Biological information processing apparatus, biological information processing method, and program

Abstract

To improve measurement accuracy and responsiveness without limiting the cycle of measurable biological information.SOLUTION: A control unit of a bedside terminal analyzes biological information signals of a plurality of slots having different lengths as analysis targets among biological information signals of a subject acquired from a sensor unit, and generates information on periodicity for each slot. Next, the control unit derives a reliability index indicating reliability of the information on the periodicity for each of the plurality of slots. Then, the controller selects the slot having the highest reliability index, and calculates and outputs the biological information of the subject based on the information relating to the periodicity of the selected slot.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a biometric information processing device, a biometric information processing method, and a program. [Background technology]

[0002] Conventionally, there has been known a biological information processing device that uses a sensor to acquire biological information signals related to breathing or heartbeat and analyzes the biological information signals to detect periodicity in order to measure biological information such as respiratory rate and heart rate from a living body. For example, Patent Document 1 describes a method for calculating a frequency (respiratory rate) included in measurement data based on a position autocorrelation function (PACF) calculated from N-dimensional measurement data (biological information signals) over a certain period of time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-175492 Summary of the Invention [Problem to be solved by the invention]

[0004] However, actual breathing may occasionally be disturbed temporarily by coughing, breath-holding, or noise due to body movement. Conventional techniques have had the problem that when a temporary disturbance occurs in the respiratory rhythm, the accuracy of the calculated respiratory rate deteriorates until the noise corresponding to the disturbance falls outside the period of analysis (hereinafter referred to as the slot). For example, if the slot is 30 seconds, the noise is included in the analysis period from the time the noise occurs until 30 seconds have elapsed, resulting in a deterioration in the accuracy of the calculated respiratory rate. In other words, a problem has arisen in that a longer slot lengthens the period during which the respiratory rate cannot be accurately calculated when noise occurs. Furthermore, if the slot is long, any changes in the breathing rhythm cannot be reflected immediately, that is, there is a problem of poor responsiveness. Furthermore, if the slot is long, the respiratory rate cannot be calculated for a certain period immediately after the start of measurement (the period until one slot's worth of data is accumulated), so there was a problem that the longer the slot, the longer the time lag until the first respiratory rate was output.

[0005] On the other hand, if the slot is shortened to solve the above problem, in the case of patients with long respiratory cycles, one respiratory cycle does not fit within the slot, and the respiratory rate cannot be measured.

[0006] Similar problems exist not only in measuring the respiratory rate but also in measuring other biological information such as the heart rate.

[0007] The present invention has been made in view of the above-mentioned problems in the prior art, and has as its object to improve measurement accuracy and responsiveness without limiting the period of measurable biological information. [Means for solving the problem]

[0008] In order to solve the above problems, the biological information processing device according to the present invention comprises: an acquisition means for acquiring a biological information signal of a subject; an analysis means for analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired by the acquisition means, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation means for deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; a selection means for selecting a period with the highest reliability index; an output unit that outputs biological information of the subject calculated based on information about the periodicity of the period selected by the selection unit; Equipped with.

[0009] Further, the biological information processing method according to the present invention comprises: an acquisition step of acquiring a biological information signal of the subject; an analysis step of analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired in the acquisition step as analysis targets, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation step of deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; a selection step of selecting the period with the highest reliability index; an output step of outputting the biological information of the subject calculated based on information about the periodicity of the period selected in the selection step; Equipped with.

[0010] In addition, the program according to the present invention is Computer, an acquisition means for acquiring a biological information signal of a subject; an analysis means for analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired by the acquisition means, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation means for deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; selection means for selecting the period in which the reliability index is highest; an output means for outputting the biological information of the subject calculated based on the information regarding the periodicity of the period selected by the selection means; Function as. [Effects of the Invention]

[0011] According to the present invention, it is possible to improve measurement accuracy and responsiveness without limiting the period of measurable biological information. [Brief explanation of the drawings]

[0012] Advantages and features provided by one or more embodiments of the present invention will be more fully understood from the following detailed description and the accompanying drawings, which are for illustrative purposes only and are not intended to limit the scope of the invention. [Figure 1] FIG. 1 is a system configuration diagram of a biological information monitoring system. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of a station server. [Figure 3] FIG. 2 is a block diagram showing the functional configuration of a bedside terminal. [Figure 4] 10 is a flowchart showing the flow of a respiration rate calculation process executed by the bedside terminal. [Figure 5] 10 is a diagram showing a biological information signal input from a sensor unit, a biological information signal of slot 1, a biological information signal of slot 2, and a biological information signal of slot 3. FIG. [Figure 6] 10A and 10B are diagrams for explaining derivation of a reliability index when information on periodicity is an autocorrelation function. [Figure 7] 10A and 10B are diagrams for explaining derivation of a reliability index when information on periodicity is an autocorrelation function. [Figure 8] 10 is a diagram showing the reliability index of each slot and the highest reliability index when the information about the periodicity is an autocorrelation function. FIG. [Figure 9] FIG. 10 is a diagram for explaining a method for calculating the respiratory rate when information regarding periodicity is an autocorrelation function. [Figure 10] FIG. 10 is a diagram for explaining a time T used in Modification 1. [Figure 11] FIG. 10 is a diagram showing slots 1 to 4 in Modification 1. [Figure 12] FIG. 10 is a diagram illustrating a method for determining a slot length in Modification 2. [Figure 13] FIG. 13 is a diagram showing a slot configuration in Modification 4. [Figure 14] FIG. 13 is a diagram illustrating the use of a spare slot in Modification 4. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present invention will be described with reference to the drawings, but the scope of the invention is not limited to the illustrated examples.

