CN106333652A - Sleep state analysis method - Google Patents

Sleep state analysis method Download PDF

Info

Publication number
CN106333652A
CN106333652A CN201610908281.XA CN201610908281A CN106333652A CN 106333652 A CN106333652 A CN 106333652A CN 201610908281 A CN201610908281 A CN 201610908281A CN 106333652 A CN106333652 A CN 106333652A
Authority
CN
China
Prior art keywords
signal
electrocardiosignal
sleep state
wavelet
analysis method
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201610908281.XA
Other languages
Chinese (zh)
Other versions
CN106333652B (en
Inventor
刘冬冬
刘畅
赵相坤
陈卉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Capital Medical University
Original Assignee
Capital Medical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Capital Medical University filed Critical Capital Medical University
Priority to CN201610908281.XA priority Critical patent/CN106333652B/en
Publication of CN106333652A publication Critical patent/CN106333652A/en
Application granted granted Critical
Publication of CN106333652B publication Critical patent/CN106333652B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405Determining heart rate variability
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/08Detecting, measuring or recording devices for evaluating the respiratory organs
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118Determining activity level
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4815Sleep quality
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7253Details of waveform analysis characterised by using transforms

Abstract

The invention discloses a sleep state analysis method. REM (Rapid Eyes Movement) sleep can be recognized via body movement information in combination with vegetative nerve balance analysis. Thus, the objective sleep quality of a patient is further evaluated more accurately via awakening, stable or instable sleep and REM sleep conditions.

