JP2021175492A - Biological information processing device, biological information processing method and program - Google Patents

Abstract

To accurately calculate the value related to the periodicity of biological information.SOLUTION: A bedside terminal (biological information processing device) acquires N biological information signals (N is an integer of 2 or more) for measuring the same biological information from a sensor unit (step S1). Then, the autocorrelation of the locus of the point specified by N biological information signals in the N-dimensional space is analyzed (step S32), and the autocorrelation of the locus of the point in the N-dimensional space is used to find the periodicity of the locus of the point (step S42). Thereby, a value (respiration rate) related to the periodicity of the biological information to be measured is calculated (step S44).SELECTED DRAWING: Figure 4

Description

The present invention relates to a biometric information processing device, a biometric information processing method and a program.

Conventionally, in order to measure the respiratory rate and the heart rate from a living body, a biological information processing device that acquires a biological information signal by using a sensor that detects the respiratory rate and the heart rate and detects the periodicity from the biological information signal is known. ..

As a means for acquiring a biological information signal, a radio wave type sensor, a pressure type sensor, and a sensor using a plurality of them are used. In a normal living body state (unrestrained state), it is difficult to be in a stationary state, and some movement is always occurring. When such a target is a subject, movements other than the measurement target become noise, so a method of removing body movements with a bandpass filter or the like is known.

In addition, a technique has been proposed to detect microtremors of the body surface based on the vibration of the measurement target, such as the subject's respiration and heartbeat, by detecting the phase change (phase difference) of the reflected wave with respect to the irradiation wave. Microwaves are used for efficient detection (see Patent Documents 1 and 2).

In Patent Document 1, two biometric information signals, an I signal and a Q signal, are used separately, and the respiratory rate, heart rate, and the like are calculated from the periodicity of each autocorrelation function.

In Patent Document 2, the periodicity is detected from the locus of points where the components of the I signal and the components of the Q signal are plotted on the IQ coordinate system, and the respiratory rate, heart rate, and the like are calculated.

FIG. 13 shows how the points specified by the I signal and the Q signal move with the passage of time on the IQ plane. When the I signal and the Q signal are signals representing respiration, theoretically, the locus of the point specified by the I signal and the Q signal rotates around the origin and reciprocates on the arc according to the respiration operation. The movement of points on this arc corresponds to exhalation and inspiration. The amount of displacement corresponding to respiration is obtained from the value of the angle θ corresponding to each point on the locus, where θ is the angle formed by the vector consisting of the component of the I signal and the component of the Q signal with the axis of the I signal. Periodicity can be detected.

Japanese Patent No. 6290501 Japanese Unexamined Patent Publication No. 2018-064642

However, in Patent Document 1, since two biometric information signals are processed separately, only one signal is used when calculating the respiratory rate and the heart rate from the periodic waveform, which is an accurate value. There was a risk that it could not be obtained. For example, when the cycles of the I signal and the Q signal appear to be doubled, it is difficult to determine the exact cycle. Further, since only a single signal is used in the processing of each of the two biometric information signals, the I signal and the Q signal, the signal itself is likely to be buried in noise. Further, of the two biometric information signals, the I signal and the Q signal, the periodic characteristics of the biometric information signal appear prominently in only one of the I signal and the Q signal, and only noise can be detected in the other biometric information signal. In some cases.

Further, even in the technique described in Patent Document 2, when noise such as body movement is introduced, the point specified by the I signal and the Q signal on the IQ plane does not rotate around the origin in the actual measurement. In some cases, it was difficult to detect periodicity. For example, as shown in FIG. 14, the loci of the I signal and the Q signal on the IQ plane may not form an arc and may draw a loop. In particular, when using an electric air mat, noise is large when breathing is detected from a patient on the bed with the air mat sandwiched between them.

The present invention has been made in view of the above-mentioned problems in the prior art, and an object of the present invention is to accurately calculate a value related to the periodicity of biological information.

In order to solve the above problem, the invention according to claim 1 includes an acquisition means for acquiring N biometric information signals (N is an integer of 2 or more) for measuring the same biometric information, and an N-dimensional space. The periodicity of the locus of the point is obtained from the analysis means for analyzing the autocorrelation of the locus of the point specified by the N biometric information signals above and the autocorrelation of the locus of the point in the N-dimensional space. This is a biometric information processing apparatus including a calculation means for calculating a value related to the periodicity of the biometric information as the measurement target.

According to the second aspect of the present invention, in the biometric information processing apparatus according to the first aspect, the acquisition means acquires the biometric information signal from one or more radio wave sensors.

According to the third aspect of the present invention, in the biometric information processing apparatus according to the second aspect, the acquisition means differs from the I signal by a predetermined phase from the I signal as the biometric information signal from the radio wave sensor. Acquire each of the Q signals.

According to a fourth aspect of the present invention, in the biometric information processing apparatus according to the second or third aspect, the acquisition means further acquires the biometric information signal from one or more pressure sensors.

According to a fifth aspect of the present invention, in the biometric information processing apparatus according to the first aspect, the acquisition means acquires the biometric information signal from two or more pressure sensors.

The invention according to claim 6 is the biometric information processing apparatus according to any one of claims 1 to 5, wherein the biometric information is respiration and / or heartbeat.

The invention according to claim 7 is the biological information processing apparatus according to any one of claims 1 to 6, wherein the analysis means has a first time on the locus of the point in the N-dimensional space. The value corresponding to the norm of the vector between the first point corresponding to the first point and the second point corresponding to the second time different from the first time by a predetermined time difference is set to the first point. The autocorrelation is analyzed by adding up the sums while changing the time of the above and calculating the sum for each predetermined time difference while changing the predetermined time difference.

