulative value of the difference from one day before to the cumulative value of the difference from two days before (S152).Then, the control unit 100 stores the value obtained by adding the difference from two days before on the current day to the cumulative value of the difference from two days before on the current day as the cumulative value of the difference from the previous day on the current day (S154).

[0086] Furthermore, if the difference two days before the current day is different in sign from the cumulative value of the difference two days before, the difference from the previous day on the current day is stored as the cumulative value of the difference from the previous day on the current day.

[0087] That is, by executing the cumulative value calculation process, the control unit 100 appropriately calculates the cumulative value used to appropriately determine the trend (whether the parameter value is increasing or decreasing) of the parameter value.

[0088] In the above, we have explained the case where the processing of S148 is performed when the positive and negative signs of the previous day difference data for the current day are different from the cumulative value of the previous day difference data for one day before, and it is determined that the parameter value is not monotonically increasing (S144; No).However, in addition to this, the cumulative value may be reset and the previous day difference data for the current day may be newly stored as the cumulative value of the previous day difference data for the current day.

[0089] 6, the control unit 100 determines whether the cumulative value of the difference data from the previous day has changed for any one of the parameters (S124). Specifically, the control unit 100 determines whether the cumulative value has exceeded or fallen below the threshold value stored in the threshold value table 206.

[0090] Then, when any one of the calculated parameters matches the upper limit, the control unit 100 determines that the user has a sign of worsening heart failure and issues an alert (S124; Yes → S126).Furthermore, when the cumulative values ​​of the difference data from the previous day for all parameters are within the thresholds, the control unit 100 determines that the user has no sign of worsening heart failure (S124; No → S128).

[0091] Furthermore, when issuing an alert in S126, the control unit 100 may notify a terminal device (smartphone, computer, tablet, etc.) of the subject, their family, or their primary care physician. The control unit 100 may also transmit the presence of signs of worsening heart failure to an electronic medical record server or output the information to a linked digital heart failure notebook. When signs of worsening heart failure are present, the control unit 100 may notify a call center or the like, call the user to advise them to go to a hospital, or connect them to telenursing.

[0092] [1.4 Operational examples and working examples] A specific example of the operation of the cumulative value calculation process will be described using the parameter values ​​in FIG.

[0093] (1) 4 / 2 If the base date (today) is 4 / 2, the difference between the parameter value "18.3" on 4 / 2 and the parameter value "17.9" ​​on the previous day, 4 / 1, is "+0.4". Also, the cumulative value of the difference data for the previous day is "+0.4" as of 4 / 2.

[0094] (2) 4 / 3 If the base date (today) is 4 / 3, the difference between the parameter value "18.4" on 4 / 3 and the parameter value "18.3" on the previous day, 4 / 2, is "+0.1." Also, the difference between the parameter value "18.4" on 4 / 3 and the parameter value "17.9" ​​on 4 / 1, two days earlier, is "+0.5."

[0095] Furthermore, the cumulative value of the difference data from the previous day, 4 / 3, "+0.1", and the cumulative value of the difference data from the previous day, 4 / 2, one day earlier, "+0.4", are both positive and negative "+". Therefore, "+0.1" and "+0.4" are added together, and the cumulative value of the difference data from the previous day, 4 / 3, becomes "+0.5".

[0096] (3) 4 / 4 If the base date (today) is 4 / 4, the difference between the parameter value "18.1" on 4 / 4 and the parameter value "18.4" on the previous day, 4 / 3, is "-0.3." Also, the difference between the parameter value "18.1" on 4 / 4 and the parameter value "18.3" on 4 / 2, two days earlier, is "-0.2."

[0097] Here, the sign of the difference data for the previous day on 4 / 4, "-0.3", is different from the cumulative value of the difference data for the previous day from one day before, "+0.5". Therefore, when the difference data for two days before on 4 / 4, "-0.2", is compared with the difference data for two days before on 4 / 4, "+0.5", the sign is different. Therefore, the control unit 100 stores the difference data for the previous day on 4 / 4, "-0.3", as the cumulative value of the difference data for the previous day on 4 / 4.

[0098] (4)4 / 5 If the base date (today) is 4 / 5, the difference between the parameter value "18.5" on 4 / 5 and the parameter value "18.1" on the previous day, 4 / 4, is "+0.4." Also, the difference between the parameter value "18.5" on 4 / 5 and the parameter value "18.4" on 4 / 3, two days earlier, is "+0.1."