Description

A kind of sleep state analysis method
Technical field
The present invention relates to sleep analysis field is and in particular to a kind of sleep state analysis method.
Background technology
Sleep is the important physiological activity of human body, the quality of sleep for human body and spirit recovery have important Impact.Meanwhile, sleep state also has close contacting with mental sickness such as such as depressions.It is right that increasing researcher is wished Sleep state carries out deep analysis.
Traditional method generally to obtain sleep state by stating of active, but, the sleep of many times human body active Situation fluctuation is very big, and subjective assessment with actual sleep state condition and differs.At present, researcher is increasingly employed Polysomnography (psg) is monitoring the sleep state of user.Polysomnography, generally in full night sleep procedure, connects Continue and synchronously trace the indexs such as brain electricity, breathing to carry out sleep state analysis, this needs in detected person's body, head and jaw Face pastes a large amount of electrodes to meet demand signals so that there being substantial amounts of wire around patient body, from objective and subjectivity side All can sleep to user and constitute adverse effect in face, thus the objectivity of impact analysis structure.
Thus, needing one kind badly affects less analysis method for user's sleep state.
Content of the invention
In view of this, the present invention provides a kind of sleep state analysis method, with when carrying out sleep state monitoring or analysis Reduce the impact for user's sleep quality, improve the accuracy of the dormant data obtaining.
A kind of sleep state analysis method of the present invention includes:
1st, a kind of sleep state analysis method, comprising:
The body movement signal obtaining user's electrocardiosignal (ecg) and characterizing 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 and then is become according to heart rate Different in nature temporal signatures and respiratory component signal and described body movement signal obtain and characterize user's parasympathetic nervous system excitement degree Excited coefficient;
Sleep state is obtained according to described excitement coefficient.
Preferably, described heart rate variability temporal signatures and respiratory component signal are obtained according to user's electrocardiosignal, go forward side by side And obtained according to heart rate variability temporal signatures and respiratory component signal and described body movement signal and characterize user's parasympathetic system The excited coefficient of system excitement degree includes:
Phase signal and corresponding respiratory component signal between corresponding r-r is obtained according to user's electrocardiosignal;
According to the corresponding heart rate variability of phase signal acquisition (hrv) instantaneous frequency meansigma methodss between described r-r;
Envelope signal according to described respiratory component signal acquisition binaryzation;
Body is obtained according to described body movement signal and moves status signal;
State letter is moved according to described heart rate variability (hrv) instantaneous frequency meansigma methodss, the envelope signal of binaryzation and body Number calculate excited coefficient, described excitement coefficient is used for characterizing the parasympathetic nervous system excitement journey of user.
Preferably, obtain phase signal and corresponding respiratory component signal between corresponding r-r according to user's electrocardiosignal to include:
With 8 layers for Decomposition order, wavelet decomposition is carried out to described electrocardiosignal, the wavelet conversion coefficient of 1-4 layer is carried out soft Threshold filter obtains the wavelet conversion coefficient after threshold process, keeps the wavelet conversion coefficient of 5-7 layer constant simultaneously, and removes the 8 layers of wavelet conversion coefficient, the wavelet conversion coefficient based on the wavelet conversion coefficient after threshold process and 5-7 layer carries out small echo weight Structure obtains filtered electrocardiosignal;
Described filtered electrocardiosignal is carried out with the detection of qrs wave group and obtains qrs wave group;
Phase signal between r-r is obtained according to described qrs wave group;
Row interpolation is entered and through down-sampling and smoothing processing, root using cubic spline interpolation according to qrs wave group to Detection Point According to the respiratory sinus arrhythmia feature in electrocardiosignal, extract described respiratory component signal.
Preferably, the described wavelet conversion coefficient to 1-4 layer carries out the small echo change after soft-threshold de-noising obtains threshold process Change coefficient to include being filtered according to equation below:
ecg j , k &overbar; = sgn ( e c g j , k ) - &beta; ( t - | ecg j , k | ) * t , | e c g j , k | &greaterequal; t 0 , | e c g j , k | < t
Wherein, sgn is sign function, ecgJ, kFor the wavelet conversion coefficient on electrocardiosignal different scale,For Estimation wavelet conversion coefficient after threshold process, j is wavelet decomposition scales, and k is k-th data point on corresponding yardstick, t be to Fixed threshold value, β is the arithmetic number more than 1.
Preferably, described carry out qrs wave group detection include:
Filtered electrocardiosignal is carried out with Hilbert (hilbert) conversion, with the signal group after original signal and conversion Become complex analytic signal, wherein, the real part of described complex analytic signal is original signal, and imaginary part is the signal after conversion
Calculate the difference value of the point of described complex analytic function, according to described difference value using the point meeting predetermined condition as Qrs wave group Detection Point, the described point meeting predetermined condition exceedes the difference of predetermined threshold and current point in the difference value of front n point Value is less than the difference value of former point.