The invention according to claim 8 is the bio-information processing apparatus according to claim 7, wherein the analysis means sets the N biometric information signals at t and t at the first time and I _j (t) (. Z (τ) is obtained by the equation (1), where j = 1 to N), the predetermined time difference is τ, the upper limit of the first time is k, and the total in τ is Z (τ).

The invention according to claim 9 takes a minimum value in the bio-information processing apparatus according to claim 7 or 8, when the predetermined time difference is an integral multiple of the cycle of the bio-information.

The invention according to claim 10 includes an acquisition step of acquiring N biometric information signals (N is an integer of 2 or more) for measuring the same biometric information, and the N biometric information in an N-dimensional space. The measurement target was obtained by obtaining the periodicity of the locus of the point from the autocorrelation of the locus of the point specified by the signal and the autocorrelation of the locus of the point in the N-dimensional space. It is a biometric information processing method including a calculation step of calculating a value related to the periodicity of biometric information.

The invention according to claim 11, wherein the computer is an acquisition means for acquiring N biometric information signals (N is an integer of 2 or more) for measuring the same biometric information, and the N number of biometric information signals in an N-dimensional space. An analysis means for analyzing the autocorrelation of the locus of a point specified by a biological information signal, and the measurement target by obtaining the periodicity of the locus of the point from the autocorrelation of the locus of the point in the N-dimensional space. It is a program for functioning as a calculation means for calculating a value related to the periodicity of biological information.

According to the present invention, it is possible to accurately calculate a value related to the periodicity of biological information.

It is a system block diagram of a biological information monitoring system. It is a block diagram which shows the functional configuration of a station server. It is a block diagram which shows the functional structure of a bedside terminal. It is a flowchart which shows the process executed by a bedside terminal. (A) is a figure which shows an example of a biological information signal. (B) is a figure which shows an example of PACF. It is a figure which shows the example of the time change of two biometric information signals (I signal / Q signal). It is a figure for demonstrating the recursive filtering process. It is a figure which shows the locus in the IQ plane of the I signal and Q signal obtained by measuring the respiration of Example 1. FIG. (A) is time series data of the respiratory waveform. (B) is the time series data of the I signal. (C) is the time series data of the Q signal. (D) is a PACF created from the I signal and the Q signal. (A) is a figure which shows the locus of _{the I 1} signal and the Q ₁ signal obtained by the 1st microwave sensor with the respiration of Example 2 as the measurement target in the I _1- Q _{1 plane.} (B) is a figure which shows the locus of _{the I 2} signal and the Q ₂ signal obtained by the 2nd microwave sensor in the I ₂ −Q ₂ plane of the respiration of Example 2 as the measurement target. (A) is time series data of the respiratory waveform. (B) is a time-series data of the _{I 1} signal. (C) is a time-series data for _{Q 1} signal. (D) is the time series data of the _{I 2 signal.} (E) is a time-series data _{Q 2} 'signal. (F) is a PACF created from an _{I 1} signal, a Q ₁ signal, an I ₂ signal, and a Q _{2 signal.} (A) is the time series data of the respiratory waveform of Example 3. (B) is a time-series data of the _{I 1} signal. (C) is a time-series data for _{Q 1} signal. (D) is the time series data of the _{I 2 signal.} (E) is a time-series data _{Q 2} 'signal. (F) is time series data of the signal output from the piezoelectric sensor. (G) is _{a PACF created from an I 1} signal, a Q ₁ signal, an I ₂ signal, a Q ₂ signal, and a piezoelectric sensor signal. It is a figure which shows the theoretical locus of the point specified by the I signal and Q signal on the IQ plane. It is a figure which shows the example of the actual locus of the point specified by the I signal and the Q signal on the IQ plane.

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

[Configuration of biometric information monitoring system]
FIG. 1 shows the system configuration of the biological information monitoring system 100.
As shown in FIG. 1, the biometric information monitoring system 100 includes a station server 10 provided in a nurse station, a bedside terminal 30 (biological information processing device) provided in each bed in a hospital room, and a nurse or the like. It is configured to include a mobile terminal 60 carried by a medical worker. The biological information monitoring system 100 is used in a medical facility such as a hospital. The station server 10 and the bedside terminal 30 can communicate with each other by wireless communication. Further, the station server 10 and the mobile terminal 60 can communicate with each other by wireless communication. It is desirable that the wireless communication in the medical facility is in the sub-giga band where the frequency used for communication does not interfere with the medical device. The number of bedside terminals 30 and mobile terminals 60 is not particularly limited.

The station server 10 centrally manages the measurement data (biological information) of each patient collected by the bedside terminal 30. The display unit 13 of the station server 10 displays a monitoring screen for monitoring biological information of a plurality of patients. Examples of biological information include respiratory rate, heart rate, body temperature, SpO _{2 and the} like. At the nurse station, the station server 10 can grasp changes in the physical condition of each patient. Further, the station server 10 transmits the measurement data of each patient to the mobile terminal 60. Further, the station server 10 may transmit the measurement data of each patient to a cloud server or the like connected via a communication network.

Further, an external HDD (Hard Disk Drive) 20, UPS (Uninterruptible Power Supply) 21, and a printer 22 are connected to the station server 10.
The external HDD 20 stores data such as biometric information of each patient managed by the station server 10.
The UPS 21 is a device that has a built-in device for storing electric power such as a secondary battery, and can supply electric power with a predetermined output for a certain period of time even if the electric power supply from the outside is interrupted due to a power failure or the like.
The printer 22 prints measurement data on paper, and prints various measurement data and the like displayed on the display unit 13 of the station server 10.

