JP2008068019A - Method and apparatus for outputting exhalation time - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an apparatus capable of precisely estimating a sleeping stage. <P>SOLUTION: An analyzing unit 28 has an input interface 26 for acquiring an exhalation signal containing a plurality of respiration cycles, a first analyzing function 21 for separating a first times t1 from the maximum to the minimum and second times t2 from the maximum to the minimum contained in a plurality of the respiration cycles and recording the average value of them on a memory 25 in a sampling time unit, a second analyzing function 22 for identifying whether either one of the first and second times t1 and t2 is an exhalation time in the sampling time unit and the function 24 of outputting either one of the average value t1a of the first times t1 and the average value t2a of the second times as the average value tea of the respiration time. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an apparatus and a method capable of outputting an expiration time from a respiratory signal.

  Patent Document 1 describes a system for accurately determining the sleep state of a subject including mid-wakening. This system includes a body motion detection unit, a heartbeat or pulse detection unit, a determination unit that processes outputs obtained therefrom to determine midway arousal and sleep state, and a unit that outputs a determination result. Yes.

  Patent Document 2 describes a movable bed that supports a caregiver who is sleeping without feeling mental stress so that the user can turn over in a natural state. For this purpose, it is described that biological information such as the brain wave, heart rate, respiratory rate, eye movement, body motion number, etc. of the cared person is detected. In addition, it is described that using a sheet-like biological information detection sensor (sheet-like sensor) because wearing a sensor such as an electrode on a cared person to obtain such biological information has a physical sense of restraint. ing. This sheet-like sensor has a sheet-like insulator arranged opposite to each other, and its capacitance fluctuates with the heartbeat or breathing movement of the cared person. The signal (biological information signal) obtained by this sheet-like sensor includes frequency components of heartbeat and respiration. Furthermore, it is described that a biological information signal is analyzed to obtain a heart rate, a respiratory rate, and a body motion number, and a sleep depth is estimated from the biological information.

  In Patent Documents 3 and 4, voltage fluctuations based on the respiratory motion of the human body are measured at regular intervals, and the positive peak value of the voltage and the interval (time) between adjacent peaks are calculated from the measurement results. It is described that a sleep state is estimated by obtaining an average value of peak intervals, a value based on dispersion of peak intervals, and the like from the obtained peak values and peak intervals. Conventionally, there is a sleep polygraph (polysonograph, PSG) method that detects brain waves, eye movements, jaw electromyography, etc., and determines the sleep depth from the detected waveform as a method for detecting a sleep state change. The equipment is large, unsuitable for daily use, and requires qualified personnel. On the other hand, the method of estimating sleep depth with emphasis on respiratory rate, heart rate, body movement information, especially fluctuation in heart rate can obtain biological information without restraining the human body, but compared with polysomnogram It is described that the accuracy is quite bad.

  In Patent Document 5, whether or not arousal is determined based on the peak interval and peak value ratio of the respiratory motion waveform of a human, and whether the waveform is an average value of the peak-to-peak area or whether the sleep is deep or shallow due to dispersion And controlling the temperature in the bed, which is a sleeping environment, so that the human body temperature adjustment functions effectively according to the determined sleep state.

  Patent Document 6 describes that the environmental state in the bedroom and the physiological state of the sleeping person are detected, the health state of the sleeping person is detected, and the sleeping state is made comfortable. The physiological state detecting means detects a sleeping state of the sleeping person, such as a brain wave, heartbeat, breathing, body movement, skin temperature, myoelectric potential, blood pressure, sweating, skin potential, and snoring. The environmental condition detection means detects the temperature, humidity, wind speed, radiant heat, etc. in the bedroom or the bed, controls the air conditioner, the environment control means in the bed, and generates a sedative scent from the fragrance generator. Is described.

  In Patent Document 7, a change in load corresponding to body movement due to sleep of a sleeper is generated as a respiratory signal, and the sleeper's apnea or hypopnea is determined based on a change in the frequency of the respiratory signal. It is described.

In Patent Document 8, in order to monitor a sleeping person's breathing state with higher accuracy, a plurality of sensors arranged under a sleeping person with a predetermined distribution and outputting signals corresponding to a load or vibration from the sleeping person are provided. In the respiratory monitoring device provided, each sensor is divided into phase groups having a phase width of π / 5, and the output signals of the sensors belonging to the group having the largest number of sensors in the phase group are shifted by π from the phase of this group. It is described that the respiration signal is calculated by averaging the signals obtained by inverting the output signals of the sensors belonging to a certain group.
JP 2002-34955 A JP 2004-121837 A JP-A-2005-118151 JP 2006-20810 A JP 2006-198023 A JP 7-328079 A JP 2004-24684 A JP 2005-198781 A

  A highly accurate method for determining the sleep depth is a PSG method that detects an electroencephalogram and uses the detected waveform. On the other hand, in contrast to the PSG method, the method of trying to determine the sleep state using a sheet-like sensor that does not give a sleeping feeling to the sleeper is the peak value of the respiratory signal in addition to the respiratory rate, heart rate, and body movement information. In order to improve the accuracy, the peak interval, the average value of the peak interval, the variance of the peak interval, and the like are taken into consideration. However, the accuracy of estimating the sleep state is not high with respect to the determination result of the PSG method.

  The plurality of respiratory cycles included in the respiratory signal includes an inhalation part for inhaling and an exhalation part for exhaling. Among these, the time of the expiration part, that is, the increase and decrease of the expiration time, and the low frequency component of the brain wave during sleep, for example, the amplitude component of the spectrum of 3 Hz or less (especially 0.5 Hz to 3.0 Hz) called δ wave The point of view considered to have a correlation between the increase and decrease of the sum of the values obtained. Therefore, by extracting the expiration time from the respiratory signal and determining the sleep state, an element corresponding to a low frequency component of an electroencephalogram, particularly an electroencephalogram that is affected by the sleep state, can be included in the determination element. For this reason, since the waveform component of the electroencephalogram or a component corresponding thereto can be added to the determination of the sleep stage without directly detecting the electroencephalogram, it does not give the sleeping person a sense of restraint with respect to the PSG method, for example, a sheet The sleep state can be accurately determined using the state sensor.

  When the respiratory cycle included in the respiratory signal is regarded as a minimum-maximum-minimum respiratory peak, one of minimum to maximum and maximum to minimum should be the inspiratory part and the other should be the expiratory part. is there. However, in the current situation in which the sleep state is determined using the breathing time, the number of breaths, etc. as parameters, the peak interval and the amplitude are mainly noticed, and therefore a method for extracting the exhalation part from the breathing signal is disclosed. Neither is it considered to extract the exhalation part when generating the respiratory signal.

  One embodiment of the present invention is an apparatus that can analyze a respiratory signal. The apparatus separates a means for obtaining a respiratory signal including a plurality of respiratory cycles, a first time from a local maximum to a local minimum included in the plurality of respiratory cycles, and a second time from a local minimum to a maximum; A means for recording a statistic of a first time, a second time and / or a value associated therewith in a memory with a predetermined time or a predetermined number of cycles as a sampling unit, and based on the statistic in a sampling unit; Means for identifying that one of the first time and the second time is an expiration time; and one of the first time statistic and the second time statistic as an expiration time statistic Means for outputting.

  First, the respiratory cycle (breathing peak) included in the respiratory signal is composed of an inspiratory part and an expiratory part. By the process of increasing the signal strength of the respiratory signal, the inspiratory part and the expiratory part are respirated. There is a possibility of regular replacement during Furthermore, even if an attempt is made to apply the law that the expiration time is longer than the inspiration time in the adjacent exhalation and inspiration due to the action of the autonomic nerve, if you try to judge at the individual breathing peak, the influence of noise etc. appears greatly and the data Reliability decreases. On the other hand, if an attempt is made to make a judgment from the entire respiratory signal obtained during sleep, it becomes difficult to discriminate due to the effect of switching between the inspiratory part and the expiratory part.

  In the apparatus according to one aspect of the present invention, from a minimum to a maximum, from a maximum to a minimum, using a predetermined time or a predetermined number of cycles as a sampling unit, not an individual respiration peak and not from a respiratory signal of the whole sleep. Each time is statistically processed as the second time and the first time. For example, an appropriate time between several tens of seconds to several minutes, specifically, between about 30 seconds and about 5 minutes is set as a sampling unit, and statistics during that time, for example, the first time and the second time are set. One time is identified as the expiration time based on the average value of.

  One means for generating a respiration signal is arranged in a predetermined distribution so as to detect a load change of the user in a lying state, and a plurality of detection elements that output signals corresponding to the load or vibration from the user Is a sensor unit. An example is a sheet-type sensor unit that can detect a load change of a user in a lying state, and includes a detection result of a sensor including a plurality of pressure-sensitive elements arranged in a matrix on a sheet-like support member. Based on this, a respiratory signal is generated. In the case of receiving a respiratory signal from such a sensor unit, the means for acquiring at least the signals of a plurality of chest detection elements that are likely to be under the chest of the user among the signals of the plurality of detection elements together with the respiratory signal. It is desirable to obtain.

  Further, the recording means selects, as a reference element, a chest detection element that outputs a signal having the maximum amplitude among the amplitude statistics of signals from each of the plurality of chest detection elements in a sampling unit. The statistics of the phase difference between the signal and the respiratory signal (for example, the average value of the phase difference) are stored as the statistics of the values related to the first and second times (for example, semiconductor memories such as SRAM and DRAM, It is desirable to record in a register or a hard disk. The identifying means recognizes one of the first time and the second time as an expiration time when the phase difference statistic is smaller than the first value in a sampling unit, and the phase difference statistic is When larger than the second value, it is possible to recognize the other of the first time and the second time as the expiration time.

  When a multipoint sensor unit including a plurality of detection elements is employed, the detection element under the chest detects a decrease in pressure during expiration and detects an increase in pressure during inspiration. Therefore, by confirming in advance the trend of the signal of the reference element, that is, whether the signal changes from minimum to maximum or from maximum to minimum when the pressure decreases, the phase difference from the signal from the reference element If there is not, the expiration time can be identified according to the signal trend of the reference element. For example, when the average of the phase difference is within the first range centered on zero (for example, 0 ± π / 10), the phase of the signal from the reference element matches the phase of the respiratory signal. One of the first time and the second time can be recognized as the expiration time according to the tendency of the signal of the reference element. On the other hand, when the average of the phase difference is within the second range centered around 180 degrees (for example, π ± π / 10), the phase of the signal from the reference element and the phase of the respiratory signal are inverted. Contrary to the signal tendency of the reference element, the other of the first time and the second time can be recognized as the expiration time.

