CN106333652B - A kind of sleep state analysis method - Google Patents
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Abstract
The invention discloses a kind of sleep state analysis methods, can identify REM sleep by body movement information combination vegetative nerve equilibrium analysis.To further more accurately evaluate the objective sleep quality of patient by awakening, stabilization, unstable sleep and REM sleep situation.
Description
Technical field
The present invention relates to sleep analysis fields, and in particular to a kind of sleep state analysis method.
Background technique
Sleep is the important physiological activity of human body, and the quality of sleep has the recovery of human body and spirit important
It influences.Meanwhile sleep state also has close contact with mental diseases such as such as depression.More and more researchers wish pair
Sleep state carries out deep analysis.
Conventional method usually obtains sleep state by stating for active, still, the sleep of many times human body active
Situation fluctuation is very big, and subjective assessment and actual sleep state condition be not identical.Currently, researcher uses more and more
Polysomnography (PSG) monitors the sleep state of user.Polysomnography is usually in full night sleep procedure, even
Continue and trace the indexs such as brain electricity, breathing synchronously to carry out sleep state analysis, this is needed in detected person's body, head and jaw
Face pastes a large amount of electrodes to meet demand signals, so that having a large amount of conducting wire around patient body, from objective and subjectivity side
Face can all sleep to user and constitute adverse effect, thus the objectivity of impact analysis structure.
It needs as a result, a kind of for the lesser analysis method of user's sleep state influence.
Summary of the invention
In view of this, the present invention provides a kind of sleep state analysis method, when carrying out sleep state monitoring or analysis
Reduce the influence for user's sleep quality, improves the accuracy of the dormant data of acquisition.
A kind of sleep state analysis method of the invention includes:
1, a kind of sleep state analysis method, comprising:
It obtains user's electrocardiosignal (ECG) and characterizes the body movement signal of at least one limb motion state of human body；
Heart rate variability temporal signatures and respiratory component signal are obtained according to user's electrocardiosignal, and are become in turn according to heart rate
Anisotropic temporal signatures and respiratory component signal and the body movement signal obtain characterization user's parasympathetic excitement degree
Excited coefficient；
Sleep state is obtained according to the excited coefficient.
Preferably, described that heart rate variability temporal signatures and respiratory component signal are obtained according to user's electrocardiosignal, it goes forward side by side
And characterization user's parasympathetic system is obtained according to heart rate variability temporal signatures and respiratory component signal and the body movement signal
System excitement degree excited coefficient include:
Corresponding R -- R interval signal and corresponding respiratory component signal are obtained according to user's electrocardiosignal；
According to corresponding heart rate variability (HRV) the instantaneous frequency average value of the R -- R interval signal acquisition；
According to the envelope signal of the respiratory component signal acquisition binaryzation；
Body, which is obtained, according to the body movement signal moves status signal；
State letter is moved according to the heart rate variability (HRV) instantaneous frequency average value, the envelope signal of binaryzation and body
Number excited coefficient is calculated, the excitement coefficient is used to characterize the parasympathetic excitement journey of user.
Preferably, corresponding R -- R interval signal is obtained according to user's electrocardiosignal and corresponding respiratory component signal includes:
Wavelet decomposition is carried out to the electrocardiosignal for Decomposition order with 8 layers, 1-4 layers of wavelet conversion coefficients are carried out soft
Threshold filter obtains the wavelet conversion coefficient after threshold process, while keeping 5-7 layer of wavelet conversion coefficient constant, and removal the
8 layers of wavelet conversion coefficient, based on the wavelet conversion coefficient and 5-7 layers of wavelet conversion coefficient progress small echo weight after threshold process
Structure obtains filtered electrocardiosignal；
QRS complex detection is carried out to the filtered electrocardiosignal and obtains QRS complex；
R -- R interval signal is obtained according to the QRS complex；
Interpolation is carried out to Detection Point using cubic spline interpolation according to QRS complex and passes through down-sampling and smoothing processing, root
According to the respiratory sinus arrhythmia feature in electrocardiosignal, the respiratory component signal is extracted.
Preferably, the wavelet conversion coefficient to 1-4 layers carries out soft-threshold de-noising and obtains the small echo change after threshold process
Changing coefficient includes being filtered according to the following formula:
Wherein, sgn is sign function, ECGJ, kFor the wavelet conversion coefficient on electrocardiosignal different scale,For
Estimation wavelet conversion coefficient after threshold process, j are wavelet decomposition scales, and k is k-th of data point on corresponding scale, t be to
Fixed threshold value, β are the positive real number greater than 1.
