CN106344008B - Waking state detection method and system in sleep state analysis - Google Patents
Waking state detection method and system in sleep state analysis Download PDFInfo
- Publication number
- CN106344008B CN106344008B CN201610843516.1A CN201610843516A CN106344008B CN 106344008 B CN106344008 B CN 106344008B CN 201610843516 A CN201610843516 A CN 201610843516A CN 106344008 B CN106344008 B CN 106344008B
- Authority
- CN
- China
- Prior art keywords
- electro
- spike
- ocular signal
- blink
- eeg signals
- 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.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/398—Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4815—Sleep quality
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- Molecular Biology (AREA)
- Veterinary Medicine (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Surgery (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Anesthesiology (AREA)
- Ophthalmology & Optometry (AREA)
- Psychology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The present invention relates to waking state detection method and systems in a kind of analysis of sleep state, the method comprise the steps that acquiring the real-time electro-ocular signal and real-time EEG signals of user after user starts sleep procedure；Wavelet decomposition is carried out to the real-time electro-ocular signal and real-time EEG signals respectively, and signal reconstruction is carried out according to the wavelet coefficient of setting low-frequency range and obtains electro-ocular signal and EEG signals；According to the feature of the correlation and eye electrical waveform of blinking of synchronization EEG signals and electro-ocular signal, blink activity is detected on electro-ocular signal；When detecting blink activity, determine that user is currently at waking state.Technical solution of the present invention, the program can reduce influence of the external interference to waking state testing result, more accurately detect the waking state of user, effectively improve the effect of assisting sleep in user's sleep state analytic process.
Description
Technical field
The present invention relates to assisting sleep technical fields, more particularly to waking state detection side in a kind of analysis of sleep state
Method and system.
Background technique
There are some ancillary equipments on the market at present to carry out auxiliary people's sleep, i.e. assisting sleep, to improve user
Sleep quality.Sleep state analysis is the important means that ancillary equipment understands user's sleep quality, and in the process, it needs
User's sleep state is detected, accurately to know that user is awake or asleep state, then can be carried out corresponding
Intervening measure.
Polysomnogram (Polysomnography, PSG), also known as sleep electroencephalogram are clinically to examine at present for sleeping
It is disconnected and analysis " goldstandard ".Polysomnogram analyzes sleep using a variety of vital signs, in these signs,
Brain electricity is in core status；Utilize 4 species rhythm of brain wave: δ wave (1-3Hz), θ wave (4-7Hz), α wave (8-12Hz), β wave (14-
Frequency characteristic 30Hz).According to the brain wave of the different rhythm and pace of moving things and eye movement feature, other than the awake stage, sleep can be with
It is divided into non-dynamic sleep (No Rapid Eye Movement Sleep, NREM sleep) and the dynamic sleep (Rapid that is sharp-eyed of being sharp-eyed
Eye Movement Sleep, REM sleep) period.Wherein non-dynamic sleep of being sharp-eyed can be divided into 4 periods again: the S1 phase is (completely
Regain consciousness to the transition stage between sleep), the S2 phase (shallowly sleeps the stage), the S3 phase (middle deep sleep), the S4 phase (sound sleep phase).
Under normal circumstances, whether detection user is in waking state, is the signal (δ by utilizing brain wave in 4 frequency ranges
Wave frequency section, θ wave frequency section, α wave frequency section and β wave frequency section) training waking state identification model (classifier) come to EEG signals into
Row identification, these identification models are often the universal identification model being trained using other people brain wave, but due to brain electricity
A human specific of signal is very strong, and the intensity of brain electricity is very weak (brain electricity is microvolt rank, and electrocardio is millivolt rank), in signal
It is easily interfered by outer signals when acquisition, so as to cause the influence that is interfered also is easy in waking state detection process, it is difficult to
The waking state for accurately detecting user is easy to cause and performs the intervening measure of mistake in assisting sleep, influences user's
Sleep quality.