The bedside terminal 30 is installed on the bedside of each patient, acquires patient biometric information from sensor units 40A and 40B such as a breathing sensor and a pulse oximeter, and displays the measurement result. In addition, the bedside terminal 30 reads the IC card 51 to acquire the identification information of the medical staff, and acquires the biological information of the patient from the measuring device 52 compatible with the HR (Health Record) joint (registered trademark) such as a thermometer. To do. The bedside terminal 30 transmits the measurement data of the biological information to the station server 10.

The mobile terminal 60 displays measurement data (biological information) of each patient transmitted from the station server 10. As a result, the medical staff can confirm the change in the physical condition of each patient from other than the nurse station. The measurement data of each patient may be directly transmitted from the bedside terminal 30 to the mobile terminal 60.

[Station server configuration]
FIG. 2 shows the functional configuration of the station server 10.
As shown in FIG. 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, I / F (interface) units 17 to 19, and the like. Each part is connected by a bus.

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

The operation unit 12 is configured to include a keyboard equipped with cursor keys, character / number input keys, various function keys, and a pointing device such as a mouse, and controls operation signals input by key operations on the keyboard and mouse operations. Output to unit 11.

The display unit 13 is configured to include a monitor such as an LCD (Liquid Crystal Display), and displays various screens according to an instruction of a display signal input from the control unit 11.

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 by wireless communication.

The storage unit 15 is composed of an HDD, a non-volatile semiconductor memory, or the like, and stores various data. For example, the storage unit 15 stores the measurement data of the biological information received from the bedside terminal 30 in association with the patient. Further, the storage unit 15 stores patient information of each patient corresponding to each bedside terminal 30. The patient information includes patient identification information (patient name, patient ID), medical history of the patient, precautions, matters to be reported, and the like. The patient information is stored in the storage unit 15 in advance by inputting the patient information in advance or acquiring the 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 may be stored in the external HDD 20.

The communication unit 16 is configured by a network interface or the like, and transmits / receives data to / from an external device connected via a communication network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

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 the external HDD 20, UPS 21, and the printer 22, respectively.

[Bedside terminal configuration]
FIG. 3 shows the functional configuration of the bedside terminal 30.
As shown in FIG. 3, the bedside terminal 30 includes a control unit 31, an operation unit 32, a display unit 33, a timekeeping unit 34, a wireless communication unit 35, a storage unit 36, an I / F unit 37, a data input unit 38, and the like. Each part is connected by a bus.

The control unit 31 is composed of a CPU, RAM, and the like, and comprehensively controls the processing operation of each unit of the bedside terminal 30. Specifically, the CPU reads various processing programs stored in the storage unit 36, develops them in the RAM, and performs various processing in cooperation with the programs.

The operation unit 32 includes various switches, various function buttons, and the like, and outputs these operation signals to the control unit 31. The various function buttons include buttons for inputting various treatment contents (sputum suction, body temperature measurement, etc.), getting out of bed (toilet, bathing, etc.), and the like.

The display unit 33 is configured to include an LCD or the like, and displays various screens according to an instruction of a display signal input from the control unit 31. For example, the display unit 33 displays a screen for displaying biological information (measured values, measured waveforms, etc.) acquired from the sensor units 40A and 40B.

The timekeeping unit 34 has a timekeeping circuit (RTC: Real Time Clock), and the current time and time is timed by this timekeeping circuit and output to the control unit 31.

The wireless communication unit 35 is a wireless interface for transmitting / receiving data to / from the station server 10 by wireless communication.

The storage unit 36 is composed of a non-volatile semiconductor memory or the like, and stores various processing programs, parameters, files, and the like necessary for executing the program. The storage unit 36 stores the biometric information of the patient corresponding to the bedside terminal 30.

Further, the storage unit 36 stores patient identification information (patient name, patient ID) of the patient corresponding to the bedside terminal 30.

The I / F unit 37 is an interface for connecting to the sensor units 40A and 40B, and receives a patient's biometric information signal from the sensor units 40A and 40B.
Note that FIG. 3 illustrates a case where two sensor units 40A and 40B are connected to the bedside terminal 30, but the present invention is not limited to this example. For example, one or three or more sensor units may be connected to the bedside terminal 30, or one sensor unit connected to the bedside terminal 30 may include a plurality of sensors.

Each of the sensor units 40A and 40B includes a control unit 41, a sensor 42, a storage unit 43, an I / F unit 44, and the like, and each unit is connected by a bus.

The control unit 41 is composed of a CPU, RAM, and the like, and comprehensively controls the processing operations of each unit of the sensor units 40A and 40B. Specifically, the CPU reads various processing programs stored in the storage unit 43, develops them in the RAM, and performs various processing in cooperation with the programs.

The sensor 42 is a sensor for measuring the biological information of the patient, and generates a biological information signal according to the type of the sensor. As the sensor 42, a sensor corresponding to biological information to be measured is used, and examples thereof include a radio wave type sensor and a pressure type sensor.

The radio wave sensor transmits radio waves, receives radio waves reflected by the object to be measured, and detects the moving speed (movement) of the object to be measured by changing the frequency between the transmitted wave and the reflected wave. It is a non-contact type sensor. Examples of the radio wave type sensor include a microwave sensor and a millimeter wave sensor. Further, the radio wave type sensor includes a Doppler sensor, an FMCW (Frequency Modulated Continuous Wave) sensor, and the like. The radio wave sensor outputs an I signal and a Q signal that differs from the I signal by a predetermined phase. For example, the Q signal is 90 ° out of phase with the I signal.

The pressure sensor is a contact type sensor that detects the movement of the object to be measured by converting the pressure from the object to be measured into an electric signal.

The storage unit 43 is composed of a non-volatile semiconductor memory or the like, and stores various processing programs, parameters, files, and the like necessary for executing the program.
The I / F unit 44 performs data communication with the bedside terminal 30.