Preferably, described according to the corresponding heart rate variability of phase signal acquisition (hrv) instantaneous frequency meansigma methodss between described r-r Including:
Between described r-r, phase signal carries out Hilbert-Huang (hilbert-huang) conversion acquisition time-frequency conversion signal;
Sliding window by the use of predetermined length calculates the meansigma methodss of described time-frequency conversion signal as heart rate variability (hrv) Instantaneous frequency meansigma methodss, described instantaneous frequency meansigma methodss are used for characterizing the excitement degree of the parasympathetic nervous system of user.
Preferably, the described envelope signal according to described respiratory component signal acquisition binaryzation includes:
With 8 layers for Decomposition order, wavelet decomposition is carried out to described respiratory component signal;
The respiratory component sequence that wavelet reconstruction obtains reconstructing is carried out to the wavelet conversion coefficient of 1-3 layer;
Obtain local maximum and the local minimum of described respiratory component sequence;
Enter interpolation and obtain coenvelope between adjacent local maximum, between adjacent local minimum, enter row interpolation Obtain lower envelope;
Average envelope is obtained according to described coenvelope and described lower envelope;
Average envelope is asked for after first derivative, carry out the envelope signal that binary conversion treatment obtains binaryzation, described binaryzation Envelope signal be used for representing sympathetic/parasympathetic nervous system excitement dominant characteristics.
Preferably, obtain the dynamic status signal of body according to described body movement signal to include:
Obtain the body movement signal of binaryzation according to described body movement signal;
Sliding window using scheduled time length divides to the body movement signal of described binaryzation, obtains each time The body of section moves status signal.
Preferably, according to the excited coefficient of equation below calculating:
Wherein, m ' (t) is the envelope signal of described binaryzation,Move status signal for described body.
Preferably, described electrocardiosignal is chest lead electrocardiosignal.
The present invention passes through to obtain user's electrocardiosignal and body movement signal, obtains heart rate variability based on electrocardio letter signal analysis Property temporal signatures and respiratory component signal, and and then coalition move signal acquisition and characterize user's parasympathetic nervous system excitement degree Excited coefficient, carry out sleep state analysis, thus it is not necessary to user Head And Face arrange electrode, carrying out sleep state Reduce the impact for user's sleep quality when monitoring or analysis, improve the accuracy of the dormant data obtaining.
Brief description
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 Literary composition in the detailed description of the present invention, detailed describes some specific detail sections.Do not have for a person skilled in the art The description of these detail sections can also understand the present invention completely.In order to avoid obscuring the essence of the present invention, known method, mistake Journey, flow process, element and circuit do not describe in detail.
Additionally, it should be understood by one skilled in the art that provided herein accompanying drawing be provided to descriptive purpose, and Accompanying drawing is not necessarily drawn to scale.
Under normal circumstances, the contrary sympathetic and parasympathetic nervouss of function are in mutual balance restriction human body.This two In individual nervous system, when a side plays positive interaction, the opposing party then plays negative interaction, good balance coordination and the physiology controlling body Activity and each organ, including heart, lung etc., this is neuro vegetative function.If neuro balance quilt Break, then various dysfunctions will occur.Open due to there is no method direct measurement to go out parasympathetic nervous system at present Power, typically measures other physiological process indirectly to reflect its function, for example, pass through heart rate variability analysis (heart rate Variability, hrv), if there is sleep disorder in sleep procedure, vegetative nerve activity balancing will be broken, therefore analyze The temporal signatures of sleep hrv can reflect the stable and labile state in sleep, thus obtaining sleep quality further.So And opposite sex difference and the unstability due to hrv, including being easily subject to drug influence, the present invention is by with reference to the breathing in electrocardiosignal Composition, from the regulation angle to lung functions for the vegetative nerve, the accurate measurement for sympathetic, parasympathetic nervous system activity provides Extra 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) sleeping, current sleep medicine research thinks that the Main Function of rem phase is to aid in people and carries out on spirit level Loosen and recover, but compared with normal matched group, rem Sleep latency in certain customers and the time shortens, but its density, Intensity and quantity all increase, and first rem extends the length of one's sleep.Say from physiological theory angle, in the plant god in sleep rem stage Similar through poised state and microarousal or phase sleep, except for the difference that during rem stage, human muscle is in the state loosened the most, Can be understood as almost there is no any limb motion, therefore can be known with reference to vegetative nerve equilibrium analyses by body movement information Do not go out rem sleep.Thus further trouble is more accurately evaluated by awakening, stable, unstable sleep and rem sleep state The objective sleep quality of person.