Summary of the invention
Based on this, it is necessary in view of the above-mentioned problems, providing waking state detection method in a kind of analysis of sleep state and being
System, can accurately detect the waking state of EEG signals, effectively improve the accuracy rate of sleep state identification.
Waking state detection method in a kind of analysis of sleep state, comprising:
After user starts sleep procedure, the real-time electro-ocular signal and real-time EEG signals of user are acquired；
Wavelet decomposition is carried out to the real-time electro-ocular signal and real-time EEG signals respectively, and according to the small of setting low-frequency range
Wave system number carries out signal reconstruction and obtains electro-ocular signal and EEG signals；
According to the feature of the correlation and eye electrical waveform of blinking of synchronization EEG signals and electro-ocular signal, in eye telecommunications
Blink activity is detected on number；
When detecting blink activity, determine that user is currently at waking state.
Waking state detection system in a kind of analysis of sleep state, comprising:
Signal acquisition module, for after user starts sleep procedure, acquiring the real-time electro-ocular signal and real-time brain of user
Electric signal；
Signal reconstruction module, for carrying out wavelet decomposition to the real-time electro-ocular signal and real-time EEG signals respectively, and
Signal reconstruction, which is carried out, according to the wavelet coefficient of setting low-frequency range obtains electro-ocular signal and EEG signals；
Blink detection module, for the correlation and blink eye electric wave according to synchronization EEG signals and electro-ocular signal
The feature of shape detects blink activity on electro-ocular signal；
State determination module, for when detecting blink activity, determining that user is currently at waking state.
Waking state detection method and system in above-mentioned sleep state analysis, after user starts sleep procedure, acquisition is used
The real-time electro-ocular signal and real-time EEG signals at family, carry out wavelet decomposition and low-frequency range rebuilds to obtain electro-ocular signal and brain telecommunications
Number；According to the feature of the correlation and eye electrical waveform of blinking of synchronization EEG signals and electro-ocular signal, on electro-ocular signal
Blink activity is detected therefore, it is determined that user is currently at waking state.The program can in user's sleep state analytic process,
Influence of the external interference to waking state testing result is reduced, the waking state of user is more accurately detected, effectively mentions
The effect of high assisting sleep.
Detailed description of the invention
Fig. 1 is the flow chart of waking state detection method in the sleep state analysis of one embodiment；
Fig. 2 is the EEG signals and electro-ocular signal schematic diagram in one common awake period；
Fig. 3 is electro-ocular signal waveform spike area schematic diagram in sliding window；
Fig. 4 is to detect the movable result schematic diagram of blink；
Fig. 5 is waking state detection system structure in the sleep state analysis of one embodiment.
Specific embodiment
The embodiment of waking state detection method and system in sleep state analysis of the invention is illustrated with reference to the accompanying drawing.
Refering to what is shown in Fig. 1, Fig. 1 is the flow chart of waking state detection method in the sleep state analysis of one embodiment,
Include:
Step S101 acquires the real-time electro-ocular signal and real-time EEG signals of user after user starts sleep procedure；
In this step, it can be and user is being carried out to ensure that user is awake in the analysis of the sleep states such as assisting sleep
In the state of, after user starts sleep, to the real-time electro-ocular signal and real-time eeg signal acquisition of user, worn by user
Related eye electricity sensing equipment and brain electricity sensing equipment, the electro-ocular signal and EEG signals that acquisition user generates in sleep procedure.
It can be that a frame is acquired with 30s when acquiring signal, it is subsequent that every frame electro-ocular signal and EEG signals are carried out
Analysis processing.