[0014] [Configuration of biological information monitoring system] FIG. 1 shows the system configuration of a biological information monitoring system 100. As shown in FIG. 1, the biological information monitoring system 100 includes a station server 10, a bedside terminal 30 (biological information processing device), and a mobile terminal 60. The biological information monitoring system 100 is used in a medical facility such as a hospital. The station server 10 is provided in a nurse's station. The bedside terminal 30 is provided in each bed in a hospital room. The mobile terminal 60 is carried by a medical professional such as a nurse. The station server 10 and the bedside terminal 30 are capable of mutual data communication via wireless communication. Furthermore, the station server 10 and the mobile terminal 60 are also capable of mutual data communication via wireless communication. It is desirable that the wireless communication within the medical facility use a frequency in the sub-gigahertz band that does not interfere with medical equipment. The number of bedside terminals 30 and mobile terminals 60 is not particularly limited.

[0015] The station server 10 centrally manages the biological information of each patient (subject) collected by the bedside terminal 30. The display unit 13 of the station server 10 displays a monitoring screen for monitoring the biological information of multiple patients. Examples of the biological information include respiratory rate, heart rate, body temperature, SpO2, etc. At the nurse station, the station server 10 can grasp changes in the condition of each patient. The station server 10 also transmits the biological information of each patient to the mobile terminal 60. The station server 10 may also transmit the biological information of each patient to a cloud server or the like connected via a communication network.

[0016] Furthermore, the station server 10 is connected to an external HDD (Hard Disk Drive) 20, a UPS (Uninterruptible Power Supply) 21, and a printer 22. The external HDD 20 stores data such as biological information of each patient managed by the station server 10. The UPS 21 is a device that incorporates a device for storing power, such as a secondary battery, and is capable of supplying power at a predetermined output for a certain period of time even if the external power supply is cut off due to a power outage or the like. The printer 22 prints biometric information on paper, and prints the biometric information and the like displayed on the display unit 13 of the station server 10.

[0017] The bedside terminal 30 is installed at each patient's bedside. The bedside terminal 30 acquires the patient's biological information signal (measurement data) from a sensor unit 40 such as a respiratory sensor or pulse oximeter, and calculates and displays biological information based on the acquired biological information signal. The bedside terminal 30 also reads an IC card 51 to acquire the identification information of a medical professional, and acquires the patient's biological information from a measuring device 52 compatible with an HR (Health Record) joint, such as a thermometer. The bedside terminal 30 transmits the biological information to the station server 10.

[0018] The mobile terminal 60 displays the biological information of each patient transmitted from the station server 10. This allows medical personnel to check changes in the condition of each patient from outside the nurse station. Note that the bedside terminal 30 may be configured to transmit the biological information of each patient directly to the mobile terminal 60.

[0019] [Station Server Configuration] FIG. 2 shows the functional configuration of the station server 10. 2, the station server 10 includes a control unit 11, an operation unit 12, a display unit 13, a wireless communication unit 14, a storage unit 15, a communication unit 16, and I / F (interface) units 17 to 19. The units of the station server 10 are connected by a bus.

[0020] The control unit 11 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc. The control unit 11 comprehensively controls the processing operations of each unit of the station server 10. Specifically, the CPU of the control unit 11 reads out various processing programs stored in the storage unit 15, expands them in the RAM, and performs various processes in cooperation with the programs.

[0021] The operation unit 12 is configured with a keyboard having cursor keys, letter and number input keys, various function keys, etc., and a pointing device such as a mouse. The operation unit 12 outputs operation signals input by key operations on the keyboard or mouse operations to the control unit 11.

[0022] The display unit 13 is configured to include a monitor such as an LCD (Liquid Crystal Display), etc. The display unit 13 displays various screens in accordance with instructions of a display signal input from the control unit 11.

[0023] The wireless communication unit 14 is a wireless interface for transmitting and receiving data to and from the bedside terminal 30 and the mobile terminal 60 via wireless communication.

[0024] The storage unit 15 is configured with a hard disk drive (HDD), a nonvolatile semiconductor memory, or the like. The storage unit 15 stores various data. For example, the storage unit 15 stores biological information received from the bedside terminal 30 in association with the patient. The storage unit 15 also stores patient information for each patient corresponding to each bedside terminal 30. The patient information includes patient identification information (patient name, patient ID), the patient's medical history, precautions, handover information, and the like. The patient information is stored in advance in the storage unit 15 by inputting it in advance or by acquiring electronic medical record information from an external device. The information managed by the station server 10 may be stored in the storage unit 15 or in the external HDD 20.

[0025] The communication unit 16 is configured by a network interface, etc. The communication unit 16 transmits and receives data to and from external devices connected via a communication network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

[0026] The I / F units 17 to 19 are interfaces for connecting to various devices. Here, the I / F units 17 to 19 are connected to an external HDD 20, a UPS 21, and a printer 22, respectively.

[0027] [Bedside terminal configuration] FIG. 3 shows the functional configuration of the bedside terminal 30. 3, the bedside terminal 30 includes a control unit 31, an operation unit 32, a display unit 33, a timer unit 34, a wireless communication unit 35, a memory unit 36, an I / F unit 37, and a data input unit 38. The various units of the bedside terminal 30 are connected by a bus.

[0028] The control unit 31 is composed of a CPU, RAM, etc. The control unit 31 comprehensively controls the processing operations of each unit of the bedside terminal 30. Specifically, the CPU reads out various processing programs stored in the storage unit 36, loads them into the RAM, and performs various processes in cooperation with the programs. The control unit 31 functions as an analysis means, a derivation means, a selection means, an output means, a filter processing means, a determination means, and a change means of the present invention.

[0029] The operation unit 32 is equipped with various switches, various function buttons, etc., and outputs operation signals from these to the control unit 31. The various function buttons include buttons for inputting various treatment details (sputum suction, temperature measurement, etc.), getting out of bed (toilet, bath, etc.), etc.

[0030] The display unit 33 is configured to include an LCD or the like, and displays various screens according to instructions of a display signal input from the control unit 31. For example, the display unit 33 displays a screen that displays biological information (measured values, measured waveforms, etc.) acquired from the sensor unit 40.