Therefore, it can consider to obtain heart rate variability temporal signatures and corresponding respiratory component signal based on electrocardiosignal, And and then coalition move signal to obtain the excitement degree of the parasympathetic nervous system of user, analyze the sleep of user 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:
The body of step s100, acquisition user's electrocardiosignal (ecg) and sign at least one limb motion state of human body moves Signal.
Wherein, because the breathing state of chest lead electrocardiosignal and human body has more preferable relatedness, therefore, excellent at one In the embodiment of choosing, the chest lead electrocardiosignal of collection user is carrying out sleep state analysis.
Body movement signal can be by being connected the equipment collection with acceleration transducer, the body of its collection with user's limbs Dynamic signal can 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 and then Obtained according to heart rate variability temporal signatures and respiratory component signal and described body movement signal and characterize user's parasympathetic nervous system The excited coefficient of excitement degree.
Specifically, step s200 includes:
Step s210, according to user's electrocardiosignal obtain corresponding r-r between phase signal and corresponding respiratory component signal.
Preferably, step 210 further includes:
Step s211, pretreatment is carried out to electrocardiosignal, so that electrocardiosignal is adapted for the detection of qrs wave group.
Specifically, with 8 layers for Decomposition order, wavelet decomposition is carried out to described electrocardiosignal, the wavelet transformation system to 1-4 layer Number carries out the wavelet conversion coefficient after soft-threshold de-noising obtains threshold process, keeps the wavelet conversion coefficient of 5-7 layer constant simultaneously, And removing the 8th layer of wavelet conversion coefficient, the wavelet conversion coefficient based on the wavelet conversion coefficient after threshold process and 5-7 is carried out Wavelet reconstruction obtains filtered electrocardiosignal.
Wherein, the 8th layer of wavelet conversion coefficient is disturbed as baseline drift and is removed.
Preferably, coif4 wavelet decomposition can be adopted.
Preferably, it is filtered according to equation below:
ecg j , k &overbar; = sgn ( e c g j , k ) - &beta; ( t - | ecg j , k | ) * t , | e c g j , k | &greaterequal; t 0 , | e c g j , k | < t
Wherein, sgn is sign function, ecgJ, kFor the wavelet conversion coefficient on electrocardiosignal different scale,For Estimation wavelet conversion coefficient after threshold process, j is wavelet decomposition scales, and k is k-th data point on corresponding yardstick, t be to Fixed threshold value, β is the arithmetic number more than 1.Wherein it is possible to selected threshold function parameter β=98.
Step s212, described filtered electrocardiosignal is carried out qrs wave group detection obtain qrs wave group.
Specifically, in step s212, first, to filtered electrocardiosignal, namely the signal that reconstruct obtains is wished You convert Bert (hilbert), with signal composition complex analytic signal y (k) after original signal and conversion.Wherein, real part is filtering Electrocardiosignal afterwards, imaginary part is the signal after carrying out Hilbert transform.The amplitude of the complex analytic signal obtaining is filtered The envelope of electrocardiosignal.Thus, what the r summit in electrocardiosignal occurred in envelope rising edge turns position.Then, calculating is described multiple The difference value of the point of analytical function is according to described difference value using the point meeting predetermined condition as qrs wave group Detection Point, described full The point of sufficient predetermined condition exceedes the difference value of predetermined threshold and current point and is less than the difference of former point in the difference value of front 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, when ascensional range is more than predetermined threshold, is considered as being currently at the rising edge of envelope, in rising edge, if current point Difference value be less than former point difference value, then it is believed that finding the flex point of rising edge, namely have found r peak, using secondary as qrs The Detection Point of ripple, and q peak and s peak can be detected based on this.
Step s213, according to described qrs wave group obtain r-r between phase signal.
Step s214, according to qrs wave group using cubic spline interpolation Detection Point is entered row interpolation and through down-sampling peace Sliding process, according to the respiratory sinus arrhythmia feature in electrocardiosignal, extracts described respiratory component signal.
Step s220, according to the corresponding heart rate variability of phase signal acquisition (hrv) instantaneous frequency meansigma methodss between described r-r.
Wherein, step s220 includes:
Step s221, between described r-r phase signal carry out Hilbert-Huang (hilbert-huang) conversion obtain time-frequency Conversion signal.
The time-frequency conversion signal carrying out Hilbert-Huang transform acquisition is the frequency-time letter characterizing phase signal between r-r Number, that is, it characterizes the relation of the instantaneous frequency of phase signal and time between r-r.
The meansigma methodss that step s222, the sliding window by the use of predetermined length calculate described time-frequency conversion signal become as heart rate Different in nature (hrv) instantaneous frequency meansigma methodss, described instantaneous frequency meansigma methodss are used for characterizing the excitement of the parasympathetic nervous system of user Degree.
It is observed that the meansigma methodss 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 intrasonic area (0.003~0.04hz).Represent respectively from high frequency to low frequency by The excitement degree of the parasympathetic nervous system of Rate control reflection is gradually lowered.