The data input unit 38 of the bedside terminal 30 is composed of an NFC (Near Field Communication) reader. The data input unit 38 acquires the identification information of the medical worker from the IC card 51 possessed by the medical worker. In addition, the data input unit 38 acquires the patient's biological information from the measuring device 52 compatible with the HR joint. For example, the information acquired by the data input unit 38 is associated with the current date and time acquired from the timekeeping unit 34 and transmitted to the station server 10.

In the present embodiment, the periodicity of the locus with respect to the coordinate position of the measurement point in the N-dimensional space specified by N biometric information signals acquired from a radio wave sensor (I signal, Q signal), a pressure sensor, or the like. Ask for.

As shown in FIG. 14, even when the locus of the point specified by the I signal representing the respiration on the IQ plane and the Q signal does not draw an arc centered on the origin and draws a loop, it matches the respiration. , It is known that it reciprocates on a relatively constant trajectory. Further, on the IQ plane, the moving speed of the point specified by the I signal and the Q signal becomes slow near the position where the exhalation and the inspiration are switched.

The control unit 31 acquires N biometric information signals (time series data) for which the same biometric information is measured (N is an integer of 2 or more). That is, the control unit 31 functions as an acquisition means.
As biometric information, for example, respiration and / or heartbeat is used. Hereinafter, a case where respiration is treated as biological information will be described. The control unit 31 acquires N biometric information signals output from the sensors 42 of the sensor units 40A and 40B, respectively, for respiration as a measurement target via the I / F unit 37.

As the respiration sensor, the following types of sensors [1] to [7] can be used.
[1] Radio wave sensor (see, for example, Japanese Patent No. 6290501)
Radio wave sensors include Doppler sensor (radio wave used: microwave), FMCW sensor (radio wave used: microwave, millimeter wave), pulse Doppler sensor (radio wave used: microwave, millimeter wave), UWB (pulse radar) (used). Radio waves: microwaves), etc.

[2] Pressure sensor Examples of the pressure sensor include a piezoelectric sensor (see, for example, Japanese Patent No. 4139828), an air pressure sensor (for example, see Japanese Patent Application Laid-Open No. 2017-219341), and a load sensor (for example, Japanese Patent No. 6268219). (Refer to the official gazette).

[3] Thermistor (semiconductor temperature sensor)
A sensor using a thermistor is attached directly to the patient's nose and measures breathing by distinguishing between exhalation and inspiration from temperature changes due to airflow.

[4] Chest impedance A sensor using thoracic impedance passes a weak current between electrodes attached to the chest for electrocardiography and detects impedance changes as voltage changes.

[5] An adhesive sensor with a built-in acoustic acoustic transducer is attached to the patient's neck to measure the sound of air passing through the airways. By measuring this air sound at the _{same time as SpO 2} , the respiratory pattern (expiration / inspiration) is identified.

[6] Capnography Capnography detects respiratory movements by measuring the partial pressure of carbon dioxide contained in exhaled breath and inspiratory air.

_{[7] Oxymetry Respiratory rate using data obtained for measuring SpO 2} and pulse rate with a normal pulse oximeter, and utilizing the fact that the baseline of pulse waves fluctuates due to fluctuations in venous return due to respiration. Is calculated.

The control unit 31 acquires biometric information signals (I signal, Q signal) from, for example, one or more radio wave type sensors when acquiring N biometric information signals for which the same biometric information is measured. The control unit 31 further acquires a biometric information signal about the same biometric information as the radio wave sensor from one or more pressure sensors.

Alternatively, the control unit 31 acquires biometric information signals from two or more pressure sensors when acquiring N biometric information signals for which the same biometric information is measured.

The control unit 31 analyzes the autocorrelation of the loci of the points specified by the N biometric information signals in the N-dimensional space. That is, the control unit 31 is an analysis means. The autocorrelation is a measure showing the degree to which the time-series data to be processed matches the data obtained by shifting the data by a predetermined time, and is a function of the shift time.

Specifically, the control unit 31 determines the first point corresponding to the first time, the first time, and the predetermined time on the locus of the points specified by the N biometric information signals in the N-dimensional space. The sum of the second point corresponding to the second time, which differs by the time difference of, and the value corresponding to the norm of the vector between the two points, are added while changing the first time. The control unit 31 analyzes the autocorrelation by obtaining the sum for each predetermined time difference while changing the predetermined time difference.

The norm is a value having the concept of the length of a vector indicated by two points in N-dimensional data. The value according to the norm may be a value that matches the magnitude relation of the distance between two points in the N-dimensional space, and may not be a general norm. As a value according to the norm, for example, the sum of the absolute values of the differences between the two points and the sum of the powers of the differences between the two points can be used. It is not mandatory to take or multiply by some value.
Further, the predetermined time difference can be 0.
That is, when the first time is t, the biometric information signal at t is I (t), and the biometric information signal at the time (t + τ) deviated by the time difference τ from the first time t is I (t + τ), the norm is , | I (t) -I (t + τ) |, or a higher order such as ^{(I (t) -I (t + τ)) 2.}

In the present embodiment, the control unit 31 sets the first time at t, the N biometric information signals at _{t at I j} (t) (j = 1 to N), the predetermined time difference at τ, and the first. Z (τ) is obtained by the equation (1), where the upper limit of the time is k and the sum of τ is Z (τ).

Since the inside of the curly braces {} of the equation (1) is the sum of the squares of the differences in the signal values that differ by the time difference τ in each component of the N-dimensional data, Z (τ) takes a value of 0 or more, and τ = 0 becomes 0.