Step S102 carries out wavelet decomposition to the real-time electro-ocular signal and real-time EEG signals respectively, and according to setting
The wavelet coefficient of low-frequency range carries out signal reconstruction and obtains electro-ocular signal and EEG signals；
Wavelet decomposition is carried out to real-time electro-ocular signal and real-time EEG signals respectively first, and according to the small of setting low-frequency range
Wave coefficient reconstruction electro-ocular signal and EEG signals, in order to avoid high-frequency noise interference simultaneously stick signal essential information, I
EEG signals are analyzed on compared with low-frequency range.For the convenience of calculating, the upper frequency limit (0~8Hz) of θ wave can choose
Carry out wavelet decomposition and reconstruction.
For the convenience of calculating, the upper frequency limit that can choose the θ wave (mainly 4-7Hz) of EEG signals is rebuild,
That is 0~8Hz.
Step S103, according to the spy of the correlation and eye electrical waveform of blinking of synchronization EEG signals and electro-ocular signal
Sign detects blink activity on electro-ocular signal；
In the sleep cycle of normal person, blink is an activity specific to lucid interval, electro-ocular signal when due to blink
Amplitude it is higher, EEG signals can be interfered.
It is the EEG signals and electro-ocular signal schematic diagram in one common awake period with reference to Fig. 2, Fig. 2；Solid line is in figure
EEG signals, dotted line are electro-ocular signal.It is all generated on EEG signals and electro-ocular signal by can be seen that blink activity in figure
Downward spike, blink activity show as the waveform with the high peak of short time on electroencephalogram, this is also blink eye electricity
The feature of waveform.
In above-mentioned detection process, using the correlation between the EEG signals and electro-ocular signal of synchronization, in conjunction with blinking
The wave character of eye electro-ocular signal is judged.
In one embodiment, the movable method of detection blink, be can be such that on electro-ocular signal
(1) the low frequency electro-ocular signal is intercepted using the sliding window with setting signal amplitude and time span；
Similarity degree of the present invention using signal amplitude, electro-ocular signal and EEG signals in a sliding window, spike
Acuity and spike duration to detect whether have blink activity in sliding window, when which verifies electroencephalogram
Between slide on axis, intercept electro-ocular signal waveform.
Since the time of blink is generally less than 0.4 second, can blink in the interior detection of a slightly larger sliding window electric
Signal, such as the sampling time length of 0.6 times of setting, i.e. n=0.6fs, n are sliding window length, and fs is electro-ocular signal
Sample rate.
The amplitude of electro-ocular signal can subtract minimum value (p by the maximum value of electro-ocular signal in sliding windowmax-pmin)
Mode is found out, and under normal circumstances, the amplitude of sliding window can be set to 75 microvolts between 300 microvolts.
(2) related coefficient of the waveform of electro-ocular signal and synchronization EEG signals in sliding window, sliding are calculated separately
The duration of the acuity parameter of electro-ocular signal waveform spike and spike in window；
Here acuity parameter be characterize feature high peak of short time when meeting blink of spike waveform it is strong and weak
Parameter.
In one embodiment, calculate sliding window in electro-ocular signal waveform spike acuity parameter, may include
It is as follows:
(a) upper area area and lower area area of the electro-ocular signal waveform in sliding window are calculated separately, is calculated
Formula is as follows:
In formula, piFor the electro-ocular signal in sliding window, pmaxFor the maximum value of electro-ocular signal in sliding window, pminFor cunning
The minimum value of electro-ocular signal, area in dynamic windowupIndicate upper area area, areadownIndicate lower area area；
(b) area of the electro-ocular signal waveform spike according to the upper area area and lower area areal calculation,
Calculation formula is as follows:
In formula, blinkareaIndicate the area of spike, if expression meets condition；
Refering to what is shown in Fig. 3, Fig. 3 is electro-ocular signal waveform spike area schematic diagram in sliding window, the spike in two kinds of directions
For upper and lower part region area as shown, left figure spike direction is upward, the spike direction of right figure is downward.
(c) according to spike areal calculation acuity parameter, calculation formula is as follows:
blinkratio=blinkarea/in-blinkarea
In formula, blinkratioIndicate acuity parameter, in-blinkareaIndicate the area of non-peak portion, it is sharp here
It is ratio between upper area area and lower area area that sharp extent index, which can also be converted into,.