Step s230, the envelope signal according to described respiratory component signal acquisition binaryzation.
Specifically, step s230 includes:
Step s231, with 8 layers for Decomposition order, wavelet decomposition is carried out to described respiratory component signal.
Step s232, the wavelet conversion coefficient to 1-3 layer carry out respiratory component sequence s that wavelet reconstruction obtains reconstructing (t).
Step s233, the local maximum obtaining described respiratory component sequence and local minimum.
Step s234, enter between adjacent local maximum interpolation obtain coenvelope u (x), in adjacent local minimum Enter row interpolation between value and obtain lower envelope l (x).Specifically, cubic spline interpolation can be carried out, the spacing of every 30 seconds inserts one Point.Described coenvelope line and lower envelope line cover whole signal.
Step s235, average envelope is obtained according to described coenvelope and described lower envelope.
Specifically can be calculated by equation below:
M (t)=[u (t)+l (t)]/2
Step s236, average envelope m (t) is asked for carry out after first derivative with the envelope letter that binary conversion treatment obtains binaryzation Number m ' (t), that is,The envelope signal of described binaryzation is used for representing sympathetic/parasympathetic Application is through system stimulant dominant characteristics.That is, when m ' (t) is 1, representing and control the parasympathetic nervous system of reflection excited by lung Account for leading position in balance, when m ' (t) is -1, represent that the sympathetic nervous system excitement controlling reflection by lung accounts for leading in balance Status.
Step s240, according to described body movement signal obtain body move status signal.
For body movement signal, it typically appears as the accekeration along three directions of x, y, z.Use according to obtaining accordingly to characterize The parameter of family loosening all muscless degree.In some cases, because the sample frequency of acceleration transducer is too high, can remove extremely After point, according to such as 1hz, sub-sampling is carried out to the acceleration information of acceleration transducer output, suited the requirements with obtaining frequency, It is adapted for the body movement signal processing further.
Specifically, step s240 includes:
Step s241, the body movement signal according to described body movement signal acquisition binaryzation.
Preferably, the overall acceleration amplitude of body movement signal can according to the accekeration of different directions, be calculated, that is, CalculateWherein, act is overall acceleration amplitude, actx、acty、actzIt is respectively The body movement signal accekeration in x, y, z direction.Then binary conversion treatment is carried out to act, that is, act then recognizes more than predetermined threshold It is set to 1, otherwise regard as 0.In a preferred embodiment, predetermined threshold is chosen to be 0.04.That is, having
act &prime; = 1 , a c t &greaterequal; 0.04 0 , a c t < 0.04
Act ' is the body movement signal after binaryzation.
Step s242, using the sliding window of scheduled time length, the body movement signal of described binaryzation is divided, obtain The body taking each time period moves status signal.
Specifically, can sample each 15 seconds before and after current time, the body of the binaryzation of the sliding window of 30 seconds moves letter altogether Number.The number of the numerical value 0 in window exceedes predetermined value, for example, when 24, corresponding for this moment body is moved status signal and is set to 0, that is, body moves status signalOtherwiseIt is to represent when 0 that muscle almost loses to open that body moves status signal Power is in relaxation state.
Step s250, according to described heart rate variability (hrv) instantaneous frequency meansigma methodss, the envelope signal of binaryzation and body Dynamic status signal calculates excited coefficient.
Specifically, can be according to equation below calculating described excitement coefficient:
Wherein, m ' (t) is the envelope signal of described binaryzation,Move status signal for described body.
Methods described also includes step s300, obtains sleep state according to described excitement coefficient.
Specifically, the span of excited coefficient can be -0.997~1.4, when excited coefficient is in scope 1.15~1.4 Stable sleep is may be considered when interior;May be considered unstable sleep when excited coefficient is in scope -0.96~-0.75; May be considered awakening/microarousal state when excited coefficient is in scope -0.997~-0.96;Can when excited coefficient is 0 To be considered rem Sleep stages.
Thus, the present invention passes through to obtain user's electrocardiosignal and body movement signal, obtains the heart based on electrocardio letter signal analysis Rate variability temporal signatures and respiratory component signal, and and then coalition dynamic signal acquisition sign user's parasympathetic nervous system is emerging Put forth energy the excited coefficient of degree, carry out sleep state analysis, thus it is not necessary to the Head And Face in user arranges electrode, slept Reduce the impact for user's sleep quality when dormancy condition monitoring or analysis, improve the accuracy of the dormant data obtaining.
The sleep analysis method of the present invention can apply to the sleep state of healthy human body is monitored it is also possible to apply It is estimated in the disease with sleep disorder as symptom for such as depression etc..
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for those skilled in the art For, the present invention can have various change and change.All any modifications made within spirit and principles of the present invention, equivalent Replace, improve etc., should be included within the scope of the present invention.