Z (τ) takes a minimum value when the time difference τ matches an integral multiple of the period of biometric information (opposite to the general autocorrelation function), so as shown in the following equation (2), The sign of Z (τ) normalized so as to be in the range of 0 or more and 1 or less is inverted, and the sum of 1 is defined as PACF (Position AutoCorrelation Function). As a result, PACF (τ) takes a value of 0 or more and 1 or less, and becomes 1 when τ = 0. PACF is a value indicating the autocorrelation of the locus of points specified by N biometric information signals in N-dimensional space.

Since the PACF value reflects the tendency of the entire measurement period, the influence of both the noise included as a whole and the noise superimposed on a part (burst noise, etc.) is reduced. On the other hand, when the periodicity of the measurement target such as respiration is not uniform, if data for a measurement period that is too long compared to the period is input, the value of the function PACF (τ) will not increase and the result will be inaccurate. In some cases. Therefore, it is sufficient to divide the data into the same to several cycles as the respiratory cycle and determine the measurement period to be input. The measurement period may be variable according to the latest measurement situation. In addition, the measurement period may be slid while overlapping part by part. If the unit time for calculating the respiratory rate is longer than the measurement period, the respiratory rates in multiple measurement periods obtained within the latest unit time are added (if there is overlap, weighting is performed according to the degree of overlap). May be).

The control unit 31 calculates a value related to the periodicity of the biometric information to be measured by obtaining the periodicity of the point locus from the autocorrelation of the point locus in the N-dimensional space. That is, the control unit 31 is a calculation means. The value related to the periodicity is not particularly limited as long as it is information representing the periodicity of the biological information. Examples of values related to periodicity include period, frequency (frequency), respiratory rate (respiratory rate per unit time), heart rate (heart rate per unit time), and the like.

[Operation of bedside terminal]
Next, the operation of the bedside terminal 30 will be described.
FIG. 4 is a flowchart showing a process executed by the bedside terminal 30. This processing is realized by software processing in collaboration with the CPU of the control unit 31 and the program stored in the storage unit 36.

The control unit 31 acquires N biometric information signals (I signal, Q signal, etc. of the radio wave sensor) for measuring respiration from the sensor units 40A and 40B via the I / F unit 37 (step). S1). The N biometric information signals are continuously output from the sensor units 40A and 40B, and the control unit 31 sets the range of the time series data used for processing to a predetermined time retroactively from the present.

Next, the control unit 31 performs preprocessing on each of the acquired N biometric information signals (step S2).
Pretreatment includes clutter calibration correction, body movement detection, bed leaving detection, bandpass filter processing, and the like.

Clutter calibration correction is a process of removing movement (noise) outside the measurement target from the signal. For example, when the signal value is far from a predetermined range, it is corrected so as to be within the range.

Body movement detection and bed leaving detection are processes for detecting body movement and getting out of bed based on the magnitude of speed and amplitude obtained from biological information signals (I signal, Q signal, etc.). The signal of the part different from the resting state shall not be used for the calculation of respiratory rate.

The bandpass filter process is a process of extracting only a specific frequency band (for example, a passing range: 4 to 150 bpm) from a signal.

Next, the control unit 31 performs periodic signal calculation processing on the N biometric information signals after the preprocessing (step S3).
The periodic signal calculation process includes a trend removal process (step S31), a PACF calculation process (step S32), a reliability index calculation process (step S33), and a recursive filter process (step S34).

The trend removing process (step S31) is a process for removing a trend component (a fluctuating component having a relatively low frequency) included in the biometric information signal.

(PACF calculation process)
The PACF calculation process (step S32) is a process of calculating PACF from N biometric information signals by the above equations (1) and (2). Here, a specific calculation method of PACF will be described.

<In the case of one dimension>
N is an integer of 2 or more, but in order to make Z (τ) and PACF (τ) easier to understand, the case of N = 1 will be described first. In the case of one dimension, the Z (τ) defined in the above equation (1) can be rewritten as the following equation (3). Here, I (t) is a biological information signal at time t.

FIG. 5A shows an example of the signal I (t) acquired as the biological information signal.
(A) The signal I (t + τ _{1 ) delayed by the time difference τ 1 with} respect to the waveform of the signal I (t) shifts _{I (t) to the left by the time difference τ 1} as shown in FIG. 5 (a). It becomes a waveform.
(B) For the square of the difference between I (t) and I (t + τ ₁ ) at time t, the sum Z (τ ₁ ) is calculated along the time t.
(C) _{While changing the time difference τ 1} , the processes of (A) and (B) are repeated in order for a certain interval (for one cycle or more), and the sum Z (τ) is calculated for each time difference τ.

(D) According to the above equation (2), the code of the normalized Z (τ) is inverted, and the value obtained by adding 1 is defined as PACF (τ). FIG. 5B shows an example of PACF (τ).
As PACF (τ), a waveform of the frequency included in the signal I (t) is obtained. The PACF (τ) has reduced noise as compared with the signal I (t).

<In the case of two dimensions>
In the case of two dimensions, the Z (τ) defined in the above equation (1) can be rewritten as the following equation (4). Here, I (t) and Q (t) are I signals and Q signals obtained from the radio wave sensor at time t.

FIG. 6 shows an example of time change of two biometric information signals (I signal / Q signal). In FIG. 6, the time t is set in the direction orthogonal to the IQ plane.
Consider signal I (t + τ) and signal Q (t + τ) delayed by a time difference τ with respect to the waveforms drawn by signal I (t) and signal Q (t).
While changing the time t, add (I (t) −I (t + τ)) ² + (Q (t) −Q (t + τ)) ² to obtain the sum Z (τ) at the time difference τ.
(I (t) -I (t + τ)) ² + (Q (t) -Q (t + τ)) ² is the point specified by (I (t), Q (t)) in the IQ plane. , (I (t + τ), Q (t + τ)) is a value corresponding to the square of the distance of the point specified.