[0031] The timekeeping unit 34 has a timekeeping circuit (RTC: Real Time Clock), and measures the current date and time using this timekeeping circuit and outputs it to the control unit 31.

[0032] The wireless communication unit 35 is a wireless interface for transmitting and receiving data to and from the station server 10 via wireless communication.

[0033] The storage unit 36 ​​is composed of a non-volatile semiconductor memory or the like. The storage unit 36 ​​stores various processing programs, parameters and files required for executing the programs, etc. The storage unit 36 ​​also stores patient identification information (patient name, patient ID) of the patient corresponding to the bedside terminal 30. The storage unit 36 ​​also stores biological information of the patient corresponding to the bedside terminal 30.

[0034] The I / F section 37 is an interface for connecting to the sensor unit 40. The I / F section 37 is an acquisition means for receiving a biological information signal of a patient from the sensor unit 40. 3 illustrates a case where one sensor unit 40 is connected to the bedside terminal 30, but this is not limiting. For example, two or more sensor units may be connected to the bedside terminal 30, or one sensor unit connected to the bedside terminal 30 may include multiple sensors.

[0035] The sensor unit 40 includes a control unit 41, a sensor 42, a storage unit 43, an I / F unit 44, etc. The components of the sensor unit 40 are connected by a bus.

[0036] The control unit 41 is composed of a CPU, RAM, etc. The control unit 41 comprehensively controls the processing operations of each part of the sensor unit 40. Specifically, the CPU reads out various processing programs stored in the storage unit 43, loads them into the RAM, and performs various processes in cooperation with the programs.

[0037] The sensor 42 is a sensor for measuring biological information of a patient, and generates a biological information signal according to the type of sensor. As the sensor 42, a sensor corresponding to the biological information to be measured, for example, respiration (respiratory rate) and / or heartbeat (heart rate) is used. The following description will be given taking the case where the respiratory rate is handled as biological information as an example.

[0038] For example, the following types of sensors [1] to [7] can be used as the sensor 42. [1] Radio wave sensor (see, for example, Patent No. 6290501) Radio wave sensors include Doppler sensors (radio waves used: microwaves), FMCW sensors (radio waves used: microwaves, millimeter waves), pulse Doppler sensors (radio waves used: microwaves, millimeter waves), and UWB (pulse radar) (radio waves used: microwaves).

[0039] [2] Pressure sensor Pressure sensors include piezoelectric sensors (see, for example, Japanese Patent No. 4139828), air pressure sensors (see, for example, Japanese Patent Application Laid-Open No. 2017-219341), and load sensors (see, for example, Japanese Patent No. 6268219).

[0040] [3] Thermistor (semiconductor temperature sensor) The thermistor-based sensor is attached directly to the patient's nostrils and measures breathing by distinguishing between inhalation and exhalation based on temperature changes caused by airflow.

[0041] [4] Thoracic impedance The sensor that uses thoracic impedance passes a weak current between electrodes attached to the chest for electrocardiograms, and detects changes in impedance as changes in voltage.

[0042] [5] Acoustic An adhesive sensor with a built-in acoustic transducer is attached to the patient's neck to measure the airway sound. By measuring this airway sound simultaneously with SpO2, breathing patterns (inhalation and exhalation) can be identified. [6] Capnography Capnography detects breathing by measuring the partial pressure of carbon dioxide in inhaled and exhaled air.

[0043] [7] Oximetry Using data obtained by measuring SpO2 and pulse rate using a conventional pulse oximeter, the respiratory rate is calculated by taking advantage of the fact that the baseline of the pulse wave fluctuates due to changes in venous return caused by breathing.

[0044] [8] 3D camera The 3D camera can measure the distance from the 3D camera to specific parts of the patient, so the respiratory rate can be derived from the time series data of that distance. In addition, the 3D camera can measure the abdomen and chest simultaneously, so it can also detect paradoxical breathing, in which the chest and abdomen move in opposite phases.

[0045] The storage unit 43 is configured with a non-volatile semiconductor memory, etc. The storage unit 43 stores various processing programs, parameters and files required for executing the programs, etc. The I / F unit 44 performs data communication with the bedside terminal 30 .

[0046] The data input unit 38 of the bedside terminal 30 is configured with an NFC (Near Field Communication) reader. The data input unit 38 acquires the identification information of the medical professional from an IC card 51 carried by the medical professional. The data input unit 38 also acquires the patient's biological information from an HR joint-compatible measuring device 52. For example, the information acquired by the data input unit 38 is associated with the current date and time acquired from the clock unit 34 and transmitted to the station server 10.

[0047] [Bedside terminal operation] Next, the operation of the bedside terminal 30 will be described. The sensor unit 40 continuously detects the biological information signal of the patient, who is the subject, and transmits it to the bedside terminal 30 while the power of the sensor unit 40 and the bedside terminal 30 are on. The control unit 31 of the bedside terminal 30 stores the biological information signal input from the sensor unit 40 in the memory unit 36 ​​for a predetermined period of time. The predetermined period is at least a period corresponding to the length of a slot 3, which will be described later. The control unit 31 also executes a respiratory rate calculation process shown in FIG. 4 based on the biological information signal input from the sensor unit 40 at predetermined time intervals (e.g., 0.5 to 1 second). The control unit 31 may also cause the display unit 33 to display the biological information signal input from the sensor unit 40.

[0048] In the conventional technology, a biological information signal related to breathing for a certain period (slot) is analyzed to obtain information about the periodicity of breathing, and the breathing rate is calculated based on the obtained information. In this case, for example, if the specification is to measure the breathing rate from 4 bpm to 80 bpm, the slot must be long enough to measure the longest breathing rate of 4 bpm.