Claims (10)

1. a kind of sleep state analysis method, comprising:
The body movement signal obtaining user's electrocardiosignal (ecg) and characterizing 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 and then according to heart rate variability Temporal characteristics and respiratory component signal and described body movement signal obtain the excitement characterizing user's parasympathetic nervous system excitement degree Coefficient;
Sleep state is obtained according to described excitement coefficient.
2. sleep state analysis method according to claim 1 is it is characterised in that described obtain according to user's electrocardiosignal Heart rate variability temporal signatures and respiratory component signal, and and then according to heart rate variability temporal signatures and respiratory component signal and The excited coefficient that described body movement signal obtains sign user's parasympathetic nervous system excitement degree includes:
Phase signal and corresponding respiratory component signal between corresponding r-r is obtained according to user's electrocardiosignal;
According to the corresponding heart rate variability of phase signal acquisition (hrv) instantaneous frequency meansigma methodss between described r-r;
Envelope signal according to described respiratory component signal acquisition binaryzation;
Body is obtained according to described body movement signal and moves status signal;
Status signal meter is moved according to described heart rate variability (hrv) instantaneous frequency meansigma methodss, the envelope signal of binaryzation and body Calculate excited coefficient, described excitement coefficient is used for characterizing the parasympathetic nervous system excitement journey of user.
3. sleep state analysis method according to claim 2 corresponds to it is characterised in that being obtained according to user's electrocardiosignal R-r between phase signal and corresponding respiratory component signal include:
With 8 layers for Decomposition order, wavelet decomposition is carried out to described electrocardiosignal, soft-threshold is carried out to the wavelet conversion coefficient of 1-4 layer Filtering obtains the wavelet conversion coefficient after threshold process, keeps the wavelet conversion coefficient of 5-7 layer constant simultaneously, and removes the 8th layer Wavelet conversion coefficient, the wavelet conversion coefficient based on the wavelet conversion coefficient after threshold process and 5-7 layer carries out wavelet reconstruction Obtain filtered electrocardiosignal;
Described filtered electrocardiosignal is carried out with the detection of qrs wave group and obtains qrs wave group;
Phase signal between r-r is obtained according to described qrs wave group;
Row interpolation is entered and through down-sampling and smoothing processing using cubic spline interpolation according to qrs wave group to Detection Point, according to the heart Respiratory sinus arrhythmia feature in the signal of telecommunication, extracts described respiratory component signal.
4. sleep state analysis method according to claim 3 is it is characterised in that the described wavelet transformation system to 1-4 layer Number carries out the wavelet conversion coefficient after soft-threshold de-noising obtains threshold process and includes being filtered according to equation below:
ecg j , k &overbar; = sgn ( ecg j , k ) - &beta; ( t - | ecg j , k | ) * t , | ecg j , k | &greaterequal; t 0 , | ecg j , k | < t
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 is wavelet decomposition scales, and k is k-th data point on corresponding yardstick, and t is given threshold Value, β is the arithmetic number more than 1.
5. sleep state analysis method according to claim 3 it is characterised in that described carry out qrs wave group detection include:
Filtered electrocardiosignal is carried out with Hilbert (hilbert) conversion, multiple with the signal composition after original signal and conversion Analytic signal, wherein, the real part of described complex analytic signal is original signal, and imaginary part is the signal after conversion
Calculate the difference value of the point of described complex analytic function, according to described difference value using the point meeting predetermined condition as qrs ripple Group's Detection Point, the described point meeting predetermined condition exceedes predetermined threshold and the difference value of current point is little in the difference value of front n point Difference value in former point.
6. sleep state analysis method according to claim 2 it is characterised in that described according to phase signal between described r-r Obtain corresponding heart rate variability (hrv) instantaneous frequency meansigma methodss to include:
Between described r-r, phase signal carries out Hilbert-Huang (hilbert-huang) conversion acquisition time-frequency conversion signal;
Instantaneous as heart rate variability (hrv) by the use of the meansigma methodss of the sliding window described time-frequency conversion signal of calculating of predetermined length Average frequency value, described instantaneous frequency meansigma methodss are used for characterizing the excitement degree of the parasympathetic nervous system of user.
7. sleep state analysis method according to claim 1 it is characterised in that described according to described respiratory component signal The envelope signal obtaining binaryzation includes:
With 8 layers for Decomposition order, wavelet decomposition is carried out to described respiratory component signal;
The respiratory component sequence that wavelet reconstruction obtains reconstructing is carried out to the wavelet conversion coefficient of 1-3 layer;
Obtain local maximum and the local minimum of described respiratory component sequence;
Enter interpolation and obtain coenvelope between adjacent local maximum, enter row interpolation between adjacent local minimum and obtain Lower envelope;
Average envelope is obtained according to described coenvelope and described lower envelope;
Average envelope is asked for after first derivative, carry out the envelope signal that binary conversion treatment obtains binaryzation, the bag of described binaryzation Network signal is used for representing sympathetic/parasympathetic nervous system excitement dominant characteristics.
8. sleep state analysis method according to claim 1 is moved it is characterised in that obtaining body according to described body movement signal Status signal includes:
Obtain the body movement signal of binaryzation according to described body movement signal;
Sliding window using scheduled time length divides to the body movement signal of described binaryzation, obtains each time period Body moves status signal.
9. sleep state analysis method according to claim 6 is it is characterised in that calculate excited system according to equation below Number:
Wherein, m ' (t) is the envelope signal of described binaryzation,Move status signal for described body.
10. sleep state analysis method according to claim 1 is it is characterised in that described electrocardiosignal is the chest lead heart The signal of telecommunication.
CN201610908281.XA 2016-10-18 2016-10-18 A kind of sleep state analysis method Active CN106333652B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610908281.XA CN106333652B (en) 2016-10-18 2016-10-18 A kind of sleep state analysis method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610908281.XA CN106333652B (en) 2016-10-18 2016-10-18 A kind of sleep state analysis method