After calculating Z (τ) according to the above formula (4), PACF (τ) is calculated according to the above formula (2).

<In the case of 4 dimensions>
In the case of four dimensions, rewriting Z (τ) defined in the above equation (1) gives the following equation (5). Here, I ₁ (t), Q ₁ (t), I ₂ (t), and Q ₂ (t) are I signals and Q signals obtained from two radio wave sensors, respectively.

As shown in equation (5), the sum Z (τ) in the time difference τ is used by using the difference between the points specified by the four biometric signals in the four-dimensional space and the points at different times by a predetermined time difference τ. ) Is calculated.
After calculating Z (τ) according to the above formula (5), PACF (τ) is calculated according to the above formula (2).

(Reliability index calculation process)
The reliability index calculation process (step S33) is a process of calculating the reliability index for PACF. The reliability index is a value indicating the reliability of data.

In calculating the reliability index, for example, the following method is used.
(A) In PACF, the reliability index is set to 0 when the total number of peaks within a certain period or individual peak intervals deviate from the respiratory measurement specification range (for example, 4 to 50 bpm).
(B) When the variance of the peak interval is larger than the predetermined value, the reliability index is reduced.
(C) In PACF, when the maximum value (maximum value) at τ = 0 is the first peak, the next maximum value is the second peak, and the height of the second peak is lower than that of the first peak, there is autocorrelation. Is judged to be small, and the reliability index is reduced.

(Recursive filtering)
The recursive filter process (step S34) is a process of adding and averaging time-series data using data acquired at a plurality of times, and is used for noise reduction of moving images and the like. Specifically, the data obtained by adding the latest data and the past data at a predetermined ratio is treated as the processed data.

The recursive filtering process for PACF will be described with reference to FIG. 7. The latest PACF shown in FIG. 7 is a PACF calculated from the latest measurement data (time series data). The PACF for calculating the respiratory rate is a PACF that reflects the data up to the previous PACF in terms of time. For example, the value of the latest PACF × 0.1 + PACF for calculating respiratory rate × 0.9 is updated as the next PACF for calculating respiratory rate.
The initial value of the PACF for calculating the respiratory rate is constant at 0.

When the reliability index obtained from the latest PACF is low, noise is removed by reducing the ratio of the latest PACF used for the recursive filtering process.

When body movement or getting out of bed is continuously detected, it is determined that the latest PACF has a low reliability index, and the respiratory rate calculated based on the old PACF is continuously displayed on the display unit 33. In order to avoid this, it is desirable to reduce the ratio of the respiratory rate calculation PACF used for the recursive filter treatment when detecting body movement or getting out of bed.

After step S3, the control unit 31 performs periodicity determination processing on the PACF after the recursive filter processing (step S4).
The periodicity determination process includes a peak detection process (step S41), an individual respiratory cycle calculation process (step S42), a respiratory cycle noise removal process (step S43), and a respiratory rate calculation process (step S44).

The peak detection process (step S41) is a process of detecting a peak (maximum value / minimum value) from the waveform of the PACF after the recursive filter process.

The individual respiratory cycle calculation process (step S42) is a process of obtaining the time between adjacent peaks (between the maximum value and the maximum value and between the minimum value and the minimum value) as individual respiratory cycles (peak intervals). .. Specifically, the control unit 31 determines the time from the first peak (maximum value of τ = 0) to the second peak (second maximum value), and the time from the second peak to the third peak (third maximum value). The time until, the time from the third peak to the fourth peak (the fourth maximum value), and the like are calculated as cycles.

The respiratory cycle noise removal process (step S43) is a process for removing noise from the calculated individual respiratory cycles. For example, of the individual respiratory cycles (peak intervals), the data for two from the largest and the data for two from the smallest are excluded.

The respiratory rate calculation process (step S44) is a process of calculating the average value of the respiratory cycle after removing noise and calculating the respiratory rate from this average value. Assuming that the average value of the respiratory cycle is T (seconds), the respiratory rate is 60 / T (bpm).

The control unit 31 causes the display unit 33 to display the respiratory rate calculated in this way. The respiratory rate value displayed on the display unit 33 is updated at sampling intervals for acquiring biometric information signals from the sensor units 40A and 40B.

The timing (phase) of the increase / decrease of the value of the signal measured from the living body differs depending on the time point at which the data is acquired.
On the other hand, PACF is defined to take the maximum value at τ = 0 as shown in equations (1) and (2), so when PACF is graphed, the left end (τ = 0) is always It becomes the maximum peak position. Therefore, no matter from which point in time the data (biological information signal) is acquired, the PACF phase (timing of increase / decrease of the value) is aligned at τ = 0. Since the PACF has such a feature, the recursive filter can be applied to the PACFs having different data acquisition start times.

[Example 1]
In the first embodiment, a case where two biometric information signals output from one microwave sensor (I signal, Q signal) is used will be described.
FIG. 8 shows the loci of the I signal and the Q signal obtained by measuring respiration in the IQ plane. In FIG. 8, the direction from the state A to the state B corresponds to the exhalation, and the direction from the state B to the state A corresponds to the inspiration.

9 (a) is the time series data of the respiratory waveform, FIGS. 9 (b) and 9 (c) are the time series data of the I signal and the Q signal, respectively, and FIG. 9 (d) is the PACF created from the I signal and the Q signal, respectively. Is. The horizontal axis of FIGS. 9A to 9C is the time t, and the horizontal axis of FIG. 9D is the time difference τ. 9 (a) to 9 (c) show the positions corresponding to the states A and B in the graph.
The time-series data of the respiratory waveform in FIG. 9A is not the data actually measured, but is for showing where each biological information signal corresponds to the respiratory waveform. The same applies to Examples 2 and 3.