Preferably, the progress QRS complex, which detects, includes:
Hilbert (Hilbert) transformation is carried out to filtered electrocardiosignal, with original signal and transformed signal group
At complex analytic signal, wherein the real part of the complex analytic signal is original signal, and imaginary part is transformed signal
The difference value for calculating the point of the complex analytic function, according to the difference value will meet the point of predetermined condition as
QRS complex Detection Point, the point for meeting predetermined condition is in the difference that the difference value of top n point is more than predetermined threshold and current point
Value is less than the difference value of former point.
Preferably, described according to corresponding heart rate variability (HRV) the instantaneous frequency average value of the R -- R interval signal acquisition
Include:
Hilbert-Huang (Hilbert-Huang) transformation is carried out to the R -- R interval signal and obtains time-frequency conversion signal；
The average value of the time-frequency conversion signal is calculated as heart rate variability (HRV) using the sliding window of predetermined length
Instantaneous frequency average value, the instantaneous frequency average value are used to characterize the excitement degree of the parasympathetic of user.
Preferably, the envelope signal according to the respiratory component signal acquisition binaryzation includes:
Wavelet decomposition is carried out to the respiratory component signal for Decomposition order with 8 layers；
The respiratory component sequence that wavelet reconstruction is reconstructed is carried out to 1-3 layers of wavelet conversion coefficients；
Obtain the local maximum and local minimum of the respiratory component sequence；
Coenvelope is obtained into interpolation between adjacent local maximum, interpolation is carried out between adjacent local minimum
Obtain lower envelope；
Average envelope is obtained according to the coenvelope and the lower envelope；
The envelope signal of progress binary conversion treatment acquisition binaryzation after first derivative, the binaryzation are sought to average envelope
Envelope signal for indicating sympathetic/parasympathetic excitement dominant characteristics.
Preferably, obtaining the dynamic status signal of body according to the body movement signal includes:
The body movement signal of binaryzation is obtained according to the body movement signal；
The body movement signal of the binaryzation is divided using the sliding window of predetermined time length, obtains each time
The body of section moves status signal.
Preferably, excited coefficient is calculated according to the following formula:
Wherein, m ' (t) is the envelope signal of the binaryzation,Status signal is moved for the body.
Preferably, the electrocardiosignal is chest leads electrocardiosignal.
The present invention obtains heart rate variability by obtaining user's electrocardiosignal and body movement signal, based on the analysis of heart telecommunication signal
Property temporal signatures and respiratory component signal, and combination moves signal acquisition and characterizes user's parasympathetic excitement degree in turn
Excited coefficient, carry out sleep state analysis, do not need as a result, user Head And Face be arranged electrode, carry out sleep state
Reduce the influence for user's sleep quality when monitoring or analysis, improves the accuracy of the dormant data of acquisition.
Detailed description of the invention
Fig. 1 is the flow chart of the sleep state analysis method of the embodiment of the present invention；
Fig. 2 is the data flow figure of the sleep state analysis method of the embodiment of the present invention.
Specific embodiment
Below based on embodiment, present invention is described, but the present invention is not restricted to these embodiments.Under
Text is detailed to describe some specific detail sections in datail description of the invention.Do not have for a person skilled in the art
The present invention can also be understood completely in the description of these detail sections.In order to avoid obscuring essence of the invention, well known method, mistake
There is no narrations in detail for journey, process, element and circuit.
In addition, it should be understood by one skilled in the art that provided herein attached drawing be provided to explanation purpose, and
What attached drawing was not necessarily drawn to scale.
Under normal circumstances, the opposite sympathetic and parasympathetic nerve of function is in mutually balance restricts human body.This two
In a nervous system, when a side plays positive interaction, another party then plays negative interaction, the physiology of good balance coordination and control body
Movable and each organ, including heart, lung etc., this is neuro vegetative function.If the balance quilt of automatic nervous system
Break, then various dysfunctions will occur.At present due to being not possible to directly measure parasympathetic
Power measures other physiology courses usually to reflect its function indirectly, such as passes through heart rate variability analysis (Heart Rate
Variability, HRV), if there is sleep disturbance in sleep procedure, vegetative nerve activity balancing will be broken, therefore analyze
The temporal signatures of sleep HRV can reflect out stabilization and unstable state in sleep, to further obtain sleep quality.So
And due to anisotropic difference and the unstability of HRV, including vulnerable to drug influence, the present invention will combine the breathing in electrocardiosignal
Ingredient is provided from vegetative nerve to the adjusting angle of lung functions for sympathetic, parasympathetic movable accurate measurement
Additional auxiliary information.