Publications (2)

Publication Number Publication Date
CN106333652A true CN106333652A (en) 2017-01-18
CN106333652B CN106333652B (en) 2019-10-29

Family

ID=57840084

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610908281.XA Active CN106333652B (en) 2016-10-18 2016-10-18 A kind of sleep state analysis method

Country Status (1)

Country Link
CN (1) CN106333652B (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107961429A (en) * 2017-11-28 2018-04-27 广州视源电子科技股份有限公司 Householder method of sleeping and system, sleeping aid
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
CN108209874A (en) * 2018-01-03 2018-06-29 深圳北航新兴产业技术研究院 A kind of method and apparatus of sleep mode automatically by stages
CN108310587A (en) * 2018-02-02 2018-07-24 贺鹏程 A kind of sleep control device and method
CN108937867A (en) * 2018-06-29 2018-12-07 出门问问信息科技有限公司 A kind of dormant monitoring method and device
CN109222961A (en) * 2018-11-09 2019-01-18 中科数字健康科学研究院(南京)有限公司 A kind of portable sleep monitoring system and relevant 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
CN112656398A (en) * 2020-12-13 2021-04-16 贵州省通信产业服务有限公司 Sleep quality analysis method for unattended nursing

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8083682B2 (en) * 2006-07-19 2011-12-27 Cardiac Pacemakers, Inc. Sleep state detection
CN103717125A (en) * 2011-05-18 2014-04-09 V视股份有限公司 System and method for determining sleep and sleep stages of a person
US20140288391A1 (en) * 2012-06-22 2014-09-25 Fitbit, Inc. Wearable heart rate monitor
CN104244809A (en) * 2012-03-12 2014-12-24 美敦力公司 Heart sound sensing to reduce inappropriate tachyarrhythmia therapy
CN104720746A (en) * 2013-12-20 2015-06-24 中国移动通信集团公司 Sleeping stage determination method and system
CN104720748A (en) * 2013-12-24 2015-06-24 中国移动通信集团公司 Sleep stage determining method and sleep stage determining system
CN105105741A (en) * 2015-07-15 2015-12-02 无锡海鹰电子医疗系统有限公司 Envelope line extracting and feature point tracking method of pulse wave image
CN105769122A (en) * 2016-03-16 2016-07-20 中国科学院电子学研究所 Method for measuring sleep apnea hyponea index
WO2016131630A1 (en) * 2015-02-16 2016-08-25 Ava Ag System and method for determining the fertility phase of a female
CN105943015A (en) * 2016-06-04 2016-09-21 浙江大学 Wearable heart rate variability (HRV) monitoring device with active noise reduction function
CN105962911A (en) * 2015-03-12 2016-09-28 联发科技股份有限公司 Sleep quality management apparatus, processing unit and method

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8083682B2 (en) * 2006-07-19 2011-12-27 Cardiac Pacemakers, Inc. Sleep state detection
CN103717125A (en) * 2011-05-18 2014-04-09 V视股份有限公司 System and method for determining sleep and sleep stages of a person
CN104244809A (en) * 2012-03-12 2014-12-24 美敦力公司 Heart sound sensing to reduce inappropriate tachyarrhythmia therapy
US20140288391A1 (en) * 2012-06-22 2014-09-25 Fitbit, Inc. Wearable heart rate monitor
CN104720746A (en) * 2013-12-20 2015-06-24 中国移动通信集团公司 Sleeping stage determination method and system
CN104720748A (en) * 2013-12-24 2015-06-24 中国移动通信集团公司 Sleep stage determining method and sleep stage determining system
WO2016131630A1 (en) * 2015-02-16 2016-08-25 Ava Ag System and method for determining the fertility phase of a female
CN105962911A (en) * 2015-03-12 2016-09-28 联发科技股份有限公司 Sleep quality management apparatus, processing unit and method
CN105105741A (en) * 2015-07-15 2015-12-02 无锡海鹰电子医疗系统有限公司 Envelope line extracting and feature point tracking method of pulse wave image
CN105769122A (en) * 2016-03-16 2016-07-20 中国科学院电子学研究所 Method for measuring sleep apnea hyponea index
CN105943015A (en) * 2016-06-04 2016-09-21 浙江大学 Wearable heart rate variability (HRV) monitoring device with active noise reduction function