[0049] However, since the respiratory rate cannot be calculated for a certain period immediately after measurement begins with the sensor unit 40 (the period until one slot's worth of data is accumulated), there was a problem that if the slot was long, there would be a long time lag (delay) until the first respiratory rate was output. Furthermore, actual breathing may occasionally be disrupted temporarily by coughing, breath-holding, or noise due to body movement. When such a temporary disruption in the respiratory rhythm occurs, there is a problem that the accuracy of the calculated respiratory rate deteriorates until the noise corresponding to that breathing falls outside the period (slot) to be analyzed. For example, if the slot is 30 seconds, the noise is included in the analysis period from the time the noise occurs until 30 seconds have elapsed, and the accuracy of the calculated respiratory rate deteriorates. In other words, if the slot is long, there is a problem that the period during which the respiratory rate cannot be calculated accurately when noise occurs becomes longer. Furthermore, if the slot is long, any changes in the breathing rhythm cannot be reflected immediately, that is, there is a problem of poor responsiveness.

[0050] Therefore, in the respiratory rate calculation process, biological information signals of multiple periods of different lengths (slot 1 to slot 3) are analyzed, information regarding the periodicity of multiple breaths is calculated, and the respiratory rate based on the most reliable information from the multiple calculated pieces of information is output.

[0051] In this embodiment, it is assumed that slots 1, 2, and 3 [seconds] are preset as the periods to be used for analysis. FIG. 5 is a diagram showing the biological information signal (measurement signal) transmitted from the sensor unit 40 and the biological information signals corresponding to the respective periods of slots 1 to 3. As shown in FIG. 5, if the length of slot 1 is L1, the length of slot 2 is L2, and the length of slot 3 is L3, then L1 < L2 < L3. In FIG. 5, the vertical axis represents the signal value of the biological information signal. The horizontal axis represents time. The horizontal axis of FIG. 5 represents the past as it goes to the right with the current time point being 0. For example, the biological information signal of slot 1 is the biological information signal input from the sensor unit 40 within the period from L1 seconds ago to the current time. The biological information signal of slot 2 is the biological information signal input from the sensor unit 40 within the period from L2 seconds ago to the current time. The biological information signal of slot 3 is the biological information signal input from the sensor unit 40 within the period from L3 seconds ago to the current time. Slot 1 is suitable for calculating the respiratory rate for breathing with a shorter period than slots 2 and 3. Slot 2 is suitable for calculating the respiratory rate for breathing with a longer period than slot 1 and a shorter period than slot 3. Slot 3 is suitable for calculating the respiratory rate for breathing with a longer period than slots 1 and 2.

[0052] It is preferable that the length of each slot is determined according to the specification range of the respiratory rate to be measured. For example, when the longest slot length (the length L3 of slot 3) is set as the minimum respiratory rate RR_Min [bpm] in the specification range of the bedside terminal 30, it is preferably set as the following (1) or (2). (1) When using the autocorrelation function or PACF for the frequency analysis described later Slot length [sec] >= 2×60 / RR_Min (2) When using the Fourier transform or wavelet transform for the frequency analysis described later Slot length [sec] >= 60 / RR_Min

[0053] For example, if the maximum respiratory rate within the specification range of the bedside terminal 30 is RR_Max [bpm], the shortest slot length (length of slot 1) is preferably set to (3) or (4) below. (3) When using the autocorrelation function or PACF for frequency analysis (described later) Slot length [sec] >= 2×60 / RR_Max (4) When using Fourier transform or wavelet transform for frequency analysis (described later) Slot length [sec] >= 60 / RR_Max The reason why the period is doubled when using an autocorrelation function or PACF for the frequency analysis described below is that in these analyses, the biological information signal is shifted to calculate the correlation function, which requires a length twice the period.

[0054] The respiration rate calculation process will be described below with reference to Fig. 4. The respiration rate calculation process is executed by the control unit 31 in cooperation with a program stored in the storage unit.

[0055] 4, in the respiratory rate calculation process, the control unit 31 first executes slot 1 processing, slot 2 processing, and slot 3 processing in parallel. The control unit 31 may execute slot 1 processing, slot 2 processing, and slot 3 processing in parallel by multitasking. Alternatively, the control unit 31 may include multiple CPUs, and the multiple CPUs may execute slot 1 processing, slot 2 processing, and slot 3 processing in parallel.

[0056] In the slot 1 process, first, the control unit 31 acquires the biological information signal of slot 1 (step S11). That is, the control unit 31 acquires the biological information signal from L1 seconds ago to the present from the storage unit .

[0057] If measurement by the sensor unit 40 has just started and biological information signals for slot 1 have not yet been accumulated, the control unit 31 outputs, for example, an error signal and ends the slot 1 process.

[0058] Next, the control unit 31 performs filtering on the acquired biological information signal (step S12). In step S12, the control unit 31 performs filtering on the acquired biological information signal using an LPF (Low-pass filter) or a BPF (Band pass filter). For example, the control unit 31 extracts the biological information signal in a specific frequency band using a cutoff frequency fc1 that is predetermined according to the length of slot 1. This reduces the influence of high-frequency noise.

[0059] Next, the control unit 31 performs decimation processing on the filtered biological information signal (step S13). In step S13, the control unit 31 reduces the sampling interval of the biological information signal to a predetermined sampling interval according to the length of slot 1.

[0060] Next, the control unit 31 analyzes the decimated biological information signal of slot 1 and generates information relating to the periodicity of breathing (step S14). In step S14, the control unit 31 calculates, for example, the autocorrelation function of the biological information signal of slot 1 and generates this as information related to the respiratory periodicity. The autocorrelation function is a measure of how closely a time-series signal matches a time-shifted version of the signal, and is expressed as a function of the magnitude of the time shift (τ).

[0061] The analysis method of the biological information signal is not limited to the calculation of the autocorrelation function. For example, the biological information signal of slot 1 may be Fourier transformed (fast Fourier transformed) and the obtained frequency domain waveform may be used as information on the periodicity of breathing. Alternatively, the biological information signal of slot 1 may be wavelet transformed and the obtained frequency domain waveform may be used as information on the periodicity of breathing.