  It is desirable that the means for discriminating can further apply the law that the expiration time is longer than the inspiration time in the adjacent exhalation and inspiration due to the action of the autonomic nerve. When the judgment based on the phase difference statistic cannot be clearly made, that is, the phase difference statistic is not included in the first range and the second range, the means for identifying is the first time statistic in sampling units. When the amount is longer than the second time statistic, the first time is recognized as the expiration time, and when the first time statistic is shorter than the second time statistic, the second time It is desirable to recognize time as expiration time. Independent of the phase difference statistic, the means for identifying is the sampling unit, and when the statistic for the first time is longer than the statistic for the second time, the first time is set to the expiration time. If the statistic for the first time is shorter than the statistic for the second time, the second time can be recognized as the expiration time.

  Another aspect of the present invention is a living body monitoring system including the analysis device described above and a sensor unit including a plurality of detection elements. This living body monitoring system can extract and output the expiration time included in the respiratory signal obtained from the sensor unit.

  Furthermore, this living body monitoring system estimates the sleep state by including the increase / decrease in expiration time statistics as a first factor corresponding to the increase / decrease in the intensity of the low frequency component of the electroencephalogram, and estimating the sleep state. It is desirable to further have a means for outputting. By determining the sleep state using the expiration time as the first element, an element corresponding to a low frequency component of an electroencephalogram, particularly an electroencephalogram that is affected by the sleep state, can be included in the determination element. For this reason, since it is possible to add the waveform component of the electroencephalogram or a component corresponding thereto to the determination of the sleep stage without directly detecting the electroencephalogram, the PSG method can be accurately performed without giving the sleeping person a sense of binding. A biological monitoring system capable of outputting an estimated value of a sleep state can be provided.

  According to another aspect of the present invention, the increase or decrease in the expiration time statistic output from the biological monitoring system and the analysis device corresponds to the increase or decrease in the intensity of the low frequency component of the electroencephalogram. It is an environment control system which has the apparatus which estimates a sleep state by including it in a judgment element as an element of, and controls at least one part of a living environment. The sleep stage or sleep state is estimated, and the temperature, scent, brightness, etc. of the bedroom are controlled based on the result, and more comfortable sleep and awakening can be provided by controlling the bed.

Still another aspect of the present invention is an analysis method including the following steps.
a1. Obtaining a respiratory signal that includes multiple respiratory cycles.
a2. The first time from the maximum to the minimum included in the plurality of respiratory cycles and the second time from the minimum to the maximum are separated, and the first time, with the predetermined time or the predetermined number of cycles as a sampling unit, Calculating statistics of the second time and / or their associated values and recording them in memory.
a3. Identifying one of the first time and the second time as expiration time in sampling units based on statistics.
a4. One of the first time statistic and the second time statistic is output as the expiration time statistic.

  The obtaining step (a1) includes a plurality of detection elements arranged in a predetermined distribution so as to detect a change in the load of the user in a lying state and outputting a signal corresponding to the load or vibration from the user. It is preferable to acquire at least signals of a plurality of chest detection elements that are likely to be under the chest of the user among the signals of the plurality of detection elements together with the respiratory signal based on the detection result of the sensor unit. In the recording step (a2), the chest detection element that outputs the signal having the maximum amplitude among the amplitude statistics of the signals from each of the plurality of chest detection elements is selected as a reference element in the sampling unit. Preferably, the statistic of the phase difference between the reference element signal and the respiratory signal is recorded in the memory. Further, in the identifying step (a3), when the statistic of the phase difference is within the first range centered on zero, one of the first time and the second time is recognized as the expiration time, and the phase difference Is within the second range centered on 180 degrees, it is preferable to recognize the other of the first time and the second time as the expiration time.

  Further, in the identifying step (a3), the phase difference statistic is not included in the first range and the second range, and the first time statistic is the second time statistic in sampling units. When the amount is longer than the amount, the first time is recognized as the expiration time, and when the first time statistic is shorter than the second time, the second time is recognized as the expiration time. Is preferred. In the identifying step (a3), when the statistic for the first time is longer than the statistic for the second time in a sampling unit, independently of the statistic of the phase difference, the first time May be recognized as the expiration time, and the second time may be recognized as the expiration time when the statistic of the first time is shorter than the statistic of the second time.

Still another embodiment of the present invention is a method including the above-described a1 to a3 and the following step a5.
a5. Either the first time statistic or the second time statistic is used as the expiration time statistic, and the increase or decrease in the expiration time statistic corresponds to the increase or decrease in the intensity of the low frequency component of the electroencephalogram. Estimate and output the sleep state by including it as a determination element.

Still another embodiment of the present invention is a method including the above-described a1 to a3 and the following step a6.
a6. Either the first time statistic or the second time statistic is used as the expiration time statistic, and the increase or decrease in the expiration time statistic corresponds to the increase or decrease in the intensity of the low frequency component of the electroencephalogram. Including at least a part of the living environment by estimating the sleep state by including it as a determination element.

  FIG. 1 shows an example of a bedroom home system. The home system 50 includes a biological information detection unit 10 including a sensor sheet 2 installed on a bed or a futon in a bedroom, and a control unit 20 for the bedroom. The control unit 20 is connected to the home LAN 60, and can control the environment in the bedroom by controlling the air conditioner 61, the light 62, and the aroma device 63 of the bedroom connected to the LAN 60. Therefore, the home system 50 has a function as an environment control system. Further, the control unit 20 sends information detected by the biological information detection unit 10 through the home LAN 60 and a result of analyzing the information to the monitoring unit 58. It is also possible to send to the external monitoring unit 59 via an external network connected to the home LAN 60 via the gateway 66, for example, the Internet 65. Therefore, the home system 50 has a function as a living body monitoring system.