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
余永奇等: "心率变异性在睡眠相关疾病中若干研究进展", 《大家健康》 *
刘冬冬: "基于经验模式分解的心肺耦合技术在睡眠分析中的应用", 《中国医疗设备》 *
肖蒙等: "睡眠期间心电变化规律研究进展", 《航天医学与医学工程》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107961429A (en) * 2017-11-28 2018-04-27 广州视源电子科技股份有限公司 Householder method of sleeping and system, sleeping aid
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
CN108209874A (en) * 2018-01-03 2018-06-29 深圳北航新兴产业技术研究院 A kind of method and apparatus of sleep mode automatically by stages
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
CN108310587A (en) * 2018-02-02 2018-07-24 贺鹏程 A kind of sleep control device and method
CN108937867A (en) * 2018-06-29 2018-12-07 出门问问信息科技有限公司 A kind of dormant monitoring method and device
CN109222961A (en) * 2018-11-09 2019-01-18 中科数字健康科学研究院(南京)有限公司 A kind of portable sleep monitoring system and relevant sleep monitoring method
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
CN112656398A (en) * 2020-12-13 2021-04-16 贵州省通信产业服务有限公司 Sleep quality analysis method for unattended nursing
CN112656398B (en) * 2020-12-13 2022-10-28 贵州省通信产业服务有限公司 Sleep quality analysis method for unattended nursing

Also Published As

Publication number Publication date
CN106333652B (en) 2019-10-29

Similar Documents

Publication Publication Date Title
CN106333652A (en) Sleep state analysis method
US7623912B2 (en) Method, apparatus and system for characterizing sleep
CN103584840B (en) Automatic sleep stage method based on electroencephalogram, heart rate variability and coherence between electroencephalogram and heart rate variability
CN101658425B (en) Device and method for detecting attention focusing degree based on analysis of heart rate variability
CN204931634U (en) Based on the depression evaluating system of physiologic information
CN105147248A (en) Physiological information-based depressive disorder evaluation system and evaluation method thereof
CN114748080B (en) Method and system for detecting and quantifying sensory-motor function
CN104127193A (en) Evaluating system and evaluating method of depressive disorder degree quantization
CN109475311A (en) It determines the method for subjectxperiod&#39;s property physiology course frequency and determines the device and system of subjectxperiod&#39;s property physiology course frequency
CN103892812B (en) Matching degree analytical technology
CN106236027B (en) Depressed crowd&#39;s decision method that a kind of brain electricity is combined with temperature
CN113243890B (en) Sleep apnea syndrome recognition device
Singh et al. Biosignal based on-road stress monitoring for automotive drivers
Roy et al. i-PRExT: Photoplethysmography derived respiration signal extraction and respiratory rate tracking using neural networks
CN114648040A (en) Method for extracting and fusing multiple physiological signals of vital signs
Szypulska et al. Prediction of fatigue and sleep onset using HRV analysis
Sivaranjni et al. Detection of sleep apnea through ECG signal features
CN105125186A (en) Method and system for determining intervention treatment mode
Attarodi et al. Detection of driver's drowsiness using new features extracted from HRV signal
CN106510641A (en) Method and system for detecting different sleep stages of human body
Dembrani et al. Accurate detection of ECG signals in ECG monitoring systems by eliminating the motion artifacts and improving the signal quality using SSG filter with DBE
Bashi et al. Diagnosis of obstructive apnea disease AHI in chemical warfare veterans based on HRV signals analysis using the ANFIS neural network
Karlen et al. Adaptive sleep/wake classification based on cardiorespiratory signals for wearable devices
Sharan ECG-derived respiration for sleep-wake stage classification
Schumann Fluctuations and synchronization in complex physiological systems

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant