KR20160087762A

KR20160087762A - Method for inter-sleep analysis based on biomedical signal

Info

Publication number: KR20160087762A
Application number: KR1020160004005A
Authority: KR
Inventors: 박광석; 윤희남; 김상경; 이원규; 한정민; 정다운
Original assignee: 서울대학교산학협력단
Priority date: 2015-01-14
Filing date: 2016-01-13
Publication date: 2016-07-22
Also published as: KR101771835B1

Abstract

In the present invention, biometric signals are measured from two persons taking sleep in the same space to extract biometric information, analysis of synchronization characteristics between living systems, analysis of sleep correlation, analysis of influence characteristics between living systems, The present invention relates to a method for analyzing mutual influences based on bio-signals based on sleep interactions, and provides various indicators for judging the interaction of two persons sleeping.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001]

More particularly, the present invention relates to a method for analyzing sleep mutual influences, and more particularly, to a method for analyzing sleep mutual influences by two persons taking sleep in the same space, And analyzing the influence characteristics between the living systems to analyze the sleep mutual influence.

Sleep accounts for about one-third of human life, and plays an important role in energy storage and recovery as well as hormone secretion and memory coagulation. If you do not have enough sleep or a good night's sleep, your mental activity becomes blurred and your body becomes insensitive. In modern society, people are increasingly complaining of sleep disturbances due to increased stress, aging, drugs, and changes in the sleep cycle. Sleep disorders can be divided into sleep disorders such as insomnia, excessive sleep, narcolepsy, and sleep apnea, and night sleep, night sleep, and sleepwalking.

Polysomnography (PSG) is a test to determine the cause of a disease that occurs during sleep. A variety of measurement signals obtained through electroencephalogram, electrocardiogram, safety (eye awake), EMG, and video recording are combined to determine sleep state, sleep apnea, arrhythmia, and rapid eye movements. Methods commonly used to analyze sleep stages of sleepers include frequency analysis of electroencephalograms, methods of utilizing oxygen saturation to determine the state of sleep by measuring blood oxygen saturation, A method of measuring an activity using an applied Actigraph, and a method of using a heart rate variability. However, most of the conventional techniques are limited to techniques for analyzing the sleeping structure or sleep-related disease of an individual by using bio-signals and techniques for analyzing the binding characteristics between the living body systems. In addition, There has been no research on the technology of analyzing. It is an important task for family health to investigate the mutual effects of sleep on a person's bed, a brother or sister who sleeps in a bed or a neighboring bed because sleep disturbance or sleeping on either side can affect others.

The maternal and fetal heart rate was maintained at a certain rate during fetal-maternal heart rate (MRI). The maternal heart rate was measured as the maternal heart rate coordination, 2009, PNAS). In addition, there is also a research result (2003, The American Journal of Physiology - Regulatory, Integrative and Comparative Physiology) about the cardiopulmonary coupling characteristic according to the sleep of a newborn. However, . No data has been reported analyzing the interactions between the two sleepers.

Korean Patent Laid-Open Publication No. 2014-0120513 relates to an apparatus and method for determining a sleep phase, which comprises measuring a raw signal including a heartbeat signal and a motion signal, calculating a heart rate parameter and motion information based on the measured raw signal, The user determines the sleeping phase by judging the sleeping phase by using the heart rate information and the motion information calculated after the calculation. However, this is a method of judging the sleep stage of an individual, and is far from evaluating sleep effects on others.

Korea Patent Publication No. 2014-0120513

In view of the above, it is an object of the present invention to provide a variety of indicators for determining the interaction between sleeping surfaces of two people based on binding characteristics based on various bio-signals measured from two persons taking sleep in one space .