[0062] Furthermore, for example, when the bedside terminal 30 is provided with a plurality (N pieces) of sensor units 40 and N pieces of bio-information signals measuring respiration are transmitted from each of the sensor units 40, a PACF (Position AutoCorrelation Function) may be calculated as information regarding the periodicity of respiration. The PACF is a value indicating the autocorrelation of a locus of points specified by the N pieces of bio-information signals in an N-dimensional space (see, for example, Patent Document 1). When calculating the PACF, for example, the control unit 31 first calculates the N pieces of bio-information signals at a first time t and t as I j (t) (j=1 to N), a predetermined time difference is τ, the upper limit of the first time is k, and Z(τ) is the sum at τ, and Z(τ) is calculated by equation (1).

[0063]

number

[0064] Z(τ) takes a minimum value when the time difference τ is an integer multiple of the period of the biological information (this is the opposite of a general autocorrelation function). Therefore, the PACF is obtained by normalizing Z(τ) so that it falls within the range of 0 to 1, inverting the sign of Z(τ), and adding 1.

[0065] Next, the control unit 31 detects peaks in the information relating to respiratory periodicity calculated in step S14 (step S15), and derives a reliability index 1 for the information relating to respiratory periodicity (step S16). The reliability index is an index value representing the reliability of the information relating to respiratory periodicity generated based on the biological information signal of each slot. The index value representing the reliability of the information relating to respiratory periodicity generated based on the biological information signal of slot 1 is set as reliability index 1. For example, when the information about respiratory periodicity is an autocorrelation function, the control unit 31 determines the value of the largest peak among the peaks excluding the first peak, which is the first peak of the autocorrelation function, as the reliability index, as shown in Figures 6 and 7. The first peak is excluded because the first peak in an autocorrelation function is always 1 (maximum). In the example shown in Figure 6, the value of the second peak becomes the reliability index. In the example shown in Figure 7, the value of the third peak becomes the reliability index. The same applies when the information about respiratory periodicity is a PACF. The reliability index becomes higher as the respiratory period (frequency) becomes more constant. When there is noise such as coughing, breath holding, or body movement, the period becomes irregular, and the reliability index becomes lower.

[0066] When the information about the periodicity of breathing is a frequency domain waveform obtained by Fourier transforming or wavelet transforming the bio-information signal, the control unit 31 uses the height of the maximum peak as the reliability index. The reliability index increases as the breathing frequency approaches a constant. When noise such as coughing, breath holding, or body movement occurs, the frequency becomes non-constant, and the peaks become dispersed, resulting in an overall decrease in the reliability index. In other words, the reliability index decreases.

[0067] On the other hand, in the slot 2 process, first, the control unit 31 acquires the biological information signal of slot 2 (step S21). That is, the control unit 31 acquires the biological information signal from L2 seconds ago to the present from the storage unit .

[0068] If measurement by the sensor unit 40 has just started and no biological information signal has been stored for slot 2 yet, the control unit 31 outputs, for example, an error signal and ends the slot 2 process.

[0069] Next, the control unit 31 performs filtering on the acquired biological information signal (step S22). For example, similar to step S12, the control unit 31 performs filtering on the acquired biological information signal using an LPF (Low-pass filter) or a BPF (Band pass filter). This reduces the influence of high frequency noise. Here, a cutoff frequency fc2 different from the cutoff frequency fc1 is used to extract the biological information signal in a specific frequency band.

[0070] Next, the control unit 31 performs decimation processing on the filtered biological information signal (step S23). In step S23, the control unit 31 reduces the sampling interval of the biological information signal so that it becomes a sampling interval that is predetermined according to the length of slot 2.

[0071] Next, the control unit 31 analyzes the decimated biological information signal of slot 2 to generate information related to the periodicity of breathing (step S24). The process of step S24 is the same as the process of step S14 except that the biological information signal of slot 2 is the subject of analysis, and therefore the same description will be used.

[0072] Next, the control unit 31 detects peaks in the information relating to respiratory periodicity calculated in step S24 (step S25), and derives a reliability index 2 for the information relating to respiratory periodicity (step S26). The reliability index 2 is an index value representing the reliability of the information relating to respiratory periodicity generated based on the biological information signal of slot 2. The processing in step S26 is similar to the processing described in step S16, and therefore the description thereof will be used here.

[0073] On the other hand, in the slot 3 process, first, the control unit 31 acquires the biological information signal of slot 3 (step S31). That is, the control unit 31 acquires the biological information signal from L3 seconds ago to the present from the storage unit .

[0074] If measurement by the sensor unit 40 has just started and no biological information signal has been stored in slot 3 yet, the control unit 31 outputs, for example, an error signal and ends the slot 3 process.

[0075] Next, the control unit 31 performs filtering on the acquired biological information signal (step S32). For example, similar to step S12, the control unit 31 performs filtering on the acquired biological information signal using an LPF (Low-pass filter) or BPF (Band pass filter). This reduces the influence of high-frequency noise. Note that here, a cutoff frequency fc3 different from the cutoff frequencies fc1 and fc2 is used to extract a biological information signal of a specific frequency band. Here, "cutoff frequency fc1>cutoff frequency fc2>cutoff frequency fc3". Since longer slots target lower frequency biological information signals, the influence of high-frequency noise can be reduced according to the slot length by lowering the cutoff frequency for longer slots.

[0076] Next, the control unit 31 performs decimation processing on the filtered biological information signal (step S33). In step S33, the control unit 31 reduces the sampling interval of the biological information signal so that it becomes a sampling interval that is predetermined according to the length of slot 3. Here, by making the sampling interval longer for longer slots, the calculation cost can be reduced. Therefore, it is preferable that "the sampling interval of slot 1 < the sampling interval of slot 2 < the sampling interval of slot 3".