[0099] Here, the sign of the difference data from the previous day on 4 / 5, "+0.4", is different from the sign of the cumulative value of the difference data from one day before, "-0.3". When comparing the difference data from two days before on 4 / 5, "+0.1", with the cumulative value of the difference data from two days before, "+0.5", they are both "+".

[0100] Therefore, the control unit 100 rewrites the cumulative value of the difference data from the previous day on 4 / 4, which is one day earlier, to the cumulative value of the difference data from the previous day two values ​​earlier, "+0.5". Figure 8 shows the state of the rewritten buffer. As shown by the bold frame in Figure 8, the cumulative value of the difference data from the previous day on 4 / 4 has been rewritten to "+0.5".

[0101] Then, the control unit 100 adds the difference data two days before, "0.1", to the cumulative value of the difference data from the previous day on 4 / 3, which is two days before, and stores the result as the cumulative value of the difference data from the previous day on 4 / 5.

[0102] FIG. 9 plots the transition of parameters. In FIG. 9, the average respiratory rate is used as the parameter. FIG. 9(a) plots the parameter values ​​of the average respiratory rate. FIG. 9(b) is a graph plotting the cumulative value of the difference data from the previous day, and FIG. 9(c) is a diagram in which the processing of this embodiment is applied. FIG. 9(a) shows measurement data of a heart failure patient, and the x-axis shows the number of days until the patient whose data was measured was hospitalized due to worsening heart failure (0, at the right end of the graph, is the day of hospitalization).

[0103] For example, as shown by dashed line P10 in Figure 9(b), the cumulative value of the difference data from the previous day is "-0.3," interrupting the upward trend in the parameter value. In contrast, after correction in Figure 9(c), the cumulative value of the difference data from the previous day is corrected to "1.7," as shown by the dashed line. This results in a continuous upward trend in the parameter value. Therefore, for example, at dashed line P16, 12 days before hospitalization, the cumulative value of the difference data from the previous day is "3," enabling the user's worsening heart failure to be detected. In Figure 9(b), the cumulative value of the difference data from the previous day for the corresponding date, dashed line P12, 11 days before hospitalization, is "3.3," detecting worsening heart failure one day later than in Figure 9(c). Because early detection of worsening heart failure affects prognosis, detecting it even one day earlier is effective. While a certain degree of effectiveness is observed without using a correction method, using a correction method enables earlier detection of long-term parameter changes.

[0104] Similarly to FIG. 9, the operation example of FIG. 10 is a graph based on the average respiratory rate as a parameter. FIG. 10(a) uses the average respiratory rate as a parameter, and plots the parameter values ​​of the average respiratory rate. FIG. 10(b) is a graph plotting the cumulative value of the difference from the previous day as is, and FIG. 10(c) is a diagram in which the processing of this embodiment is applied. In the example of FIG. 10, while FIG. 10(b) cannot detect exacerbation of heart failure, FIG. 10(c) can detect exacerbation of heart failure.

[0105] [1.5 Effects, etc.] In this way, according to this embodiment, the parameters can be used to appropriately detect signs of worsening heart failure in a user. Furthermore, when signs of worsening heart failure are detected, the control unit 100 issues an alert, which enables, for example, a doctor or staff member to become aware of the user's condition more quickly.

[0106] In addition, being able to detect signs of worsening heart failure in users early can lead to early treatment, improving prognosis and preventing a decline in quality of life compared to emergency hospitalization after the onset of the disease.

[0107] The system of this embodiment was verified using 18 cases of patients who were hospitalized due to worsening heart failure and underwent emergency medical treatment (intravenous diuretic injection), and 4 cases of patients who were hospitalized without heart failure. Based on these patients, the parameter thresholds were adjusted so that the sensitivity for 18 cases was 100% and the specificity for 4 cases was 100%.

[0108] As a result, the detection system using this embodiment issued alerts to seven subjects who had never been hospitalized for heart failure for a total of 3,987 days, and the false alarm rate was 4.1%, which was an extremely excellent result.

[0109] It has been suggested that data from daily blood pressure and weight measurements of heart failure patients may not be sufficient to detect signs of worsening heart failure. Direct vital signs are considered an effective indicator for detecting a user's physical condition, particularly respiratory rate for heart failure patients with underlying heart disease. Heart failure patients' subjective tolerance levels for symptom severity vary, and many patients end up hospitalized because they tolerate the symptoms too well. Therefore, in order to detect signs of worsening heart failure, it is effective to monitor using objective indicators and identify the signs of worsening.

[0110] Furthermore, worsening heart failure can occur suddenly or over a long period of several weeks, and the parameters and thresholds for detecting signs of worsening heart failure are different from those for simple changes in physical condition and require specialized parameters.