The present invention relates to a method for analyzing a sleep-related mutual influence based on a living body signal, the method comprising: measuring respiration, electrocardiogram and electroencephalogram signals, which are biological signals during sleep, from two persons taking sleep in the same space, Extracting a breathing interval, a heartbeat position, a heartbeat interval, a band EEG, and a sleeping phase, and acquiring phase information and instantaneous phase information of the biometric information from the extracted information; Calculating a synchronization index by applying a synchronization analysis technique to the phase of the same biometric information measured by the two persons, determining that the synchronization index is synchronized when the synchronization index value is equal to or greater than a predetermined value, detecting the synchronization interval, Analyzing synchronization characteristics between biological systems by calculating a ratio and quantifying the ratio; The sleeping phase of the two persons is analyzed to analyze the cross correlation, and the heartbeat interval is extracted from the heartbeat positions of the two persons. Calculating the average heart rate in units of 30 seconds, calculating the average heart rate tendency by smoothing the average heart rate, analyzing the correlation by cross correlation analysis of the average heart rate tendency, and analyzing the correlation between 30 seconds The unit power is calculated and smoothed to analyze the cross correlation, and the average respiratory rate is calculated and smoothed in the interval of 30 seconds in the respiratory interval of the two persons, and the cross correlation is analyzed to calculate the delay time and the correlation coefficient value of the cross correlation A correlation analysis step between biological systems; And a degree of contribution of one person's biometric information to a change in the phase of the biometric information of either one of the two persons is evaluated by an EMA (Evolution Map Approach) method, directional calculation is performed in units of 30 seconds, The heart rate variability index is calculated from the heart rate interval to analyze the autonomic nervous system effect and the coherence index is calculated from the band EEG to analyze the influence of the central nervous system and determine the strength of the influence as the direction of the directional calculation, Analyzing and analyzing the influence of the central nervous system on the basis of the difference of the directional calculation, and calculating a positive effect or a negative effect on the difference between the respective sleep-related HRV index and the coherent index; And calculating the number and duration of the sleeping step of the other person in the state where the sleeping phase of one of the two persons has changed and converting the number and duration of the sleeping step of the other person into a ratio to the whole sleeping face, And a mutual influence analysis step, wherein when the signal is measured in the biometric information acquisition step, the awake interval of any one of the two persons is excluded as a distortion section, and the synchronization interval is set between biometric signals measured during sleep , A bio-signal-based sleep mutual influence analysis method is provided.

The bio-information acquisition step may further include calculating a sleeping phase from the electroencephalogram signal, extracting a band EEG using a bandpass filter, extracting a heartbeat position and a heartbeat interval from the electrocardiogram signal, Obtains a breathing interval from the extracted heartbeat position, obtains a band respiration signal using a bandpass filter, applies a Hilbert transform to the band brainwave and the band respiration signal to obtain a phase, and acquires an instantaneous phase from the extracted heartbeat position, Provides a method for analyzing signal-based sleep interactions.

The present invention also provides a method for analyzing synchronization characteristics between living body systems, wherein the biometric information includes at least one of a heart rate interval, a respiration interval, a delta wave of a band EEG, a seta wave, an alpha wave, a sigma wave, .

The present invention is also characterized in that the index for calculating the positive influence or the negative influence in the biosimilar influence characteristic evaluation step is calculated by calculating an average value for the total sleep surface to evaluate the influence on the entire sleep surface, A sleep-phase mutual influence analysis method based on a bio-signal, which evaluates influences by sleep stages.

The sleep mutual influence analysis step may further include a sleep-wakefulness effect analysis that calculates the number of times the other person wakes up due to the awake of either one of the two persons, Provides a method for analyzing sleep mutual effects based on bio-signals.

The present invention also provides a bio-signal-based sleep inter-effect analysis method for further measuring the degree of safety and in-situ signals in order to calculate the sleep phase.

Techniques for analyzing the binding characteristics of biological signals and systems can be applied to biorhythm induction and furthermore, sleep induction analysis techniques in combination with external stimulation techniques during sleep. Further, the present invention can be applied to a technique for analyzing sleep-related diseases by analyzing binding characteristics between living body systems.

The technique proposed in the present invention can be applied not only to the sleeping but also to the field of identifying social interaction among people in everyday life, and can also be applied to the field of analyzing external stimuli and binding characteristics for improving concentration.