  The biological information detection unit 10 uses a pressure sensor (pressure sensor) 7 as a detection element, a sensor sheet 2 in which a plurality of pressure sensitive elements 7 are arranged in an array or matrix, and a plurality of pressure sensitive elements 7. And a data processing unit 3 that collects signals and sends them to the control unit 20. The sensor sheet 2 is composed of a plurality of sub-sheets 2a, 2b and 2c. Each of the sub-sheets 2a, 2b and 2c uses a thin plastic sheet 4 as a base material. The sensor sheet 2 is configured by attaching (assembling) a plurality of pressure-sensitive elements 7 to each sheet 4 on a sheet-like support member to constitute a sheet-type sensor, and the plurality of pressure-sensitive elements 7 are arranged at appropriate intervals. Are arranged regularly. Therefore, it is possible to detect a load change of the user in a lying state by the plurality of pressure sensitive elements 7. The distribution of the pressure-sensitive elements 7 can be intensively arranged at a location corresponding to the chest when capturing breathing as the center, and is not limited to the above. Moreover, these pressure sensitive elements 7 can also be directly attached to a bedclothing such as a bed. The pressure sensitive element 7 may be any element having a function of outputting a signal corresponding to a load or vibration from the user, and examples thereof include a piezoelectric element and an electrostatic pressure sensor.

  The sheet 4 is also provided with wiring 8 for taking out signals from the plurality of pressure sensitive elements 7. Therefore, by placing the sensor sheet 2 on a bed or the like, a large number of pressure sensitive elements 7 can be arranged on the bed. For this reason, the signal (load signal) from the pressure-sensitive element 7 is a change in load applied to the bedding of the body movement of the sleeping person 9 without attaching a sensor or electrode directly to the subject (sleeping person) 9 lying on the bed. Can be converted to For this reason, by analyzing the load signal from the pressure-sensitive element 7, it is possible to monitor the sleeping state of the sleeping person (subject) 9 and other states.

  The data processing unit 3 generates a respiration signal from the load signal. For example, Patent Document 7 discloses that a body associated with respiration is subjected to fast Fourier transform (FFT) for frequency analysis of a signal from each pressure-sensitive element 7 and the power spectrum in the respiratory frequency component (δ wave component). It describes that a plurality of pressure-sensitive elements 7 that detect a load change according to movement are extracted. Further, it is described that a breathing curve (breathing signal) 39 is generated by adding signals that fall within a predetermined phase difference using the pressure sensitive element 7 having the largest power spectrum among them as a reference element.

  The control unit 20 can be configured using an appropriate hardware resource, for example, a computer including a memory (including a semiconductor memory such as a register and a RAM, a hard disk), a CPU, a display, and various interfaces. The control unit 20 includes a function as an analysis unit 28 that estimates a sleep state. In addition, the control unit 20 includes a function as the environment control unit 30 that outputs a control signal to the air conditioner 61, the light 62, the fragrance 63, and the like in the bedroom based on the estimated result via the home LAN 60. Further, the control unit 20 includes a display 29 for displaying and outputting the estimated result of the sleep state, the environmental control status, and the like.

  The analysis unit 28 receives the respiration signal 39 from the information processing unit 3 of the biological information detection system 10, analyzes the respiration signal stored in the memory 25 and the input interface 26 stored in the memory 25, and calculates the statistically processed value. The first analysis function 21 stored in the memory 25, the second analysis function 22 for judging the expiration time from the statistically processed values, the function 24 for outputting the expiration time, the increase / decrease in the expiration time, A third analysis function 23 that includes a determination element as a first element corresponding to an increase / decrease in the intensity of the frequency component and estimates the sleep state, and an output interface 27 that outputs the estimated result of the sleep state are included. The output interface 27 outputs the estimated sleep state to the environmental control unit 30 and sends it to the monitoring units 58 and / or 59 via the home LAN 60.

  FIG. 2 is a flowchart showing the processing in the control unit 20. In step 71, a respiration signal 39 including a plurality of respiration peaks is acquired by the input interface 26 and stored in the memory 25. As shown in FIG. 3A, the respiration signal 39 includes a plurality of cycles (respiration cycle, respiration curve) 38 indicating respiration. Breathing consists of inspiration and expiration. Accordingly, each breathing cycle 38 has an inhalation portion 36 and an expiration portion 37, and the breathing time tb is the sum of the inspiration time ti and the expiration time te. That is, the respiratory cycle 38 includes a maximum-minimum-maximum. When the analysis is performed centering on the interval between the peaks (respiration peaks) and the height, it is also possible to recognize the minimum-maximum-minimum respiration peak as one respiration cycle. During breathing, the respiratory cycle 38 is repeated a plurality of times, so that the respiratory signal 39 is a signal having a plurality of respiratory cycles 38.