[0077] Next, the control unit 31 analyzes the decimated biological information signal of slot 3 to generate information related to respiratory periodicity (step S34). The processing of step S34 is the same as the processing of step S14 except that the analysis target is the biological information signal of slot 3, and therefore the same explanation will be used.

[0078] Next, the control unit 31 detects peaks in the information relating to respiratory periodicity calculated in step S34 (step S35), and calculates a reliability index 3 for the information relating to respiratory periodicity (step S36). The reliability index 3 is an index value representing the reliability of the information relating to respiratory periodicity generated based on the biological information signal of slot 3. The processing in step S36 is similar to the processing described in step S16, and therefore the description thereof will be used here.

[0079] When the slot 1 process to slot 3 process are completed, the control unit 31 selects the slot with the highest reliability index value from among slots 1 to 3 (step S41). Fig. 8 shows a graph of the autocorrelation function for each slot when the autocorrelation function is calculated as information related to respiratory periodicity. In the example shown in Fig. 8, slot 2 has the highest reliability index 2. Therefore, slot 2 is selected in step S41. If there is no slot whose reliability index value is equal to or greater than a predetermined threshold, control unit 31 may display an error on display unit 33 without performing the processes of steps S41 to S43.

[0080] Here, when the information about respiratory periodicity is an autocorrelation function or PACF and there are multiple slots with the highest reliability index, the control unit 31 selects the slot with the shortest slot length among the slots with the highest reliability index as the target for calculating the respiratory rate. As mentioned above, the shorter the slot, the shorter the sampling time interval. Therefore, by selecting the shortest slot as the target for calculating the respiratory rate, the respiratory rate can be calculated more accurately.

[0081] Furthermore, for example, when the information regarding respiratory periodicity is a frequency domain waveform obtained by Fourier transforming or wavelet transforming the bioinformation signal, and when there are multiple slots with the highest reliability index, the control unit 31 selects the slot with the longest slot length among the slots with the highest reliability index as the target for calculating the bioinformation. When the information regarding periodicity is a frequency domain waveform, the longer the acquisition time of the original bioinformation signal, the greater the amount of data. Therefore, by selecting the longest slot as the target for calculating the respiratory rate, the respiratory rate can be calculated more accurately.

[0082] If an error signal is output from any of slot 1 processing to slot 3 processing, the control unit 31 excludes the corresponding slot from selection. This allows the respiratory rate to be calculated and output even if a biological information signal from a long slot is not stored. Furthermore, if an error signal is output from all of the slot 1 processing to slot 3 processing, the control unit 31 causes the display unit 33 to display an error.

[0083] Next, the control unit 31 calculates the respiratory rate of the patient, who is the subject, based on the information about the respiratory periodicity of the selected slot (step S42). 9 is a diagram for explaining a method for calculating the respiratory rate when an autocorrelation function or PACF is calculated as information related to respiratory periodicity. As shown in FIG. 9, the control unit 31 calculates the respiratory rate RR using the following equation (2). RR=60 / (ix×Ts)…Equation (2) where RR is the respiratory rate [bpm]. ix is ​​the data number of the largest peak P1 excluding the first peak (initial peak). The data number is the data number when the first data point is set to 0. Ts is the data interval [seconds].

[0084] When a Fourier-transformed or wavelet-transformed biological information signal is calculated as information regarding the periodicity of breathing, the respiratory rate is the value of the frequency of the maximum peak.

[0085] Then, the control unit 31 outputs the calculated respiratory rate (step S43) and ends the respiratory rate calculation process. For example, the control unit 31 outputs the calculated respiratory rate to the display unit 33 for display. The control unit 31 also outputs the calculated respiratory rate to the station server 10 and / or the mobile terminal 60 via the wireless communication unit 35. The control unit 31 also stores the calculated respiratory rate in the memory unit 31. 36 and stored in association with the patient information.

[0086] In this way, in the respiration rate calculation process, the control unit 31 analyzes the respiration-related bioinformation signals of multiple slots of different lengths among the bioinformation signals acquired from the sensor unit 40, and generates information about the respiration periodicity for each slot. Next, the control unit 31 derives a reliability index indicating the reliability of the information about the respiration periodicity for each of the multiple slots. The control unit 31 then selects the slot with the highest reliability index, and calculates and outputs the respiration rate based on the information about the respiration periodicity for the selected slot. Therefore, measurement accuracy and responsiveness can be improved without limiting the period of measurable bioinformation.

[0087] [Variation 1] In the above embodiment, the length of each slot is fixed, but the control unit 31 may adaptively change the slot length based on the peak position of the autocorrelation function or PACF or the respiratory rate.

[0088] For example, a slot 4 is provided in addition to slots 1 to 3, and in the respiratory rate calculation process, the control unit 31 executes slot 4 processing in addition to slot 1 processing to slot 3 processing. The slot 4 processing is a process of analyzing the biological information signal of the length of slot 4 and calculating the reliability index 4. The lengths of slots 1 to 3 are fixed, and the length of slot 4 is variable.

[0089] The control unit 31 determines the respiratory cycle T as the time to the highest peak P1 excluding the first peak of the autocorrelation function or PACF of the slot with the highest reliability in the most recent respiratory rate calculation process, as shown in Fig. 10, and determines the slot length of slot 4 as T × α as shown in Fig. 11. Here, α is a coefficient that provides redundancy against respiratory rate disturbances, and is set to, for example, 1.5. Alternatively, the control unit 31 may calculate the respiratory cycle T based on the respiratory rate calculated in the most recent respiratory rate calculation process. For example, if the respiratory rate is RR [bpm], the respiratory cycle T [seconds] can be calculated using the following formula (3). T =60 / RR [seconds]…Equation (3)

[0090] In this way, by adaptively changing the slot length based on the peak position of the autocorrelation function or PACF calculated in the past (most recently), or the respiratory rate, it is possible to calculate the respiratory rate using a slot of a length corresponding to the respiratory cycle of the patient, which is the subject. Therefore, the respiratory rate can be calculated more accurately and efficiently.