FIG. 1 is a conceptual diagram illustrating a sleep correlation analysis relationship between two persons based on a bio-signal, according to an embodiment of the present invention.
FIG. 2A is a graph showing a section in which the heartbeats of two persons are synchronized with each other through the " analysis of synchronization characteristics between living body systems " when two persons take sleep in the same space, The average heart rate in seconds is shown in blue and red, respectively.
FIG. 2B is a graph showing the interval in which the heartbeats of two persons are synchronized through the " analysis of synchronization characteristics between the living body systems " when the two persons take sleep in separate spaces, and FIG. The average heart rate in seconds is shown in blue and red, respectively.
FIG. 2C is a graph obtained by smoothing the average heart rate of the two persons shown in FIG. 2A, and a graph obtained by cross-correlation analysis of the average heart rate of the two smoothed persons.
FIG. 2D shows a graph obtained by smoothing the average heart rate of the two persons shown in FIG. 2B, and a graph obtained by cross-correlation analysis of the average heart rate of the two smoothed persons.
FIG. 3 is a graph showing a case where a person's awakening has an effect on a person's awakening (yellow circle) according to an embodiment of the present invention.
Figure 4 is a graph illustrating the sleeping steps of two persons in accordance with one embodiment of the present invention.
5A is a graph showing a heart rate position and a heart rate interval in an electrocardiogram signal.
5B is a graph showing respiratory intervals in the respiration signal.
FIG. 5C is a graph showing the k-complex and sleep spindle occurring in the initial 15 seconds or 15 seconds of the sleeping step N2.
FIG. 5D is a graph showing an average adult sleep phase and brain waves. FIG.

In one aspect, the invention relates to a method for analyzing sleep interactions of two persons based on bio-signals.

The biological signal of the present invention refers to an electrical signal between fine cells of a human body, and is a fine signal appearing from a few mV to a few μV. Electroencephalogram (EMG), electromyogram, electrocardiogram (ECG), etc. are typical signals and are generally used to judge the condition of a patient in the medical field. According to the present invention, by analyzing the binding characteristics of biological systems based on various bio-signals measured from two sleeping persons in one space, various indexes for judging the interaction of two people's sleep surfaces are calculated and quantified, It provides a method of analysis.

As shown in FIG. 1, the method for analyzing sleep mutual influences of two people based on the bio-signal of the present invention is a method of analyzing breathing, electrocardiogram (ECG) and electroencephalogram (EEG), which are biological signals measured during sleep from two persons taking sleep in a space And extracts biometric information such as breath interval, heart rate interval, heart rate position, band EEG, and motion information. And acquiring phase information and an instantaneous phase of each biometric information on the basis thereof. In one embodiment of the present invention, when the signal is measured in the biometric information acquisition step, a section due to the movement of one of the two persons is excluded as a distortion section. In one embodiment of the present invention, the bio-information acquisition step may include extracting a band EEG using the band-pass filter for the EEG signal, extracting a position of a heartbeat from the ECG signal, Acquires a band respiration signal, applies a Hilbert transform to the band EEG and the band respiration signal to obtain a phase, and acquires an instant phase from the extracted heart beat position.

The electrocardiogram or heartbeat signal in the present invention is as shown in FIG. 5A. The heartbeat position is the position at which the R peak, which is the highest and sharpest waveform in the electrocardiogram, appears. Also, the time interval between the current heart rate position and the previous heart rate position is called the heart rate interval. Similarly, the respiratory interval refers to the distance between the respiratory signal peaks, as shown in FIG. 5B.

In the present invention, the sleeping phase refers to that which is evaluated by a " sleeping polygraph test " performed in a hospital. In other words, the sleep phase can be judged based on changes in the characteristics of the EEG measured during sleep, and it is preferable to judge the changes in the characteristics of the EOG and chin EMG along with the electroencephalogram have. In this sleep phase standard classification, there are five stages of W, N1, N2, N3, and R. According to the method introduced in the American Academy of Sleep Medicine (AASM), "The AASM Manual for the Scoring of Sleep and Associated Events," the characteristics of the electroencephalogram, If it meets the condition of the sleeping phase, the sleeping phase is determined.

In the present invention, the electroencephalogram (EEG) is also referred to as an electroencephalogram (EEG), which is an electric current generated when a signal is transmitted between the nervous system and the brain, and is recorded through an electrode attached to the scalp as a neurophysiological measurement method for electrical activity of the brain. These types of EEG can be classified according to frequency and amplitude, and basically 0 ~ 30Hz, about 20 ~ 200μV amplitude are shown and they are named by frequency band as shown in Table 1.

[Table 1, EEG names and functions by band]

These delta, theta, alpha, beta, and gamma waves are frequency regions of EEG classified arbitrarily for convenience, and the range of bands may be variable depending on the research. When analyzing EEG, we perform power spectrum analysis that shows the distribution of power for each frequency component as a whole.

Sleep stages can be summarized as follows.