In step 72, the first analysis function 21 causes the first time t1 of the first portion 36 from the local maximum to the local minimum included in each of the respiratory cycles 38 and the second time 37 of the second portion 37 from the local minimum to the local maximum. The time t2 of 2 is separated, statistics are taken at a predetermined sampling interval, and these statistics are recorded in the memory 25. In the respiration signal 39, the switching portion between the first portion 36 and the second portion 37 is a local maximum point and a local minimum point, and the position can be obtained from waveform differentiation. In this process, if the amplitude of the respiratory cycle 38 exceeds a predetermined value, it is cut as body movement noise. It also cuts shoulder noise. The shoulder noise is such that a small change as shown in FIG. Shoulder noise can be removed by deleting vertices that satisfy the following conditions.
(MX _i −MN _i−1 ) + (MX _{i + 1} −MN _i ) <Cs (MX _{i + 1} −MN _i−1 )
Deletes vertices MX _i and MN _i ,
(MX _i −MN _i ) + (MX _{i + 1} −MN _{i + 1} ) <Cs (MX _i −MN _{i + 1} )
Delete vertices MX _{i + 1} and MN _i
However, Cs is a threshold value, for example, 1.2.

  One of the first portion 36 and the second portion 37 is an exhalation portion. For example, in the case of a thermistor type nasal airflow sensor, the temperature rises during exhalation, and the temperature data is combined and acquired from the sensor side, so that the first part 36 and the second part 37 are connected to the exhalation part and the inhalation part. Can be identified. However, the identification method cannot be applied to the respiratory signal obtained from the sensor sheet 2.

  One of the other identification methods is to apply the law that the expiration time is longer than the inspiration time in the adjacent exhalation and inspiration due to the action of the autonomic nerve. However, there is no clear standard for the difference between the expiration time and the inspiration time, and it is often difficult to clearly distinguish between expiration and inspiration when the peak is such that shoulder noise rides as described above. .

  Furthermore, in the biological information detection unit 10 described above, a biological signal is detected using the sensor sheet 2 in which a plurality of pressure sensitive elements 7 are arranged in a predetermined distribution such as an array or a matrix. For example, as described in Patent Document 7 or Patent Document 8, in a sensor that detects a biological signal by a large number of detection elements, in order to remove noise and improve signal strength, the reference point is mainly used. A signal obtained by adding signals obtained at multiple points is normally output. The respiration signal is generated every time sampling of a signal from a sensor element such as a pressure sensitive element is repeated a predetermined number of times.

  For example, in Patent Document 8, when 256 cycles of load signals are processed, a person or an object is determined, and the presence / absence of a signal in the respiratory frequency band (δ wave component) is determined. Select a sensor group to calculate the respiratory signal. Therefore, every time the respiration signal is calculated, if the reference point or the reference sensor group changes, the phases of expiration and inspiration included in the respiration signal may be shifted or reversed. Furthermore, the reference point or the reference sensor group is selected based on the signal intensity, and is not limited to detecting the chest movement. The number of times of sampling for generating the respiration signal depends on the setting of the sensor unit. When sampling is performed every 0.1 seconds, the phase may be inverted every 25.6 seconds in the above case. is there. Actually, the possibility that the sleeping person's state changes every 25.6 seconds is very small, so the time interval for phase inversion and shifting is long, and in many cases it is a unit of several minutes.

  The above processing is excellent as a respiration signal for detecting a respiration interval, a respiration rate, and the like. However, when trying to separate inspiration and expiration, there is a possibility that the data becomes meaningless if the phase of the respiratory signal is reversed. For this reason, in the analysis unit 28 of the present example, in step 71, the input interface 26 can detect the movement of the chest among the pressure sensitive elements 7 in addition to the respiratory signal 39 from the biological information detection unit 10. The signal 41 of the breast detecting element that will be used is acquired. It is also possible to receive and analyze signals from all the pressure sensitive elements 7.

  In step 72, the first analysis function 21 determines the first time t1 in a predetermined sampling unit, for example, a sampling time unit of 25.6 seconds corresponding to the biological information detection unit 10 described above. The statistics with the time t2 of 2, for example, the total time or the average time are calculated, and the processing shown in FIG. 4 is performed. First, in step 81, an average value t1a for each sampling time of the first time t1 and an average value t2a for each sampling time of the second time t2 are calculated.

  In step 82, as shown in FIG. 5, among the pressure sensitive elements 7 of the sensor sheet 2, the pressure sensitive elements 7 arranged in the portion 7 a of the upper five rows are used as the chest detection elements, and from these pressure sensitive elements 7. The center of gravity, that is, the position considered to have the highest signal intensity is calculated from the average amplitude within the sampling time of the signal. In step 83, the reference row 7b including the pressure sensitive element 7 closest to the center of gravity is obtained. There is a high possibility that the sleeper's chest is placed on the reference row 7b. Further, in step 84, the pressure sensitive element 7 that outputs a signal having the maximum average amplitude within the sampling time among the pressure sensitive elements 7 included in the reference column 7b is set as the reference element 7c. In step 85, the phase difference between the signal of the reference element 7c and the respiratory signal 39 is obtained for each respiratory cycle, and the average value Pa of the phase differences within the sample time is obtained. In step 86, these average values t1a, t2a and Pa are stored in the memory 25.

  As shown in FIG. 5, among the pressure sensitive elements 7 arranged on the sensor sheet 2, the hatched element 7 x is a sensor element capable of detecting a sleeper's load or vibration. The signal from the element 7 is subjected to processing such as phase inversion and added. As shown in FIG. 6, for example, the signal of the chest reference element 7c obtained as described above and the signal of the abdominal pressure-sensitive element 7y of the same reference row 7b are inverted in phase. Therefore, if the signal strength of the chest reference element 7c is high, a respiration signal 39 in phase with the signal of the chest reference element 7c is output, and if the signal strength of the abdominal pressure sensitive element 7y is high, the signal of the chest reference element 7c. A respiration signal with the opposite phase is output.