The signal acquired from an actual sensor is often buried in noise, making it difficult to detect the frequency (frequency). Even if such a signal is included, by using PACF that reflects the overall movement of a plurality of signals, the frequency of the biological information to be measured (in the respiratory data, it corresponds to the respiratory rate per unit time). .) Can be detected with high accuracy.

Further, as shown in FIGS. 8 and 9 (c), the Q signal used in the first embodiment also decreases after the signal value increases when going from the state A to the state B, and changes from the state B to the state A. When heading, the signal value increases and then decreases. That is, the value of the Q signal repeats increasing and decreasing twice while actually making one round trip on the locus on the IQ plane. In this way, the PACF is not affected even when the Q signal or the I signal moves at a frequency twice that of the respiration to be measured.

Further, since the PACF always starts from the maximum value at τ = 0 (= the phase is aligned), the recursive filter can be applied to the PACF obtained at different timings, and there is an advantage that noise can be easily reduced. be.

[Example 2]
In the second embodiment, a case where four biometric information signals output from two microwave sensors (I signal / Q signal × 2) is used will be described.
FIG. 10A shows the loci of _{the I 1} signal and the Q ₁ signal obtained by the first microwave sensor with the respiration as the measurement target in the I ₁ −Q _{1 plane.} FIG. 10B shows the loci of _{the I 2} signal and the Q ₂ signal obtained by the second microwave sensor with the respiration as the measurement target in the I ₂ −Q _{2 plane.} In FIGS. 10A and 10B, the direction from the state A to the state B corresponds to the exhalation, and the direction from the state B to the state A corresponds to the inspiration.

11 (a) is the time-series data of the respiratory waveform, FIGS. 11 (b) and 11 (c) are _{the time-series data of the I 1} signal and the Q ₁ signal, respectively, and FIGS. 11 (d) and 11 (e) are I. _{Time-series data of 2} signals and Q ₂ signals, FIG. 11 (f) is a PACF created from _{I 1} signal, Q ₁ signal, I ₂ signal, and Q _{2 signal.} The horizontal axis of FIGS. 11 (a) to 11 (e) is the time t, and the horizontal axis of FIG. 11 (f) is the time difference τ. 11 (a) to 11 (e) show the positions corresponding to the states A and B in the graph.

Even when the number of sensors that output biometric information signals for certain biometric information is increased, the autocorrelation analysis method of biometric information using PACF can be easily applied.
Generally, the S / N ratio of the output signal changes depending on the positional relationship between the subject and the sensor. Therefore, by arranging the sensors at multiple positions, one of the sensors will be correct even if the patient moves. It is desirable to keep the patient in the state of being captured.
For example, as shown in FIG. 11 (d), even if _{the amplitude of the I 2} signal output from the second microwave sensor is small and the S / N ratio is poor due to being buried in noise, the PACF can be used. It is possible to comprehensively detect the periodic movement of the subject's breath from the four signals output from the 1-microwave sensor and the 2nd microwave sensor.

[Example 3]
In the third embodiment, a case where two microwave sensors (I signal / Q signal × 2) and five biometric information signals output from one piezoelectric sensor are used will be described.
_{I 1} signal obtained by the first microwave sensor _{for measurement of respiration, locus of Q 1} signal in the I _1- Q _{1 plane, I 2} signal obtained by the second microwave sensor for measurement of respiration, Q trajectory in _I 2 -Q ₂ plane ₂ signal is the same as that shown in FIG. 10 (a) and (b).

12 (a) is the time-series data of the respiratory waveform, FIGS. 12 (b) and 12 (c) are _{the time-series data of the I 1} signal and the Q ₁ signal, respectively, and FIGS. 12 (d) and 12 (e) are I. _{Time-series data of 2} signals and Q ₂ signals, FIG. 12 (f) shows time-series data of signals output from a piezoelectric sensor for measurement of respiration, and FIG. 12 (g) shows I ₁ signal, Q ₁ signal, and I ₂ signal, _{Q 2} signals, a PACF created from the piezoelectric sensor signals. The horizontal axis of FIGS. 12 (a) to 12 (f) is the time t, and the horizontal axis of FIG. 12 (g) is the time difference τ. 12 (a) to 12 (f) show the positions corresponding to the states A and B in the graph.

Even when sensors with different phases (timing of increase / decrease of value) such as microwave sensor and piezoelectric sensor are combined, the frequency of biometric information to be measured can be detected from the movement of all signals output from each sensor. can.

As described above, according to the present embodiment, the autocorrelation of the loci of the points specified by the N biometric information signals in the N-dimensional space is used to make the value related to the periodicity of the biometric information accurate. It can be calculated well. In the case of one biometric information signal, when there is a lot of noise or when the increase / decrease apparently shows a double period, the correct period or frequency may not be obtained, but in the present embodiment, the correct period and frequency may not be obtained. Periodicity can be comprehensively detected from a plurality of biological information signals.

Further, on the locus of points specified by N biometric information signals in N-dimensional space, the sum of the values corresponding to the norm of the vector between two points different by a predetermined time difference is used, and the self of the locus of the points is used. The correlation can be obtained.
Since the sum of the values according to the norm is a value of 0 or more, the "time difference" at which this sum is the minimum value can be determined to be an integral multiple of the period of the biological signal.

Specifically, by using Z (τ) defined by the above equation (1), it is possible to accurately calculate a value related to the periodicity of biological information.

Further, since the PACF (τ) defined by the above equation (2) includes a term in which the sign of Z (τ) normalized so as to be in the range of 0 or more and 1 or less is included, the PACF (τ) is , It becomes a function of τ that takes a value less than or equal to a predetermined value, and becomes the maximum value when τ = 0. Further, by setting the predetermined value to 1 so that the value of PACF (τ) is in the range of 0 or more and 1 or less, PACF (τ) = 1 at τ = 0. In this way, by using a new periodic signal that reflects the period of the biological information, it is possible to accurately detect the periodicity of the biological information.