[0111] Therefore, when the measurement subject (detection subject) is a user (patient) with heart failure, the detection device and detection system of this embodiment are used. The detection device of this embodiment can detect signs of worsening heart failure when the trends in the average respiratory rate, average heart rate, respiratory rate variance, heart rate variance, and heart rate daily calculation rate exceed thresholds.

[0112] [2. Second Embodiment] Next, a second embodiment will be described. In the above-described embodiment, a case where a so-called outlier is determined based on the cumulative value has been described. In this embodiment, a different method is used to determine the overall trend of parameter values.

[0113] The second embodiment has the same hardware configuration and main software configuration as the first embodiment, and only the differences from the first embodiment will be described.

[0114] (1) Correction method using the slope of the regression line The following description will be made with reference to Fig. 11. The control unit 100 calculates the difference from the previous day, etc. for each parameter. Here, the control unit 100 may calculate the difference from the previous day, the difference from two days ago, etc., or may calculate the cumulative value of the difference from the previous day up to the previous day. These calculations have been explained in the first embodiment, so they will not be explained here. Fig. 11 is a flow based on Fig. 7, and the same processes are assigned the same reference numerals and explanations will be omitted.

[0115] Here, the control unit 100 determines whether the previous day difference data for the current day is equal in sign to the cumulative value of the previous day difference data from one day before (S144). If the previous day difference data for the current day is equal in sign to the cumulative value of the previous day difference data from one day before, the control unit 100 adds the cumulative value of the previous day difference data for the current day to the cumulative value of the previous day difference data from one day before and stores the result as the cumulative value of the previous day difference data for the current day (S144; Yes → S146). If the previous day difference data for the current day is not equal in sign to the cumulative value of the previous day difference data from one day before, the control unit 100 calculates the slope of the regression line for a predetermined number of days going back from the current day (for example, three days, five days, etc. including the current day) (S202).

[0116] When the control unit 100 calculates the regression line, it determines whether the cumulative value of the difference data from the previous day on the day farthest from the current day among the specified number of days that is the interval for which the regression line is calculated (for example, in the case of a three-day period including the current day, it is the day two days before, the oldest day compared to the current day. Note that the first day of the specified number of days that is the interval for which this regression line is calculated is hereinafter referred to as the "initial day") is positive or negative and whether the slope of the regression line is positive or negative (S204).

[0117] If the positive and negative values ​​determined in S204 are equal (S204; Yes), the control unit 100 assumes that there has been an approximate increase or decrease from the initial day to the current day among the predetermined number of days used to calculate the regression line, and calculates the difference between the parameters of the initial day and the parameters of the current day.The control unit 100 then adds the calculated difference to the cumulative value of the difference data of the previous day for the initial day, and stores the result as the cumulative value of the difference data of the previous day for the current day (S206).

[0118] If the positive and negative values ​​determined in S204 are not equal (S204; No), the control unit 100 stores the difference between the parameter of the current day and the parameter of the initial day as the cumulative value of the difference data from the previous day of the current day (S208).

[0119] For example, Figure 12(a) is a graph showing the transition of the average value of the respiratory rate, and Figure 12(b) is a graph showing the slope over three days.

[0120] (2) How to calculate the cumulative difference from the previous day after correcting with the moving average The control unit 100 calculates the average value (hereinafter referred to as the moving average value) for each parameter for a predetermined number of days including the current day (for example, for the past three days, the current day, the previous day, and two days before). Figure 13(a) is a graph plotting the average value of the respiratory rate for each day, and Figure 13(b) is a graph plotting the moving average for three days.

[0121] Furthermore, the control unit 100 calculates the difference from the previous day using the moving average value. The control unit 100 calculates the cumulative value of the difference from the previous day for the difference from the previous day calculated from the moving average value. Figure 13(c) is a graph plotting the cumulative value of the difference from the previous day. When the moving average value is used, it is not necessary to make the correction described above when calculating the cumulative difference from the previous day. When the cumulative value of the difference from the previous day calculated for each parameter exceeds a threshold value, it is possible to detect signs of worsening heart failure.

[0122] (3) How to calculate the difference between any two days The control unit 100 calculates the difference between the current day and a predetermined number of days ago (e.g., seven days ago) for each parameter. This calculates the amount of change compared to the predetermined number of days ago. For example, FIG. 14(a) is a graph plotting the average respiratory rate by day, and FIG. 14(b) is a graph showing the difference from seven days ago. Note that in FIG. 14(b), the predetermined number of days is set to seven days ago as an example, but it is preferable to set it to a day more than five days ago because the amount of change is small when the parameter continuously increases and decreases over one or two days. In this case, when the difference calculated for each parameter from the predetermined number of days ago exceeds a threshold, it is possible to detect signs of worsening heart failure.