-W: If ALPHA occurs for more than 15 seconds, blinking of the eye, activation of the EMG

-N1: If the alpha wave is decreased or eliminated, and the setta wave lasts more than 15 seconds

-N2: k-complex or sleep spindle occurred more than once in the initial 15 seconds or in the previous 15 seconds

-N3: Delta wave 0.5 ~ 2Hz EEG is greater than 75μV and more than 6 seconds

-R: Fast eye movements appear in safety, and theta waves appear

If necessary, N1 and N2 can be grouped into a single step, called Light sleep (LS), and N3 is called Deep sleep (DS), or W-L-D-R. R is the REM sleep (LS). In order to measure the sleep phase, it is necessary to measure signals of the electroencephalogram, the degree of safety, and the electromyogram of the jaw. However, since there are disadvantages of attaching many sensors, it is necessary to use only a single electroencephalogram or a relatively simple bioelectric signal May be used to estimate sleep stages.

These brain waves or electroencephalograms are related to sleep stages as shown in Table 2.

[Table 2, Relationship between sleep phases and brain waves]

In an embodiment of the present invention, synchronization characteristics between biological systems are confirmed through synchronization analysis. Specifically, a synchronization index is calculated by applying a synchronization analysis technique to the phase of the same biometric information measured by the two persons, System synchronization characteristic analysis step of determining that the synchronization index value is synchronized when the synchronization index value is equal to or greater than a predetermined value and calculating a ratio of the synchronization interval divided by the entire sleep interval. In an embodiment of the present invention, the synchronization interval is set between the bio-signals measured during sleep. In one embodiment of the present invention, the bio-information uses a heart rate interval, a respiratory interval, and a delta wave, a stereo wave, an alpha wave, a sigma wave, and a beta wave of a band EEG.

In one embodiment of the present invention, cross correlation is analyzed by smoothing the sleep phase, the average heart rate, the average respiratory rate, and the band EEG power of the two persons. Specifically, the two sleep stages are smoothed to analyze the correlation by analyzing the correlation, the heart rate interval is extracted from the heart rate positions of the two persons, the heart rate interval is calculated in units of 30 seconds, and the average heart rate is calculated, The average heart rate tendency is calculated, and the correlation is analyzed by cross correlation analysis of the average heart rate tendency. The power of 30 second power of the two band EEGs is calculated and smoothed, And analyzing the correlation of the average respiration tendency by cross correlation analysis by calculating and smoothing the average respiratory rate in 30 seconds in the respiratory intervals of the two persons.

One embodiment of the present invention includes a step of analyzing the influence characteristics between biological systems. Specifically, (1) the degree of contribution of one person's biometric information to the biometric information phase of one of the two persons is changed, and the directional calculation is performed in units of 30 seconds by evaluating each degree by the EMA (Evolution Map Approach) method . (2) The autonomic nervous system effect is analyzed by calculating the heart rate index from the heartbeat interval among the above biometric information. (3) Analyze the influence of the central nervous system by calculating the coherence index from the band EEG. Wherein the method comprises the steps of: determining a strength of influence as a direction of the directional calculation; and determining a strength of influence of the directional calculation based on a ratio of the difference between each of the sleep-related heart rate variability index and coherent index according to the directional calculation difference in the autonomic nervous system effect analysis, Calculate the impact or adverse impact. In an embodiment of the present invention, the index for calculating the positive influence or the negative influence in the analysis of the influence characteristics between biomedical systems may include calculating an average value for the total sleep surface to evaluate the influence on the entire sleep surface, To assess the influence of each step on the sleep.

In one embodiment of the present invention, the number and duration of the sleeping steps of the other person are changed in the state where the sleeping phases of the two persons have changed, and the number and duration of the sleeping steps of the other person are changed , And a step of analyzing the sleep mutual influence. In an embodiment of the present invention, the sleep mutual influence analysis step may include analyzing a sleep-wakefulness impact analysis, which is a sleep-wakefulness effect index, by calculating the number of times that another person breaks due to the motion information of one of the two persons .

Hereinafter, embodiments are provided to facilitate understanding of the present invention. However, the following examples are provided only for the purpose of easier understanding of the present invention, and the scope of the present invention is not limited to the following examples.

Example 1. Obtain biometric information from bio-signals measured during sleep

Respiration, electrocardiogram and EEG signals measured from two persons during sleep were used. The measured EEG was extracted by using a passband filter. The band of the applied filter and the extracted EEG were as follows.