[0091] [Variation 2] In the above embodiment, the length of each slot is fixed, but the control unit 31 may adaptively change the slot length based on the peak position of the autocorrelation function or PACF.

[0092] For example, as shown in the upper part of Fig. 12, the movable ranges (minimum slot length and maximum slot length) of slots 1 to 3 are set in advance, and the initial value of each slot is set within the movable range. The control unit 31 determines the time to the highest peak P1 excluding the first peak of the autocorrelation function or PACF of the slot with the highest reliability in the most recent respiratory rate calculation process as the respiratory cycle T, and calculates T×α. Here, α is a coefficient that provides redundancy for respiratory rate disturbances and is set to, for example, 1.5. The respiratory cycle α may be calculated using the above formula (3). Then, the control unit 31 sets the length of each slot to the length that is closest to T×α within the variable range of each slot length.

[0093] For example, in the example shown in Figure 12, as shown in the bottom row, within the movable range of slot 1, the maximum slot length is closest to T x α. Therefore, control unit 31 sets the length of slot 1 to the maximum slot length. Furthermore, the movable range of slot 2 includes T x α. Therefore, control unit 31 sets the length of slot 2 to T x α. Within the movable range of slot 3, the minimum slot length is closest to T x α. Therefore, control unit 31 sets the length of slot 3 to the minimum slot length.

[0094] In this way, by adaptively changing the slot length based on the peak position of the autocorrelation function or PACF calculated in the past (most recently), or the respiratory rate, it is possible to calculate the respiratory rate using a slot of a length corresponding to the respiratory cycle of the patient, which is the subject. Therefore, the respiratory rate can be calculated more accurately and efficiently.

[0095] [Variation 3] The breathing rate varies depending on age, illness, etc. Therefore, the length of the slot may be determined based on patient information of the subject, i.e., the patient's age, illness, etc.

[0096] For example, the lengths of slots 1 to 3 are stored in the storage unit 36 ​​in association with age and / or disease. The control unit 31 acquires patient information of the subject and determines the length of each slot based on the acquired patient information. The patient information may be acquired from the station server 10 based on a patient ID or the like stored in the storage unit 36. Alternatively, the user (e.g., a doctor or nurse) may input the information by operating the operation unit 32.

[0097] In this way, by determining the slot length according to the patient information, the respiratory rate can be calculated using slots of a length according to the respiratory state of the patient being examined, thereby enabling more accurate and efficient calculation of the respiratory rate.

[0098] [Variation 4] In the above embodiment, the number of slots is fixed, but it may be variable. For example, a group of basic slots that are always used and a group of spare slots that are normally unused may be provided, and the control unit 31 may change whether to use or not use the spare slots depending on the respiratory rate calculated in the most recent respiratory rate calculation process.

[0099] For example, as shown in Figure 13, a basic slot group and a spare slot group are provided. The basic slot group includes the shortest slot and the longest slot, and it is desirable to design the length of each basic slot so that the shortest slot and the longest slot are equally divided at equal intervals. It is desirable to provide spare slot groups so that they complement the gap between adjacent basic slots at equal intervals.

[0100] For example, the control unit 31 first performs a respiratory rate calculation process using the basic slot group (slots 1, 3, and 5) to calculate the respiratory cycle T×α. T×α can be calculated using the methods described in [Variation 1] and [Variation 2] above. If the slot whose slot length is equal to or longer than T×α and closest to T×α (hereinafter referred to as the optimal slot) is a spare slot, the control unit 31 changes the slot to use the optimal slot (slot 2 in FIG. 14), as shown in FIG. 14. Then, the control unit 31 performs a respiratory rate calculation process using slot 2, which is a spare slot, in addition to slots 1, 3, and 5, which are basic slots. If the optimal slot changes due to a change in the patient's respiratory cycle T, the control unit 31 returns the spare slot that was in use to an unused slot.

[0101] In this way, by changing the number of slots depending on the patient's most recent respiratory cycle, the respiratory rate can be calculated more accurately.

[0102] As described above, the control unit 31 of the bedside terminal 30 analyzes the biological information signals of the subject acquired from the sensor unit 40, each of which has a different length, as an analysis target, and generates information about the periodicity of the biological information signal for each slot. Next, the control unit 31 derives a reliability index indicating the reliability of the information about the periodicity for each of the slots. The control unit 31 then selects the slot with the highest reliability index, and calculates and outputs the biological information of the subject based on the information about the periodicity of the selected slot. Therefore, measurement accuracy and responsiveness can be improved without limiting the period of measurable biological information.

[0103] Furthermore, the longer the slot length, the longer the sampling time interval of the biological information signal is set by the control unit 31, so that the calculation cost in processing with a long slot length can be reduced.

[0104] Furthermore, when the information about periodicity is an autocorrelation function or PACF and there are multiple slots with the highest reliability index, the control unit 31 selects the slot with the shortest slot length among the slots with the highest reliability index as the slot to be used to calculate the biological information. As described above, the shorter the slot, the shorter the sampling time interval. Therefore, by selecting the shortest slot among the multiple slots with the highest reliability index as the slot to be used to calculate the biological information, the biological information can be calculated with high accuracy.

[0105] Furthermore, the control unit 31 performs filtering on the biological information signals of each of the multiple slots using a different cutoff frequency according to the length of the slot. Therefore, for example, the cutoff frequency can be lowered for longer slots that target biological information signals with lower frequencies (longer periods), and the influence of high-frequency noise can be effectively suppressed.

[0106] Furthermore, for example, the control unit 31 can determine the length of each of a plurality of slots according to the patient information of the subject, thereby calculating biological information using slots of lengths suitable for the condition of the subject.

[0107] Furthermore, for example, the control unit 31 can determine the length of multiple slots based on information regarding periodicity or biometric information previously calculated for the subject, and thereby calculate biometric information using slots of lengths appropriate for the subject's condition.