  The reference element 7c under the chest detects that the pressure decreases during expiration, and detects that the pressure increases during inspiration. Therefore, by confirming in advance the tendency of the signal of the reference element 7c, that is, whether the signal changes from the minimum to the maximum or from the maximum to the minimum when the pressure decreases, the expiration included in the respiration signal 39 Time can be identified. In the following, it is assumed that the reference element 7c changes its signal from the minimum to the maximum when the pressure decreases, that is, during expiration.

  In step 73 of FIG. 2, in the control unit 20, the second analysis function 22 identifies that one of the first time t1 and the second time t2 is the expiration time for each sampling time. FIG. 7 shows the identifying step 73 in more detail. In step 91, when the average value of the phase difference is within the first range centered on zero, that is, within the range of 0 ± π / 10, the phase of the signal from the reference element 7c and the phase of the respiratory signal 39 are Are in agreement. Therefore, in step 92, the second time t2 that changes from the minimum to the maximum is recognized as the expiration time te. In step 93, when the average of the phase difference is in the second range centered on 180 degrees, that is, in the range of π ± π / 10, the phase of the signal from the reference element 7c and the phase of the respiratory signal 39 are Is inverted. Therefore, in step 94, the first time t1 changing from the maximum to the minimum is recognized as the expiration time te.

  Furthermore, when the average value of the phase difference is not included in the above range, the second analysis function 22 performs the expiration and the inspiration, the average value t1a of the first time, and the average value t2a of the second time. Judging by the size of. In step 95, when the average value t1a of the first time is larger than the average value t2a of the second time, in step 96, the first time t1 is recognized as the expiration time te. In the opposite case, in step 97, the second time t2 is recognized as the expiration time te.

  Further, in step 98, the second analysis function 22 obtains a ratio between the expiration time te obtained from the phase difference and the inspiration time ti on the opposite side. If the inspiratory time ti is 1.5 times or more of the expiratory time te, it is determined that the position of the chest is different from the above assumption, and in step 99, a process of switching the expiratory time and the inspiratory time is performed. For example, it is conceivable that the laying position of the sensor sheet 2 is shifted or the user's sleeping position is shifted.

  In step 74 shown in FIG. 2, the control unit 20 outputs either the first time average value t1a or the second time average value t1a from the output function 24 as the analysis result of the second analysis function 22. Is output as an average expiration time tea. The output function 24 can also calculate and output an average value in units of 5 minutes of the expiration time tea stored in the memory 25. The average value in units of 5 minutes indicates that the expiration time te at a certain time t0 is obtained as an average value of a plurality of expiration times te obtained 5 minutes before that time t0. It does not mean that only significant data can be obtained. The averaging time is not limited to 5 minutes. It is sufficient that the time is sufficient to remove noise. In the PSG method, EEG data is subjected to frequency analysis every 5 minutes. For this reason, calculating the average value for 5 minutes is meaningful in comparison with the PSG method.

In step 75, the third analysis function 23 estimates the sleep state as a step value based on the 5-minute average value tea of the expiration time te. For estimating the sleep stage, it is possible to use not only the expiration time te but also the parameters disclosed in the above patent documents. The determination of the expiration time tea includes a method using an average value itself and a method using a standardized numerical value. In this specification, standardization means that the i-th value tea (i) in units of 5 minutes is processed (converted) by the following formula (1) using an average value A and a standard deviation B for a certain time.
(Tea (i) -A) / B (1)

  The normal (average) value (reference value) during sleep of the value thus standardized is zero. The standardized conversion is to use the average value A and standard deviation B of sleep (overnight). If the analysis unit 28 is repeatedly used for a specific user, an average value of a plurality of sleeps can be used based on the past data of the user. On the other hand, when it is initially set or for an individual user, a real-time estimated value cannot be obtained if an attempt is made to standardize from the beginning. Therefore, it is desirable to set a general value as a reference value and shift to a determination based on a standardized value after a few hours have passed.

An example of a method for determining the sleep stage based on the standardized value Te (second) of the average value tea of the expiration time is as follows.
Te <−0.5s ・ Awakening or REM sleep −0.5s ≦ Te <0.8s ・ ・ Shallow non-REM sleep 0.8s ≦ Te ・ ・ ・ ・ ・ ・ ・ Deep non-REM sleep

An example of a method for determining the sleep stage based on a general value from the average value tea (second) of the expiration time is as follows.
tea <2.2s ・ Awakening or REM sleep 2.2s ≦ tea <3.2s ・ Shallow non-REM sleep 3.2s ≦ tea ・ ・ ・ ・ ・ ・ Deep non-REM sleep

  In step 76, the estimated value of the sleep stage is output from the output interface 27. Therefore, in step 77, the environment control function 30 controls the environment so as to lead to deep non-REM sleep in the early stage of sleep, and controls the environment so that it will occur at the time of awakening or REM sleep at the stage of waking up. It is possible to perform processing in accordance with the user's sleep state, such as to do.

  In the flowchart shown in FIG. 2, steps 71, 72 and 73 are performed in series. However, since these processes use the memory 25 as a buffer, each process is performed at an independent timing. It is possible to execute. Therefore, it is possible to output the user's sleep stage in real time with almost no delay.

  FIG. 8 shows the presence or absence of correlation between some information obtained from the respiratory signal and the low frequency component (δ wave component) of the electroencephalogram (EEG). The vertical axis represents the signal intensity and merely shows a tendency to increase or decrease. Changes in the 5-minute average tba of the breathing time and the 5-minute average tia of the inspiratory time are small, and no correlation with the low frequency component of the EEG is positively recognized. On the other hand, the change in the 5-minute average tea of the expiration time is relatively large, and a stable correlation is recognized with the low frequency component of the EEG.

  FIG. 9 to FIG. 11 further show the correlation using standardized data. FIG. 9 shows the standardized value Te of the 5-minute average value of the expiration time, the standardized value of the low frequency component of EEG (the 5-minute addition value of the amplitude component of the spectrum of the δ wave component, the same applies to FIGS. 10 and 11). Is shown. The tendency of increase / decrease in the standardized value Te of the expiration time and the low frequency component of the EEG is almost the same. In particular, a good correlation is seen in the early stages of sleep. Therefore, it can be seen that by setting an appropriate threshold value, it can be used as data suggesting the sleep stage in the same manner as the low frequency component of EEG.

  On the other hand, FIG. 10 shows the standardized value of the average respiratory rate for 5 minutes and the low frequency component of EEG. It is difficult to find a law in relation to increase / decrease. FIG. 11 shows the standardized value of the ratio of the average expiratory time to the average inspiratory time for 5 minutes and the low frequency component of EEG. Also in this figure, it is difficult to find a law property in relation to increase / decrease.

  FIG. 12 shows a comparison between the sleep stage obtained by the PSG method and the sleep stage obtained by the analysis unit 28 described above. FIG. 12A shows a sleep stage obtained by the PSG method. REM sleep is sleep in which rapid eye movement is observed. The electroencephalogram is mainly a relatively fast θ wave. In humans, it is said that REM sleep occupies 1 hour and a half to 2 hours out of 6 to 8 hours of sleep.

  Stage 1 (S1) to stage 4 (S4) are collectively called non-REM sleep. Stage 1 (S1) is in a somnolence state. On the brain wave, the α wave observed at awakening decreases, and a low-amplitude potential is observed. Stage 2 (S2) is a stage where a sleep spindle is observed on the electroencephalogram. Stage 3 (S3) is a stage in which low-frequency δ waves increase, and is about 20% to 50%. Stage 4 (S4) is a stage in which the δ wave is 50% or more.

  FIG. 12B shows a state in which the above-described determination criterion is applied to the standardized value Te. FIG. 12C shows the sleep stage output from the analysis unit 28. It can be seen that the sleep stage obtained by the PSG method almost coincides with the sleep stage and that the sleep stage can be accurately determined.

  The analysis unit 28 and the analysis method described above associate the expiration time with the low frequency component of the electroencephalogram based on the fact that the increase and decrease of the expiration time included in the respiratory signal is highly correlated with the increase and decrease of the low frequency component of the electroencephalogram. By judging, the sleep stage is judged. As a result, as described above, the sleep stage can be obtained with an accuracy comparable to that of the PSG method using the respiratory signal. In addition, since the input information for estimating the sleep state may be a respiratory signal, in the apparatus, system and method according to the present invention, it is not necessary to attach an electrode or the like to the sleeper, and the sleeper's restraint can be reduced. Therefore, a system that provides more comfortable sleep can be provided.

  Although the sleep state can be accurately estimated from the expiration time, the respiration signal includes a lot of information that is considered to be related to sleep such as the number of breaths as disclosed in the above-mentioned patent document. Therefore, in addition to the expiration time, other factors included in the respiratory signal can be added as a sleep state determination element, or the main element of the determination can be selected based on the transition of sleep. There is a possibility of improvement.

The figure which shows schematic structure of the home system for bedrooms. The flowchart which shows the method of acquiring and analyzing an expiration time. FIG. 3A shows an example of a respiratory signal, and FIG. 3B shows an example of a respiratory signal having a shoulder. The flowchart which shows the more specific process of a statistical process. The figure which shows a mode that the load is added to the sensor sheet | seat. An example of a respiratory signal and a signal from a pressure sensitive element. The flowchart which shows the more specific process of an identification process. The figure which shows the correlation with some information contained in a respiration signal, and the low frequency component of an electroencephalogram. The figure which shows the correlation with an average expiration time and an EEG low frequency component. The figure which shows the correlation with an average respiration frequency and an EEG low frequency component. The figure which shows the correlation with the expiration time / inhalation time and an EEG low frequency component. 12A shows a sleep stage obtained by the PSG method, FIG. 12B shows a change in expiration time, and FIG. 12C shows a sleep stage obtained by the above analysis method.