- 0.5 to 3 Hz: Delta wave

- 3 ~ 8Hz: Theta wave

- 8 ~ 12Hz: Alpha wave

- 12 to 16 Hz: Sigma wave

- 16 ~ 25Hz: Beta wave

From the measured ECG, the R peak was detected and the position of the heartbeat was displayed. The respiration signal was obtained by applying the measured 0.15 ~ 0.5Hz bandpass filter to the measured respiration signal. A phase was obtained by applying a Hilbert transform to the extracted EEG and processed respiration signals as shown in Equation 1 below.

[Equation 1]

An instantaneous phase was obtained from the extracted heartbeat position using the following equation (2), and a distorted section was obtained as a motion section in the bio-signal measurement.

[Equation 2]

Example 2. The living body Between systems How to Analyze Synchronization Characteristics

1) Phase synchronization analysis technology

There is a variety of techniques for phase synchronization analysis, so it is not limited to any. The following description specifies a representative technique. The phase of the previously calculated bio-signal (eg, the phase of the A-delta wave and the phase of the B-delta wave) was used.

Each phase is accumulated to obtain a cumulative phase,

And the phase difference was calculated using Equation 3 by multiplying the cumulative phase by a specific constant value.

[Equation 3]

The remainder obtained by dividing the calculated phase difference by 2?,? N, m is calculated by using Equation (4).

[Equation 4]

The probability mass function of? N, m,

, And one of the synchronization indexes

Was calculated by using Equation 5.

[Equation 5]

remind

From one of the synchronization indicators

Was calculated using equation (6).

[Equation 6]

The cumulative phase of the two biomedical signals is divided by a constant value (ratio of two biomedical signal periods) to 2 pi to calculate the remaining value. (Equation 7)

[Equation 7]

The distribution of the phase residuals of the second signal corresponding to the phase remainder of the first signal is extracted and the average of them is extracted using Equation (8).

[Equation 8]

The average of the average values calculated above is calculated,

Was calculated by using Equation (9).

[Equation 9]

If the index value is above a certain threshold value, it is determined that synchronization has occurred and the method is applied to all previously extracted bio-signals (heartbeat-heartbeat, respiratory-respiration, band EEG-band EEG) (%), And the influence between the biological systems was quantified.

Example 3. Living body Between systems Correlation analysis technology

Referring to FIGS. 2A to 2D, cross correlation was analyzed by smoothing the two sleep stages, the average heart rate, the average respiration rate, and the band EEG power. To accomplish this, the time delay and the correlation coefficient were calculated by smoothing the sleep stages of two persons and performing cross correlation analysis. Also, the heart rate interval was extracted from the heart rate positions of two persons, and the average heart rate was calculated and smoothed at a heart rate interval of 30 seconds. Time delay and correlation coefficient were calculated by cross correlation analysis of average heart rate tendency of two persons measured during sleep. The power of the band EEG power of two persons was calculated and smoothed to calculate the band EEG power tendency. The time delay and the correlation coefficient were calculated by analyzing cross - correlation between the two groups' EEG power trends. The average respiratory rate was calculated by calculating the average respiratory rate in 30 seconds interval between two persons and smoothed to calculate the average respiratory rate tendency and the time delay and correlation coefficient were calculated by cross correlation analysis of the average respiratory rate tendency of two persons Respectively.

FIG. 2A is a graph showing a section in which the heartbeats of two persons are synchronized with each other through the " analysis of synchronization characteristics between living body systems " when two persons take sleep in the same space, The average heart rate in seconds is shown in blue and red, respectively. The x-axis represents time (30 seconds, eg 1:30 seconds, 2:60 seconds, 3:90 seconds, ...). FIG. 2B is a graph showing the interval in which the heartbeats of two persons are synchronized through the " analysis of synchronization characteristics between the living body systems " when the two persons take sleep in separate spaces, and FIG. The average heart rate in seconds is shown in blue and red, respectively. Comparing FIGS. 2A and 2B, it can be seen that "synchronization" occurs more often (black bars are more) when two people take sleep in the same space. By dividing the number of black bars, which is the synchronization interval, into the total sleep interval in the " analysis of synchronization characteristics between living body systems ", it is possible to calculate the rate at which synchronization occurs during sleep.