[0108] Furthermore, for example, the control unit 31 can calculate the biometric information more accurately by changing the number of slots based on the biometric information calculated in the past.

[0109] Furthermore, for example, when the information on periodicity is a frequency domain waveform obtained by Fourier transforming or wavelet transforming the bioinformation signal, and there are multiple slots with the highest reliability index, the control unit 31 selects the longest slot among the slots with the highest reliability index as the target for calculating the bioinformation, thereby enabling accurate calculation of the bioinformation.

[0110] The description in the above embodiment is an example of the biometric information processing device according to the present invention, and the present invention is not limited to this. The detailed configuration and detailed operation of each part constituting the device can be appropriately changed without departing from the spirit of the present invention.

[0111] For example, in the above embodiment, it has been described that the bedside terminal 30 performs the respiration rate calculation process to calculate the biological information (respiration rate) based on the biological information signal acquired from the sensor unit 40. Alternatively, the control unit 41 may perform the respiration rate calculation process in the sensor unit 40 to calculate the biological information. Alternatively, the station server 10 may acquire the biological information signal from the bedside terminal 30, and the control unit 11 may perform the respiration rate calculation process to calculate the biological information.

[0112] In the above embodiment, the slot with the highest reliability index is selected, and the respiratory rate is calculated and output based on information about the respiratory periodicity in the selected slot, but the processing order is not limited to this example. For example, multiple respiratory rates may be calculated based on information about the respiratory periodicity in each slot, and the respiratory rate calculated for the slot with the highest reliability index may be output as the respiratory rate of the subject.

[0113] Furthermore, in the embodiment, it is conceivable that noise such as body movement may temporarily increase the reliability index of a suboptimal slot, resulting in switching to an incorrect slot. As a countermeasure, for example, the control unit 31 may calculate the average reliability index of each slot over a predetermined period (e.g., one minute) as the reliability index of each slot. Alternatively, the control unit 31 may change the slot only when the reliability index of another slot is equal to or greater than a certain factor (e.g., two times) the reliability index of the current slot, thereby preventing frequent slot switching within a short period of time.

[0114] The programs for executing the processes in each device may be stored on a portable recording medium, and a carrier wave may be used as a medium for providing program data via a communication line.

[0115] While embodiments of the present invention have been described and illustrated in detail, the disclosed embodiments are made for purposes of illustration and example only, and not limitation, the scope of the invention being to be construed by the appended claims. [Explanation of symbols]

[0116] 10 Station Server 30 Bedside Terminal 31 Control Unit 32 Operation section 33 Display section 35 Radio Communication Department 36 Memory section 37 I / F section 40 Sensor Unit 42 sensors 100 Biometric Monitoring System

Claims

1. an acquisition means for acquiring a biological information signal of a subject; an analysis means for analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired by the acquisition means, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation means for deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; a selection means for selecting a period with the highest reliability index; an output unit that outputs biological information of the subject calculated based on information about the periodicity of the period selected by the selection unit; A biological information processing device comprising:

2. the analyzing means generates information about the periodicity by calculating an autocorrelation function of the biological information signal, calculating a PACF (Position Auto Correlation Function) of the biological information signal, performing a Fourier transform on the biological information signal, or performing a wavelet transform on the biological information signal, for each of the plurality of periods. The biometric information processing device according to claim 1 .

3. When the information regarding the periodicity is an autocorrelation function or a PACF, the derivation means derives, as the reliability index, a height of the highest peak among peaks of the autocorrelation function or the PACF excluding a first peak that is the initial peak. The biometric information processing device according to claim 2 .

4. When the information regarding the periodicity is a frequency domain waveform obtained by Fourier transform or wavelet transform of the biological information signal, the derivation means derives a peak height of the frequency domain waveform as the reliability index. The biometric information processing device according to claim 2 .

5. the analysis means increases the sampling time interval of the biological information signal as the length of the period increases; The biometric information processing device according to claim 1 .

6. When the information about the periodicity is an autocorrelation function or a PACF and there are multiple periods with the highest reliability index, the selection means selects the shortest period among the periods with the highest reliability index. The biometric information processing device according to claim 5 .

7. a filter processing means for filtering the biological information signal for each of the plurality of periods using a different cutoff frequency according to the length of the period; The biometric information processing device according to claim 1 .

8. a determining means for determining the lengths of the plurality of periods in accordance with patient information of the subject; The biometric information processing device according to claim 1 .

9. a determination means for determining lengths of the plurality of periods based on information about the periodicity previously calculated for the subject or the biological information; The biometric information processing device according to claim 1 .

10. A change unit for changing the number of the plurality of periods is provided. The biometric information processing device according to claim 1 .

11. the changing means changes the number of the plurality of periods based on the biological information calculated in the past. The biometric information processing device according to claim 1 .

12. the selection means, when the information regarding the periodicity is a frequency domain waveform obtained by Fourier transform or wavelet transform of the biological information signal and there are a plurality of periods with the highest reliability index, selects the longest period among the periods with the highest reliability index. The biometric information processing device according to claim 1 .

13. an acquisition step of acquiring a biological information signal of the subject; an analysis step of analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired in the acquisition step as analysis targets, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation step of deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; a selection step of selecting the period with the highest reliability index; an output step of outputting the biological information of the subject calculated based on information about the periodicity of the period selected in the selection step; A biometric information processing method comprising:

14. Computer, an acquisition means for acquiring a biological information signal of a subject; an analysis means for analyzing the biological information signals of a plurality of periods with different lengths among the biological information signals acquired by the acquisition means, and generating information regarding the periodicity of the biological information signals for each of the plurality of periods; a derivation means for deriving a reliability index indicating reliability of information relating to the periodicity for each of the plurality of periods; selection means for selecting the period in which the reliability index is highest; an output means for outputting the biological information of the subject calculated based on the information regarding the periodicity of the period selected by the selection means; A program to function as a

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