The method for calculating the duration of electro-ocular signal waveform spike in sliding window, may include as follows:
(d) direction of the electro-ocular signal waveform spike according to the upper area area and lower area areal calculation,
Calculation formula is as follows:
In formula, blinkdirectionIndicate spike direction, labeled as 1 indicate spike it is downward, labeled as -1 indicate spike to
On；
(e) when spike is downward, the duration of spike is calculated according to the Local modulus maxima of spike two sides；Or in point
When peak is upward, the duration of spike is calculated according to the local local minizing point of spike two sides；Calculation formula is as follows:
blinkts=vertexright-vertexleft,
In formula, blinktsIndicate the duration of spike, vertexrightIt indicates the right side vertex moment of spike, indicates point
The leftmost vertices moment at peak, right_min_loca indicate the local minizing point on the right side of spike, and right_max_loca is indicated
Local modulus maxima on the right side of spike, left_min_loca indicate the local minizing point on the left of spike, left_max_loca
Indicate that the Local modulus maxima on the left of spike, s.t. indicate constraint condition.
(3) if the related coefficient, acuity parameter and duration meet respectively preset correlation coefficient threshold,
Acuity parameter threshold and duration threshold judge that there are blink activities for electro-ocular signal in the sliding window；
Specifically, the area of the similarity degree of signal amplitude, electro-ocular signal and EEG signals in the sliding window, spike
When all meeting condition with the duration, that is, thinking current sliding window mouth, there is blink activities；
For correlation coefficient threshold, it is considered that, two vectors of the related coefficient greater than 0.7 substantially may be considered just
It is relevant, it is contemplated that interference of the electro-ocular signal to EEG signals when blink, phase when blink between electro-ocular signal and EEG signals
Closing coefficient threshold can be set to 0.9；For acuity parameter threshold, 0.3 generally can be set to；For spike it is lasting when
Between threshold value, be usually 0.3-0.4 seconds according to wink time, therefore, duration threshold can be set to 0.3 second.
Step S104 determines that user is currently at waking state when detecting blink activity；
In the sleep cycle of normal person, blink is an activity specific to lucid interval, therefore, if awake in detection
In state procedure, if having detected blink activity, i.e., it is believed that being presently at waking state within certain a period of time.
With reference to Fig. 4, Fig. 4 is to detect the movable result schematic diagram of blink, and solid line and dotted line are respectively to pass through small echo in figure
EEG signals and electro-ocular signal after transform reconstruction.On electro-ocular signal, circle has marked blink activity and has been formed by spike, can
With discovery, although the blink amount of activity detected is less, false detection rate is extremely low.
Further, in practical applications, in order to avoid erroneous detection goes out blink activity bring misrecognition, with 30 seconds eyes of a frame
For electric signal, when at least to detect 2 or 2 or more blink activities in a frame signal, determine that user is currently at
Waking state, this makes it possible to detection accuracy is greatly improved.
In one embodiment, waking state detection method in sleep state analysis of the invention, can also be from starting to adopt
In setting time T after collecting real-time EEG signals and real-time electro-ocular signal, determine that user is constantly in waking state.
Generally, it is contemplated being 10~15 minutes to the normal time for falling asleep of people, time for falling asleep is very when tired/tired out
It can extremely shorten, therefore, start the EEG signals for acquiring user under the wide-awake state of user, it is believed that starting to acquire
In a set period of time afterwards, user is in waking state, and as embodiment, the period that the present invention selects is 300
Second (5 minutes), i.e. user, which start to acquire in 300 seconds after EEG signals, can all be judged as waking state.
In order to further increase Detection accuracy, on the basis of activity scheme is blinked in the detection based on electro-ocular signal, this
Invention is directed to the EEG signals of acquisition, can also be detected by Sample Entropy manner of comparison.