[0123] 3. Third Embodiment A third embodiment will be described. In the first embodiment, an example was described in which processing was performed for each parameter, and if any parameter exceeded a value, it was detected that there was a sign of worsening heart failure.

[0124] In this embodiment, an embodiment will be described in which the control unit 100 weights the calculated parameters and determines whether heart failure is worsening.

[0125] For example, Fig. 15 is a diagram showing an example of a table that stores a weighting coefficient for each parameter. For example, a parameter that exceeds a threshold may be multiplied by a weighting coefficient of 1, and it may be determined that there are signs of worsening heart failure when the sum of all parameters exceeds 1. For example, in the example of Fig. 15, the coefficients for the respiratory rate and heart rate are 1, so the control unit 100 detects that the user has signs of worsening heart failure when the cumulative values ​​of the difference data from the previous day for at least the respiratory rate and heart rate exceed the threshold.

[0126] Furthermore, since the coefficients of respiratory rate variability, heart rate variability, and heart rate non-calculation rate are 0.5, the control unit 100 does not detect signs of worsening heart failure if the cumulative value of the previous day's difference data for just one of these parameters exceeds the threshold value; the cumulative value of the previous day's difference data for the other parameters must also exceed the threshold value.

[0127] In addition, the control unit 100 may detect that there are signs of worsening heart failure when, in determining whether the cumulative value of the parameter's previous day difference data exceeds the color value, the sum exceeds 1 on the same day or over an arbitrary period (e.g., 3 days).

[0128] The control unit 100 may also perform weighting using other methods. For example, when the cumulative value of the difference data from the previous day exceeds (falls below) the judgment threshold, the control unit 100 calculates a value for each parameter by multiplying the coefficient by the cumulative value by the judgment threshold. Then, when the sum of the calculated values ​​exceeds 1, the control unit 100 detects that the user has a sign of worsening heart failure. Note that at this time, the control unit 100 may evaluate that the greater the total value, the higher the risk of worsening heart failure.

[0129] 16(a) and 16(b) are processing examples explained using specific numerical values. For example, in FIG. 16(a), of the cumulative values ​​of the difference data from the previous day, only the respiratory rate exceeds the judgment threshold. Therefore, the control unit 100 calculates the value as 1 × 5.3 ÷ 3.0 = 1.7. In FIG. 16(a), 1.7 is the total value as it is.

[0130] 16(b), among the cumulative values ​​of the difference data from the previous day, the respiratory rate variability and heart rate variability exceed the judgment threshold. Therefore, the control unit 100 calculates the values ​​as "0.5 x 6 ÷ 4.5 = 0.7" and "0.5 x 20 ÷ 13 = 0.8". The control unit 100 then calculates the sum of the calculated values, 0.7 + 0.8 = 1.5, as the total value.

[0131] As described above, according to this embodiment, the control unit 100 can detect signs of worsening heart failure in the user by combining a plurality of calculated biological information values.

[0132] [4. Modifications] The present disclosure is not limited to the above-described embodiments, and various modifications are possible. In other words, embodiments obtained by combining technical means that are appropriately modified within the scope of the present disclosure are also included in the technical scope.

[0133] Although the above-mentioned embodiments are described separately for convenience of explanation, they can be combined to the extent possible. Furthermore, the present invention intends to obtain rights to any of the technologies described in the specification through amendments or divisional applications, etc.

[0134] In addition, the programs that run on each device in each embodiment are programs that control the CPU, etc. (programs that make a computer function) so as to realize the functions of the above-described embodiments. Information handled by these devices is temporarily stored in a temporary storage device (e.g., RAM) during processing, and then stored in various ROMs and HDDs, and is read, modified, and written by the CPU as needed.

[0135] Here, the recording medium for storing the program may be any of semiconductor media (e.g., ROM, non-volatile memory card, etc.), optical recording media / magneto-optical recording media (e.g., DVD (Digital Versatile Disc), CD (Compact Disc), BD (Blu-ray (registered trademark) Disc), etc.), magnetic recording media (e.g., magnetic tape, flexible disk, etc.), etc.

[0136] Furthermore, when distributing the program in the market, the program can be stored in a portable recording medium and distributed, or transferred to a server computer connected via a network such as the Internet. In this case, the storage device of the server device is also included in the present disclosure.