On the other hand, the sleep specificity of certain customers is mainly manifested in REM phase (rapid eye movement, Rapid Eyes
Movement) in sleep, current sleep medicine research thinks that the main function of REM phase is to aid in people and carries out on spirit level
Loosening and restore, but compared with normal control group, there is REM sleep incubation period in certain customers and the time shortens, but its density,
Intensity and quantity increase, and first REM sleep time extends.It is said from physiological theory angle, in the plant mind in sleep REM stage
It is similar to microarousal or phase sleep through equilibrium state, the difference is that human muscle is in the state loosened the most when the REM stage,
It can be understood as almost without any limb motion, therefore can be known by body movement information combination vegetative nerve equilibrium analysis
It Chu not REM sleep.To further more accurately evaluate trouble by awakening, stabilization, unstable sleep and REM sleep situation
The objective sleep quality of person.
Therefore, it may be considered that heart rate variability temporal signatures and corresponding respiratory component signal are obtained based on electrocardiosignal,
And combination moves signal to obtain the excitement degree of the parasympathetic of user in turn, and the sleep of user is analyzed based on this
State.
Fig. 1 is the flow chart of the sleep state analysis method of the embodiment of the present invention；Fig. 2 is the sleep shape of the embodiment of the present invention
The data flow figure of state analysis method.As depicted in figs. 1 and 2, the sleep state analysis method of the embodiment of the present invention includes:
Step S100, the body for obtaining user's electrocardiosignal (ECG) and characterization at least one limb motion state of human body is dynamic
Signal.
Wherein, since the respiratory state of chest leads electrocardiosignal and human body has better relevance, it is excellent at one
In the embodiment of choosing, the chest leads electrocardiosignal of user is acquired to carry out sleep state analysis.
Body movement signal can be acquired by the equipment with acceleration transducer connecting with user's limbs, the body of acquisition
Dynamic signal may include the acceleration of such as three axial directions of x, y, z.
Step S200, heart rate variability temporal signatures and respiratory component signal are obtained according to user's electrocardiosignal, and in turn
Characterization user's parasympathetic is obtained according to heart rate variability temporal signatures and respiratory component signal and the body movement signal
The excited coefficient of excitement degree.
Specifically, step S200 includes:
Step S210, corresponding R -- R interval signal and corresponding respiratory component signal are obtained according to user's electrocardiosignal.
Preferably, step 210 further comprises:
Step S211, electrocardiosignal is pre-processed, so that electrocardiosignal is adapted for QRS complex detection.
Specifically, wavelet decomposition is carried out to the electrocardiosignal for Decomposition order with 8 layers, to 1-4 layers of wavelet transformation system
Number carries out soft-threshold de-noising and obtains the wavelet conversion coefficient after threshold process, while keeping 5-7 layers of wavelet conversion coefficient constant,
And the wavelet conversion coefficient of the 8th layer of removal, the wavelet conversion coefficient based on wavelet conversion coefficient and 5-7 after threshold process carry out
Wavelet reconstruction obtains filtered electrocardiosignal.
Wherein, the 8th layer of wavelet conversion coefficient is interfered as baseline drift removes.
Preferably, coif4 wavelet decomposition can be used.
Preferably, it is filtered according to the following formula:
Wherein, sgn is sign function, ECGJ, kFor the wavelet conversion coefficient on electrocardiosignal different scale,For
Estimation wavelet conversion coefficient after threshold process, j are wavelet decomposition scales, and k is k-th of data point on corresponding scale, t be to
Fixed threshold value, β are the positive real number greater than 1.Wherein it is possible to selected threshold function parameter β=98.
Step S212, QRS complex detection is carried out to the filtered electrocardiosignal and obtains QRS complex.