The technical solution of use can be such that
The Sample Entropy of the EEG signals is calculated first, then carries out the Sample Entropy with the sample entropy threshold precalculated
Compare, if the Sample Entropy is greater than the sample entropy threshold, determines that user is currently at waking state.
Sample Entropy is a kind of measurement of time series complexity, is widely used in the detection of epilepsy.In sleep cycle
6 stages in, regain consciousness the stage EEG signals Sample Entropy highest, the present invention using the size of the Sample Entropy of EEG signals come
Judge whether user is waking state, by setting sample entropy threshold, is compared with the Sample Entropy of EEG signals.
Further, in above-mentioned comparison procedure, the selection of sample entropy threshold is also a vital ring.Insane at present
The empirical data that detection of epilepsy etc. obtains is not appropriate for being used in the accurate judgement in sleep state analysis to waking state.
As in the prior embodiments, in the setting time T after starting acquisition signal, user is construed as regaining consciousness
, therefore, in the case where ensuring user's waking state, the EEG signals that just can use the user acquired in this period calculate sample
This entropy threshold.
In one embodiment, sample entropy threshold can be calculated with the following method, comprising:
The EEG signals in setting time are divided into multiple samples first, and calculate separately the sample of each EEG signals sample
This entropy obtains Sample Entropy set；In the process, it is assumed that using 30s be a frame, 300 seconds EEG signals of acquisition are handled, that
Here just there are 10 samples, the Sample Entropy set including 10 Sample Entropies can be calculated at this time.
Then sample entropy threshold is calculated according to the Sample Entropy set；As embodiment, the calculation formula of sample entropy threshold
It can be such that
sampen_vali=sampen (y [p_start:p_end])
P_start=(i-1) * time_length*fs+1
P_end=t_start+time_length*fs-1
P_end < Tfs
In formula, wherein sampen_thre is sample entropy threshold, sampen_valiFor i-th sample in Sample Entropy set
Sample Entropy, sampen are the operation for seeking Sample Entropy, and input y [p_start:p_end] is EEG signals y in pth _ start point
Start the part until pth _ end point, time_length is the time span for calculating each sample of Sample Entropy, and fs is brain
The sample rate of electric signal, T are to start to acquire the setting time after EEG signals, and v is setup parameter.
In above-mentioned numerical procedure, the value of parameter v is extremely important, can control recognition accuracy by parameter v；Cause
This can calculate parameter v value to improve recognition accuracy by following formula:
Assuming that the set X of Sample Entropy when the awake stage obeys standardized normal distribution, i-th of element representation in set X are as follows:
Wherein,
At this point, x=v
According to the integral of standard normal distribution function:
Wherein, P (X≤x) indicates that the value in the set X of Sample Entropy is less than the probability of x, it is possible thereby to calculate, with T=
For 300s, time_length=30s, as parameter v=2.58, probability of the value less than x of Sample Entropy set X is 99.5%.
Finally, by by the Sample Entropy and Sample Entropy threshold value comparison of the EEG signals calculating of present frame, if Sample Entropy is big
In sample entropy threshold, that is, can determine whether to determine that user is currently at waking state.
In this step, it is contemplated that the Sample Entropy of the EEG signals in awake stage is maximum, the sample calculated based on the above embodiment
This entropy threshold sampen_thre, it is known that the EEG signals by Sample Entropy greater than sampen_thre are judged as that waking state can obtain
To compared with high-accuracy.
The scheme of above-described embodiment, detection blink it is movable on the basis of, further judged by EEG signals Sample Entropy,
Detection accuracy can be further increased.It, can be to avoid can in blink activity detection by further to Sample Entropy threshold decision
It fails to judge existing for energy situation.By taking mono- frame of EEG signals and electro-ocular signal 30s as an example, if do not examined in the electro-ocular signal of the same period
Blink activity is measured, the function of reinspection and leak repairing is reached by Sample Entropy threshold decision.