[0137] Furthermore, the above-mentioned data may not be stored within the device, but may be stored in an external device and called up as needed. For example, the data may be stored in a network attached storage (NAS) or on the cloud.

[0138] The scope of the present disclosure is not limited to the configurations explicitly described in the specification, but also includes combinations of the technologies disclosed in the specification. The configurations of the present disclosure for which a patent is sought are set forth in the appended claims, but it is not intended to exclude them from the technical scope on the grounds that they are not set forth in the claims.

[0139] Furthermore, in the above-mentioned specification, the statements "in the case of" and "when" are given as examples and are not intended to limit the configuration to the described contents. The disclosure also includes configurations that are not in these cases or situations, even if they would be obvious to a person skilled in the art, and the applicant intends to obtain rights to them.

[0140] Furthermore, the processes and data flows described in the specification are not limited to the order in which they are described. For example, the patent also discloses configurations in which some processes are deleted or the order is changed, and the patent holder intends to obtain the rights to such configurations.

[0141] Furthermore, although the functions described in the embodiments are executed by each device, they may be realized by one device or may further utilize an external server.

[0142] Furthermore, each functional block or feature of the device used in the above-described embodiments may be implemented or performed by an electrical circuit, for example, an integrated circuit or multiple integrated circuits. The electrical circuit designed to perform the functions described herein may include a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or a combination thereof. The general-purpose processor may be a microprocessor, or a conventional processor, controller, microcontroller, or state machine. The electrical circuit may be composed of digital circuits or analog circuits. Furthermore, as advances in semiconductor technology emerge that replace current integrated circuits, one or more aspects of the present disclosure may also utilize new integrated circuits based on that technology.

[0143] Furthermore, in this embodiment, the processing device 14 outputs the biological information based on the results output by the detection device 12, but all of the calculations may be performed by the detection device 12. Furthermore, instead of implementing this by installing an application on a terminal device (for example, a smartphone, tablet, or computer), the processing may be performed on the server side, for example, and the processing results may be returned to the terminal device.

[0144] For example, the above-described processing may be performed on the server side by uploading biometric information from the detection device 12 to the server. The detection device 12 may be realized by a device such as a smartphone equipped with an acceleration sensor and a vibration sensor. [Explanation of symbols]

[0145] 1. Abnormality detection system 3. Detection equipment 5 Processing equipment 100 control section 200 storage 300 ROM 310 RAM 400 Biosignal acquisition unit 500 Measurement location acquisition unit 600 Input section 700 Output Section 800 Information Department 900 Communications Department 10 beds 20 Mattress

Claims

1. A detection device including a biosignal acquisition unit that acquires a biosignal of a user and a control unit, The control unit calculating one or more values ​​of parameters related to the user's breathing based on the biological signal acquired from the biological signal acquisition unit; If the cumulative value of the difference data from the previous day among the calculated parameter values ​​exceeds a predetermined threshold, it is detected that the user's heart failure is showing signs of worsening. Detection device.

2. The control unit Calculating the difference between the current day data, which is the value of the parameter on the reference day, and the one day ago data, which is the value of the parameter on the previous day, as the previous day difference data for the reference day; The previous day difference data is cumulatively added up to the reference day to calculate the cumulative value of the previous day difference. The detection device of claim 1 .

3. The control unit When the difference between the current day and the previous day has the same positive or negative sign as the cumulative difference between the previous day and the previous day, calculate the cumulative difference between the previous day and the previous day. The detection device of claim 2 .

4. The control unit When the difference between the current day and the previous day is different in sign from the cumulative value of the difference between the previous day and the previous day, the difference between the current day and the parameter value two days before the current day is compared to determine whether the difference in sign is the same as the cumulative value of the difference between the previous day and the previous day two days before; When the difference in parameter value between the current day and two days before the current day has the same sign as the cumulative value of the difference between the previous day two days before, the cumulative value of the difference between the previous day one day before is rewritten to the cumulative value of the difference between the previous day two days before. The detection device of claim 2 .

5. The control unit Calculating an average respiratory rate and / or a variance in the respiratory rate as the value of the parameter related to the user's breathing The detection device of claim 1 .

6. The control unit The detection device of claim 1 , further comprising: a sensor for detecting a heart rate of the user;

7. The control unit Calculating at least one of the average heart rate, the variance of the heart rate, and the rate at which the heart rate is not calculated as the parameter value related to the user's heart rate The detection device of claim 6.

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