FIG. 2C is a graph obtained by smoothing the average heart rate of the two persons shown in FIG. 2A and a graph obtained by cross-correlation analysis of the average heart rate of the two smoothed persons, FIG. 2D is a graph showing the average heart rate of the two persons shown in FIG. A smoothed graph, and a graph obtained by cross-correlation analysis of the average heart rate of the smoothed two persons. By cross-correlation analysis, the "time delay" as the x-axis value and the "correlation coefficient value" as the y-axis value can be obtained. In Figures 2c and 2d, the smoothed mean heart rate of the two persons has a correlation of 0.5063 at a position having a " 4 " delay (4 * 30 = 120 seconds). When comparing FIG. 2C and FIG. 2D, it can be seen that the correlation between the average heart rate is high when two people in FIG. 2C take sleep in the same space, which means that they are influenced by each other.

Example 4. The living body Between systems Impact characterization

1) Directionality analysis technology

Directional analysis is a method of analyzing the coupling characteristics of two systems. Unlike the synchronous analysis method, the coupling strength and direction of two systems can be known. The method used in this study is an EMA (Evolution map approach) The contribution of the A signal to the change in the degree of contribution of the B signal and the phase of the B signal in the change is evaluated. The directionality was calculated from the bio-signal phase calculated in accordance with equation (10).

[Equation 10]

The directionality between bio - signals was calculated in 30 - second interval, which is the criterion for evaluation of the water level, and the influence between the living systems was analyzed.

2) Autonomic nervous system effect analysis

The activity of the autonomic nervous system changes according to the level of the sleep. As the sleep becomes deeper and stabilized, the action of the parasympathetic boundary becomes more active. The more sympathetic nervous system becomes closer to sleep or REM sleep, the more active the sympathetic nervous system becomes. The heart rate interval was calculated from the extracted heart rate position and the heart rate rate index was calculated from the calculated heart rate interval. At this time, heart rate variability was calculated by time domain, frequency domain, and nonlinear method.

3) Analysis of central nervous system effect

In general, it is known that the activity of low-band EEG becomes active as the sleep becomes deeper. The coherence index was calculated from Eq. 11 from the two extracted EEG waves.

[Equation 11]

The maximum value is extracted from Γ ₁ _{, 2} (ω), and Γ ^max .

4) Calculation of influence characteristic index between biological systems

The phase synchronization interval between the measured bio - signals (heart - heart rate, respiration - respiration, band EEG - band EEG) during sleep was detected and divided by the total sleep interval to quantify the interactions between the living systems during sleep.

We calculated the directionality between the bio - signals measured during the sleep and analyzed the degree of binding, to determine who is influencing who, and the strength of the influence.

Evaluate positive and negative effects by analyzing the heart rate variability and coherence index at the relevant sleep stages that are known to be influential. The index E _k, which calculates the affirmative or negative effect through the ratio of the directional index of the previous sleep phase to the current sleep phase, that is, the ratio of the difference between the previous sleep phase and the current sleep phase heart rate variance and the coherence index And I _k were calculated using Equation 12. Both E _k and I _k are indicators for analyzing the effect of the biomedical system. Negative effects are shown when "-" occurs, and positive effects when "+" is displayed.

[Equation 12]

k: the k th sleep stage

E _k And I _k were calculated to calculate the mean value for the sleep and the influence on the total sleep was also evaluated.

Example 5. Sleep Interaction Analysis

The number and duration of sleep changes of the other person in the state where the sleep phase of one of the two persons has changed is calculated. For the whole sleep phase, the number of times and the duration were converted into the ratio, which was used as an indicator of the sleep structure effect.

Example 6. Motion-based sleep-arousal impact analysis

The number of awakenings caused by one person's awakening was counted, and the number of wake-ups caused by others for the entire sleep stage was used as a measure of the sleep-arousal effect. Referring to FIG. 3, it can be confirmed that a person's arousal affects the awakening of another person (yellow circle). The upper blue graph of FIG. 3 shows the electrocardiogram of one of the two persons, and the lower vermillion graph shows the other person's electrocardiogram measured at the same time. The upper blue graph shows that the electrocardiogram that normally appears at 299.95 minutes is distorted, indicating that the person is "awake" and that the ECG signal is distorted due to movement, which is the "awake" interval during the sleep phase. Looking at the scarlet graph below, the other person wakes up or moves, then wakes up or moves. If the person is awake or moved, the controller judges that the other person is awake due to the influence of the person, and adds the number of such intervals to the total sleep time to convert the ratio.