Explanation of symbols

2 sensor sheet, 3 information processing unit, 7 pressure sensitive element 10 biological information detection unit, 20 control unit for bedroom 28 analysis unit, 30 environment control unit 50 home system for bedroom

Claims (13)

Means for obtaining a respiratory signal comprising a plurality of respiratory cycles;
The first time from the local maximum to the local minimum included in the plurality of breathing cycles and the second time from the local minimum to the local maximum are separated, and the predetermined time or the predetermined number of cycles is used as a sampling unit. Means for recording a statistic of time, said second time and / or values associated therewith in a memory;
Means for identifying in the sampling unit that one of the first time and the second time is an expiration time based on the statistic;
An analysis apparatus comprising: means for outputting one of the first time statistic and the second time statistic as the expiration time statistic. In Claim 1, the said acquisition means is arrange | positioned by predetermined distribution so that the change of a user's load of a lying state can be detected, The several signal which outputs the signal corresponding to the load or vibration from the said user is output. Along with the respiratory signal based on the detection result of the sensor unit including the detection element, among the signals of the plurality of detection elements, obtain at least signals of a plurality of chest detection elements that are likely to be under the chest of the user,
The means for recording selects, as the reference element, a chest detection element that outputs a signal having the maximum amplitude among the amplitude statistics of the signals from each of the plurality of chest detection elements in the sampling unit, Record the statistic of the phase difference between the reference element signal and the respiratory signal in memory,
The means for recognizing recognizes one of the first time and the second time as the expiration time when the statistic of the phase difference is within a first range centered on zero, An analyzer that recognizes the other of the first time and the second time as the expiration time when the statistical amount of the phase difference is within a second range centered on 180 degrees.   3. The identifying unit according to claim 2, wherein the phase difference statistic is not included in the first range and the second range, and the first time statistic is the second unit in the sampling unit. The first time is recognized as the expiration time, and when the first time statistic is shorter than the second time statistic, the second time An analysis device for recognizing the time as the expiration time.   2. The identification means according to claim 1, wherein the first time is recognized as the expiration time when the statistical amount of the first time is longer than the statistical amount of the second time in the sampling unit. When the statistic of the first time is shorter than the statistic of the second time, the analyzer recognizes the second time as the expiration time. An analysis device according to claim 1;
A biological monitoring system comprising the sensor unit.   6. The sleep state is estimated by including the increase / decrease in expiration time statistics as a first element corresponding to the increase / decrease in the intensity of the low frequency component of the electroencephalogram, and the estimated value is output. A biological monitoring system further comprising means. A living body monitoring system according to claim 5;
An increase / decrease in the expiration time statistic output from the analysis device is included in the determination element as a first element corresponding to an increase / decrease in the intensity of the low frequency component of the electroencephalogram to estimate the sleep state, and at least one of the living environments And an environment control system. Acquiring a respiratory signal including multiple respiratory cycles;
The first time from the local maximum to the local minimum included in the plurality of breathing cycles and the second time from the local minimum to the local maximum are separated, and the predetermined time or the predetermined number of cycles is used as a sampling unit. Calculating statistics of time, said second time and / or values associated therewith, and recording those statistics in memory;
Identifying one of the first time and the second time as expiration time based on the statistics in the sampling unit;
And outputting either one of the first time statistic and the second time statistic as the expiration time statistic. 9. The acquisition step according to claim 8, wherein the obtaining step includes a plurality of signals arranged in a predetermined distribution so as to detect a load change of the user in a lying state and outputting a signal corresponding to the load or vibration from the user. Along with the respiratory signal based on the detection result of the sensor unit including the detection element, among the signals of the plurality of detection elements, obtain at least signals of a plurality of chest detection elements that are likely to be under the chest of the user,
In the recording step, in the sampling unit, the chest detection element that outputs a signal having the maximum amplitude among the amplitude statistics of the signals from each of the plurality of chest detection elements is selected as a reference element, and Record the statistics of the phase difference between the reference element signal and the respiratory signal in memory
In the identifying step, when the statistic of the phase difference is within a first range centered on zero, one of the first time and the second time is recognized as the expiration time, The analysis method of recognizing the other of the first time and the second time as the expiration time when the statistical amount of the phase difference is within a second range centered on 180 degrees.   10. The identifying step according to claim 9, wherein the phase difference statistic is not included in the first range and the second range, and the first time statistic is the first unit in the sampling unit. The first time is recognized as the expiration time, and when the first time statistic is shorter than the second time statistic, the first time is recognized as the expiration time. 2. An analysis method for recognizing time 2 as the expiration time.   9. The identifying step according to claim 8, wherein the first time is recognized as the expiration time when the statistical amount of the first time is longer than the statistical amount of the second time in the sampling unit. Then, when the statistic of the first time is shorter than the statistic of the second time, the analysis method recognizes the second time as the expiration time. Acquiring a respiratory signal including multiple respiratory cycles;
The first time from the local maximum to the local minimum included in the plurality of breathing cycles and the second time from the local minimum to the local maximum are separated, and the predetermined time or the predetermined number of cycles is used as a sampling unit. Calculating statistics of time, said second time and / or values associated therewith, and recording those statistics in memory;
Identifying one of the first time and the second time as expiration time based on the statistics in the sampling unit;
One of the first time statistic and the second time statistic is used as the expiration time statistic, and the increase / decrease in the expiration time statistic is an increase / decrease in the intensity of the low frequency component of the electroencephalogram. A method including estimating and outputting a sleep state included in a determination element as a corresponding first element. Acquiring a respiratory signal including multiple respiratory cycles;
The first time from the local maximum to the local minimum included in the plurality of breathing cycles and the second time from the local minimum to the local maximum are separated, and the predetermined time or the predetermined number of cycles is used as a sampling unit. Calculating statistics of time, said second time and / or values associated therewith, and recording those statistics in memory;
Identifying one of the first time and the second time as expiration time based on the statistics in the sampling unit;
One of the first time statistic and the second time statistic is used as the expiration time statistic, and the increase / decrease in the expiration time statistic is an increase / decrease in the intensity of the low frequency component of the electroencephalogram. Including at least a part of the living environment by estimating a sleep state included in the determination element as a corresponding first element.

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