The description in the above embodiment is an example of the biometric information processing device according to the present invention, and is not limited thereto. 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.

For example, in the above embodiment, the bedside terminal 30 calculates the value (respiratory rate) related to the periodicity of respiration, but the same living body is used in the sensor units 40A and 40B connected to the bedside terminal 30. When it is possible to acquire a plurality of biometric information signals for which information is to be measured, values related to the periodicity of the biometric information (cycle, frequency, respiratory rate, heart rate, etc.) are calculated in the sensor units 40A and 40B. It may be that.
Further, the station server 10 may calculate a value related to the periodicity of biometric information by using the data collected from the bedside terminal 30.
Further, a signal processing device having a PACF calculation unit and calculating a periodic value may be independently provided at a place where communication with the sensor units 40A and 40B is possible.

Further, in the above equation (2), the range of values after normalization for Z (τ) is set to 0 or more and 1 or less, and 1 is added to the normalized Z (τ). In the calculation, the range of values after normalization for Z (τ) and the value to be added to the normalized Z (τ) are not limited to the above examples.

Further, in order to equalize the influence of the biometric information signal acquired from each sensor on the calculation of PACF, the normalized signal waveform of each sensor may be used for the calculation of PACF. As normalization, for example, the amplitude of the signal waveform is made uniform by first subtracting the average value of the signal values from the signal waveform to obtain the amplitude at the center of 0, and then dividing by the standard deviation of the signal values. Further, the amplitude of the signal waveform may be set to 1. In particular, when different types of sensors (microwave sensor, piezoelectric sensor, etc.) are used as in Example 3, it is desirable to normalize the individual signal values.

Furthermore, the biometric information signal that correctly captures the movement of the patient, that is, the highly reliable biometric information signal, has a greater influence on the calculation of PACF than other biometric information signals, thereby improving the measurement accuracy. Can be done.
For example, different weights are provided for each biometric information signal, a signal obtained by multiplying the normalized signal by the weight is generated, and these are used in the calculation of PACF. In this case, a highly reliable biometric information signal is specified from the signal output for each sensor, and the weight of the highly reliable biometric information signal is increased.
Further, it is desirable to judge that the biometric information signal having a small variance (variation) of the peak interval of the signal waveform has high reliability and to increase the influence on the calculation of PACF.

Further, the program for executing each process in each device may be stored in the portable recording medium. Further, a carrier wave may be applied as a medium for providing program data via a communication line.

10 Station server 30 Bedside terminal 31 Control unit 32 Operation unit 33 Display unit 35 Wireless communication unit 36 Storage unit 37 I / F unit 40A, 40B Sensor unit 42 Sensor 100 Biometric information monitoring system

Claims (11)

An acquisition means for acquiring N biometric information signals (N is an integer of 2 or more) for the same biometric information as a measurement target, and
An analysis means for analyzing the autocorrelation of the locus of points specified by the N biometric information signals in the N-dimensional space, and an analysis means.
A calculation means for calculating a value related to the periodicity of the biological information to be measured by obtaining the periodicity of the locus of the point from the autocorrelation of the locus of the point in the N-dimensional space.
A biometric information processing device. The biometric information processing device according to claim 1, wherein the acquisition means acquires the biometric information signal from one or more radio wave sensors. The biometric information processing apparatus according to claim 2, wherein the acquisition means acquires an I signal and a Q signal different from the I signal by a predetermined phase as the biometric information signal from the radio wave sensor. The biometric information processing device according to claim 2 or 3, wherein the acquisition means further acquires the biometric information signal from one or more pressure sensors. The biometric information processing device according to claim 1, wherein the acquisition means acquires the biometric information signal from two or more pressure sensors. The biometric information processing device according to any one of claims 1 to 5, wherein the biometric information is respiration and / or heartbeat. The analysis means corresponds to a first point corresponding to the first time and a second time corresponding to a second time different from the first time by a predetermined time difference on the locus of the point in the N-dimensional space. The sum of the values corresponding to the norm of the vector between the two points and the two points is added while changing the first time, and the sum is calculated for each predetermined time difference while changing the predetermined time difference. The biometric information processing apparatus according to any one of claims 1 to 6, which analyzes the autocorrelation by obtaining the self-correlation. The analysis means sets the first time as t, the N biometric information signals at _{t as I j} (t) (j = 1 to N), the predetermined time difference as τ, and the upper limit of the first time. The bio-information processing apparatus according to claim 7, wherein Z (τ) is obtained by the formula (1), where the sum of k and τ is Z (τ).

The bio-information processing apparatus according to claim 7 or 8, wherein the sum is a minimum value when the predetermined time difference is an integral multiple of the period of the bio-information. An acquisition process for acquiring N biometric information signals (N is an integer of 2 or more) for the same biometric information as a measurement target, and an acquisition process.
An analysis step for analyzing the autocorrelation of the locus of points specified by the N biometric information signals in the N-dimensional space, and an analysis step.
A calculation step of calculating a value related to the periodicity of the biological information to be measured by obtaining the periodicity of the locus of the point from the autocorrelation of the locus of the point in the N-dimensional space.
Biometric information processing methods including. Computer,
An acquisition means for acquiring N biometric information signals (N is an integer of 2 or more) for the same biometric information as a measurement target.
An analysis means for analyzing the autocorrelation of the loci of points specified by the N biometric information signals in N-dimensional space.
A calculation means for calculating a value related to the periodicity of biometric information as a measurement target by obtaining the periodicity of the locus of the point from the autocorrelation of the locus of the point in the N-dimensional space.
A program to function as.

Images