Specifically, in step S212, firstly, to filtered electrocardiosignal, namely the signal that reconstruct obtains is wished
You convert Bert (Hilbert), with original signal and transformed signal composition complex analytic signal y (k).Wherein, real part is filtering
Electrocardiosignal afterwards, imaginary part are the signal carried out after Hilbert transform.The amplitude of the complex analytic signal of acquisition is filtered
The envelope of electrocardiosignal.What the R summit in electrocardiosignal appeared in envelope rising edge as a result, turns position.Then, it calculates described multiple
The difference value of the point of analytical function will meet the point of predetermined condition as QRS complex Detection Point according to the difference value, described full
The point of sufficient predetermined condition is more than that the difference value of predetermined threshold and current point is less than the difference of former point in the difference value of top n point
Value.Specifically, according to by the difference value of envelope signal, such as first-order difference value y (n)-y (n-1), it can be determined that the rising of envelope
Amplitude is considered as the rising edge for being currently at envelope, in rising edge, if current point when ascensional range is greater than predetermined threshold
Difference value be less than former point difference value, then it is believed that finding the inflection point of rising edge, namely have found the peak R, by it is secondary be used as QRS
The Detection Point of wave, and the peak Q and the peak S can be detected based on this.
Step S213, R -- R interval signal is obtained according to the QRS complex.
Step S214, interpolation is carried out to Detection Point using cubic spline interpolation according to QRS complex and passes through down-sampling peace
Sliding processing, according to the respiratory sinus arrhythmia feature in electrocardiosignal, extracts the respiratory component signal.
Step S220, according to corresponding heart rate variability (HRV) the instantaneous frequency average value of the R -- R interval signal acquisition.
Wherein, step S220 includes:
Step S221, Hilbert-Huang (Hilbert-Huang) transformation is carried out to the R -- R interval signal and obtains time-frequency
Convert signal.
The time-frequency conversion signal for carrying out Hilbert-Huang transform acquisition is the frequency-time letter for characterizing R -- R interval signal
Number, that is, its instantaneous frequency and the relationship of time for characterizing R -- R interval signal.
Step S222, become using the average value that the sliding window of predetermined length calculates the time-frequency conversion signal as heart rate
Anisotropic (HRV) instantaneous frequency average value, the instantaneous frequency average value are used to characterize the excitement of the parasympathetic of user
Degree.
It is observed that the average value of HRV instantaneous frequency can be divided into three regions, including high frequency region (0.15~
0.4Hz), low frequency range (0.04~0.15Hz) and ultralow frequency area (0.003~0.04Hz).Respectively indicated out from high frequency to low frequency by
The excitement degree of the parasympathetic of Rate control reflection gradually decreases.
Step S230, according to the envelope signal of the respiratory component signal acquisition binaryzation.
Specifically, step S230 includes:
Step S231, wavelet decomposition is carried out to the respiratory component signal for Decomposition order with 8 layers.
Step S232, the respiratory component sequence s that 1-3 layers of wavelet conversion coefficient progress wavelet reconstruction is reconstructed
(t)。
Step S233, the local maximum and local minimum of the respiratory component sequence are obtained.
Step S234, coenvelope U (x) is obtained into interpolation between adjacent local maximum, in adjacent local minimum
Interpolation is carried out between value obtains lower envelope L (x).Specifically, cubic spline interpolation can be carried out, every 30 seconds spacing are inserted into one
Point.The coenvelope line and lower envelope line cover entire signal.
Step S235, average envelope is obtained according to the coenvelope and the lower envelope.
It can specifically be calculated by following formula:
M (t)=[U (t)+L (t)]/2
Step S236, the envelope letter of progress binary conversion treatment acquisition binaryzation after first derivative is sought to average envelope m (t)
Number m ' (t), that is,The envelope signal of the binaryzation is for indicating sympathetic/parasympathetic
Application is through system stimulant dominant characteristics.That is, indicating excited by the parasympathetic of lung control reflection when m ' (t) is 1
Leading position in balance is accounted for, when m ' (t) is -1, indicates to be accounted in balance by the stomodaeal nervous system excitement of lung control reflection and dominate
Status.
Step S240, body is obtained according to the body movement signal and moves status signal.
For body movement signal, the acceleration value along three directions of x, y, z is typically appeared as.It is used according to characterization is obtained accordingly
The parameter of family loosening all muscles degree.It in some cases, can be abnormal in removal since the sample frequency of acceleration transducer is excessively high
After point, sub-sampling is carried out according to such as 1Hz to the acceleration information of acceleration transducer output, suited the requirements with obtaining frequency,
It is adapted for the body movement signal being further processed.
Specifically, step S240 includes:
Step S241, the body movement signal of binaryzation is obtained according to the body movement signal.