Figure 4 is a graph illustrating the sleeping steps of two persons in accordance with one embodiment of the present invention. The upper blue graph is part of one person's sleep phase, and the lower vermilion graph shows some of the other person's sleep stages. In the initial part, the person's sleeping phase changes from LS (light sleep) to RS (REM sleep), and after a while, the other person's sleeping phase changes from LS to RS. It can be seen that the person's sleeping phase changes from LS to DS (Deep sleep) in the neighborhood of 350epoch, and then the other person's sleeping phase changes from LS to DS. In the sleep mutual influence analysis, it is possible to calculate the number of times and the duration of the sleep phase of the two persons changing to the same phase.

While the present invention has been described in connection with what is presently considered to be the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, .

Claims

In this study,
The method includes measuring respiration, electrocardiogram, and electrocardiogram signals, which are biological signals during sleep, from two persons taking sleep in the same space, and processing the signals to calculate respiration intervals, heart rate positions, heart rate intervals, Acquiring phase information and instantaneous phase information of the biometric information from the extracted information;
Calculating a synchronization index by applying a synchronization analysis technique to the phase of the same biometric information measured by the two persons, determining that the synchronization index is synchronized when the synchronization index value is equal to or greater than a predetermined value, detecting the synchronization interval, Analyzing synchronization characteristics between biological systems by calculating a ratio and quantifying the ratio;
The sleeping phase of the two persons is analyzed to analyze the cross correlation, and the heartbeat interval is extracted from the heartbeat positions of the two persons. Calculating the average heart rate in units of 30 seconds, calculating the average heart rate tendency by smoothing the average heart rate, analyzing the correlation by cross correlation analysis of the average heart rate tendency, and analyzing the correlation between 30 seconds The unit power is calculated and smoothed to analyze the cross correlation, and the average respiratory rate is calculated and smoothed in the interval of 30 seconds in the respiratory interval of the two persons, and the cross correlation is analyzed to calculate the delay time and the correlation coefficient value of the cross correlation A correlation analysis step between biological systems; And
The biometric information phase of one of the two persons is changed and the degree of contribution of another person's biometric information is evaluated by an EMA (Evolution Map Approach) method, and the directional calculation is performed in units of 30 seconds. Calculating the heart rate index from the interval, analyzing the autonomic nervous system effect, calculating the coherence index from the band EEG, analyzing the influence of the central nervous system, determining the strength of influence as the direction of the directional calculation, And analyzing the effect of the inter-biological system effect on the central nervous system effect analysis by calculating a positive or negative effect on the difference between the respective sleeping heart rate index and the coherence index according to the directional calculation difference; And
Wherein the number and duration of the sleeping steps of the other person are changed and the number and duration of the sleeping steps of the other person are converted into a ratio to the total sleeping state, An impact analysis step,
When the signal is measured in the biometric information acquiring step, the awake interval of one of the two persons is excluded as a distortion section,
Wherein the synchronization section sets between the bio-signals measured during sleep,
A method for analyzing sleep interactions based on bio - signals.

The method according to claim 1,
The bio-information acquisition step may include calculating a sleeping phase from the electroencephalogram signal, extracting a band EEG using a bandpass filter,
Extracting a heartbeat position and a heartbeat interval from the electrocardiogram signal,
Obtaining a respiration interval from the respiration signal, acquiring a band respiration signal using a bandpass filter,
Applying a Hilbert transform to the band EEG and the band respiration signal to obtain a phase,
Acquiring an instantaneous phase from the extracted heartbeat position,
A method for analyzing sleep interactions based on bio - signals.

The method according to claim 1,
In the step of analyzing synchronization characteristics between the living-body systems, the bio-information includes at least one of a heart rate interval, a respiratory interval, a delta wave of a band EEG, a seta wave, an alpha wave, a sigma wave,
A method for analyzing sleep interactions based on bio - signals.

The method according to claim 1,
The index for calculating the positive influence or the negative influence in the analysis of the influence characteristics between the biomedical systems can be calculated by calculating an average value for the entire sleep surface, evaluating the influence on the entire sleep surface, calculating an average value for the sleeping step, To evaluate,
A method for analyzing sleep interactions based on bio - signals.

The method according to claim 1,
The sleep mutual influence analyzing step may further include analyzing a sleep-wakefulness effect, which is a sleep-wakefulness effect index, by calculating the number of times the other person awakes due to the awake of one of the two persons,
A method for analyzing sleep interactions based on bio - signals.

3. The method of claim 2,
In order to calculate the sleeping phase, further measures of safety &
A method for analyzing sleep interactions based on bio - signals.