Preferably, the overall acceleration amplitude of body movement signal can be calculated according to the acceleration value of different directions, that is,
It calculatesWherein, Act is overall acceleration amplitude, Actx、Acty、ActzRespectively
The body movement signal acceleration value in x, y, z direction.Then binary conversion treatment is carried out to Act, then recognized that is, Act is greater than predetermined threshold
It is set to 1, otherwise regards as 0.In a preferred embodiment, predetermined threshold is chosen to be 0.04.That is, having
Act ' is the body movement signal after binaryzation.
Step S242, the body movement signal of the binaryzation is divided using the sliding window of predetermined time length, is obtained
The body of each period is taken to move status signal.
Specifically, current time front and back can be sampled each 15 seconds, amounts to the dynamic letter of body of the binaryzation of 30 seconds sliding windows
Number.The number of numerical value 0 in window is more than predetermined value, for example, the moment corresponding body being moved status signal and is set as at 24
0, that is, body moves status signalOtherwiseBody, which moves expression muscle when status signal is 0 and almost loses, to be opened
Power is in relaxation state.
Step S250, according to the heart rate variability (HRV) instantaneous frequency average value, the envelope signal and body of binaryzation
Dynamic status signal calculates excited coefficient.
Specifically, the excited coefficient can be calculated according to the following formula:
Wherein, m ' (t) is the envelope signal of the binaryzation,Status signal is moved for the body.
The method also includes step S300, obtain sleep state according to the excited coefficient.
Specifically, the value range of excited coefficient can be -0.997~1.4, when excited coefficient is in range 1.15~1.4
It may be considered stable sleep when interior；It may be considered unstable sleep when excited coefficient is in range -0.96~-0.75；
It may be considered awakening/microarousal state when excited coefficient is in range -0.997~-0.96；It can when excited coefficient is 0
To be considered the REM sleep stage.
The present invention obtains the heart based on the analysis of heart telecommunication signal by obtaining user's electrocardiosignal and body movement signal as a result,
Rate variability temporal signatures and respiratory component signal, and the dynamic signal acquisition characterization user's parasympathetic of combination is emerging in turn
The excited coefficient for degree of putting forth energy carries out sleep state analysis, does not need that electrode is arranged in the Head And Face of user as a result, is being slept
Reduce the influence for user's sleep quality when dormancy condition monitoring or analysis, improves the accuracy of the dormant data of acquisition.
Sleep analysis method of the invention can be applied to be monitored the sleep state of healthy human body, can also apply
In being assessed for such as depression etc. by the disease of symptom of sleep disturbance.
The above description is only a preferred embodiment of the present invention, is not intended to restrict the invention, for those skilled in the art
For, the invention can have various changes and changes.All any modifications made within the spirit and principles of the present invention are equal
Replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of sleep state analysis method, comprising:
It obtains user's electrocardiosignal (ECG) and characterizes the body movement signal of at least one limb motion state of human body, the body is dynamic
Signal is acceleration signal；
Heart rate variability temporal signatures and respiratory component signal are obtained according to user's electrocardiosignal, and in turn according to heart rate variability
Temporal signatures and respiratory component signal and the body movement signal obtain the excitement of characterization user's parasympathetic excitement degree
Coefficient；
Sleep state is obtained according to the excited coefficient；
Wherein, described that heart rate variability temporal signatures and respiratory component signal, and basis in turn are obtained according to user's electrocardiosignal
It is excited that heart rate variability temporal signatures and respiratory component signal and the body movement signal obtain characterization user's parasympathetic
The excited coefficient of degree includes:
Corresponding R -- R interval signal and corresponding respiratory component signal are obtained according to user's electrocardiosignal；
According to corresponding heart rate variability (HRV) the instantaneous frequency average value of the R -- R interval signal acquisition；
According to the envelope signal of the respiratory component signal acquisition binaryzation；
Body, which is obtained, according to the body movement signal moves status signal；
Status signal meter is moved according to the heart rate variability (HRV) instantaneous frequency average value, the envelope signal of binaryzation and body
Excited coefficient is calculated, the excitement coefficient is used to characterize the parasympathetic excitement degree of user；
It is described to include: according to user's electrocardiosignal corresponding R -- R interval signal of acquisition and corresponding respiratory component signal
Wavelet decomposition is carried out to the electrocardiosignal for Decomposition order with 8 layers, soft-threshold is carried out to 1-4 layers of wavelet conversion coefficients
Filtering obtains the wavelet conversion coefficient after threshold process, while keeping 5-7 layers of wavelet conversion coefficient constant, and removes the 8th layer
Wavelet conversion coefficient, based on after threshold process wavelet conversion coefficient and 5-7 layers wavelet conversion coefficient carry out wavelet reconstruction
Obtain filtered electrocardiosignal；
QRS complex detection is carried out to the filtered electrocardiosignal and obtains QRS complex；
R -- R interval signal is obtained according to the QRS complex；
Interpolation is carried out to Detection Point using cubic spline interpolation according to QRS complex and passes through down-sampling and smoothing processing, according to the heart
Respiratory sinus arrhythmia feature in electric signal extracts the respiratory component signal.
2. sleep state analysis method according to claim 1, which is characterized in that the wavelet transformation system to 1-4 layers
Wavelet conversion coefficient after number progress soft-threshold de-noising acquisition threshold process includes being filtered according to the following formula:
Wherein, sgn is sign function, ECGj,kFor the wavelet conversion coefficient on electrocardiosignal different scale,At threshold value
Estimation wavelet conversion coefficient after reason, j are wavelet decomposition scales, and k is k-th of data point on corresponding scale, and t is given threshold
Value, β are the positive real number greater than 1.
3. sleep state analysis method according to claim 1, which is characterized in that the progress QRS complex, which detects, includes:
Hilbert (Hilbert) transformation is carried out to filtered electrocardiosignal, is formed with original signal and transformed signal multiple
Analytic signal, wherein the real part of the complex analytic signal is original signal, and imaginary part is transformed signal
The difference value for calculating the point of the complex analytic function will meet the point of predetermined condition as QRS wave according to the difference value
Group's Detection Point, the point for meeting predetermined condition are small more than the difference value of predetermined threshold and current point in the difference value of top n point
In the difference value of former point.
4. sleep state analysis method according to claim 1, which is characterized in that described according to the R -- R interval signal
Obtaining corresponding heart rate variability (HRV) instantaneous frequency average value includes:
Hilbert-Huang (Hilbert-Huang) transformation is carried out to the R -- R interval signal and obtains time-frequency conversion signal；
The average value for calculating the time-frequency conversion signal using the sliding window of predetermined length is instantaneous as heart rate variability (HRV)
Average frequency value, the instantaneous frequency average value are used to characterize the excitement degree of the parasympathetic of user.
5. sleep state analysis method according to claim 1, which is characterized in that described according to the respiratory component signal
Obtain binaryzation envelope signal include:
Wavelet decomposition is carried out to the respiratory component signal for Decomposition order with 8 layers；
The respiratory component sequence that wavelet reconstruction is reconstructed is carried out to 1-3 layers of wavelet conversion coefficients；
Obtain the local maximum and local minimum of the respiratory component sequence；
Coenvelope is obtained into interpolation between adjacent local maximum, interpolation acquisition is carried out between adjacent local minimum
Lower envelope；
Average envelope is obtained according to the coenvelope and the lower envelope；
The envelope signal of progress binary conversion treatment acquisition binaryzation after first derivative, the packet of the binaryzation are sought to average envelope
Network signal is for indicating sympathetic/parasympathetic excitement dominant characteristics.
6. sleep state analysis method according to claim 1, which is characterized in that it is dynamic to obtain body according to the body movement signal
Status signal includes:
The body movement signal of binaryzation is obtained according to the body movement signal；
The body movement signal of the binaryzation is divided using the sliding window of predetermined time length, obtains each period
Body moves status signal.
7. sleep state analysis method according to claim 4, which is characterized in that calculate excited system according to the following formula
Number:
Wherein, m ' (t) is the envelope signal of the binaryzation,Status signal is moved for the body.
8. sleep state analysis method according to claim 1, which is characterized in that the electrocardiosignal is chest leads electrocardio
Signal.
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| CN108201435A (en) * | 2017-12-06 | 2018-06-26 | 深圳和而泰数据资源与云技术有限公司 | Sleep stage determines method, relevant device and computer-readable medium |
| CN108209874B (en) * | 2018-01-03 | 2021-01-26 | 深圳北航新兴产业技术研究院 | Automatic sleep staging method and device |
| CN108310587B (en) * | 2018-02-02 | 2021-03-16 | 贺鹏程 | Sleep control device and method |
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| CN109222961B (en) * | 2018-11-09 | 2024-01-19 | 中科数字健康科学研究院（南京）有限公司 | Portable sleep monitoring system and related sleep monitoring method |
| CN112006673A (en) * | 2020-08-26 | 2020-12-01 | 西安电子科技大学 | Human body heart rate detection method and system, storage medium, computer equipment and terminal |
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