Waking state detection method in the sleep state analysis of the embodiment of the present invention, is carried out in many cases using EEG signals
In waking state test experience, accurate judgement, accuracy with higher have been obtained.
The scheme of the embodiment of the present invention carries out accurate judgement to waking state, can form waking state detector, the inspection
If surveying device output result is "Yes", i.e. judgement current state is waking state, if output result is "No", be can determine that current
State is uncertain state (neither waking state but can not be considered sleep state).
Accuracy rate relative to conventional method may be subjected to interference effect, and technology of the invention is tighter for part interference
The signal of weight, may will affect recall rate, but not interfere with accuracy rate, can be adapted in sleep state analysis for clear
The detection identification for the state of waking up.
Refering to what is shown in Fig. 5, Fig. 5 is waking state detection system structural representation in the sleep state analysis of one embodiment
Figure, comprising:
Signal acquisition module 101, for after user starts sleep procedure, acquire user real-time electro-ocular signal and in real time
EEG signals；
Signal reconstruction module 102, for carrying out wavelet decomposition to the real-time electro-ocular signal and real-time EEG signals respectively,
And signal reconstruction is carried out according to the wavelet coefficient of setting low-frequency range and obtains electro-ocular signal and EEG signals；
Blink detection module 103, for the correlation and blink eye according to synchronization EEG signals and electro-ocular signal
The feature of electrical waveform detects blink activity on electro-ocular signal；
State determination module 104, for when detecting blink activity, determining that user is currently at waking state.
Waking state detection system and awake shape in sleep state analysis of the invention in sleep state analysis of the invention
State detection method corresponds, the technical characteristic that the embodiment of waking state detection method illustrates in the analysis of above-mentioned sleep state
And its advantages suitable for sleep state analysis waking state detection system embodiment in, hereby give notice that.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (9)
1. waking state detection method in a kind of sleep state analysis characterized by comprising
After user starts sleep procedure, the real-time electro-ocular signal and real-time EEG signals of user are acquired；
Wavelet decomposition is carried out to the real-time electro-ocular signal and real-time EEG signals respectively, and according to the wavelet systems of setting low-frequency range
Number carries out signal reconstruction and obtains electro-ocular signal and EEG signals；
According to the feature of the correlation and eye electrical waveform of blinking of synchronization EEG signals and electro-ocular signal, on electro-ocular signal
Detect blink activity；
When detecting blink activity, determine that user is currently at waking state；
The feature of the correlation and eye electrical waveform of blinking according to synchronization EEG signals and electro-ocular signal, in eye telecommunications
The step of detection blink activity includes that the sliding window with setting signal amplitude and time span is utilized to intercept low frequency eye on number
Electric signal；Calculate separately the related coefficient of the waveform of electro-ocular signal and synchronization EEG signals in sliding window, sliding window
The acuity parameter of interior electro-ocular signal waveform spike and the duration of spike；If the related coefficient, acuity are joined
The several and duration meets preset correlation coefficient threshold, acuity parameter threshold and duration threshold respectively, judges
There are blink activities for electro-ocular signal in the sliding window.
2. waking state detection method in sleep state analysis according to claim 1, which is characterized in that the calculating is slided
The step of acuity parameter of electro-ocular signal waveform spike, includes: in dynamic window
Upper area area and lower area area of the electro-ocular signal waveform in sliding window are calculated separately, calculation formula is such as
Under:
In formula, piFor the electro-ocular signal in sliding window, pmaxFor the maximum value of electro-ocular signal in sliding window, pminFor sliding window
The minimum value of electro-ocular signal, area in mouthfulupIndicate upper area area, areadownIndicate lower area area；
According to the area of electro-ocular signal waveform spike described in the upper area area and lower area areal calculation, calculation formula
It is as follows:
In formula, blinkareaIndicate the area of spike, if expression meets condition；
According to spike areal calculation acuity parameter, calculation formula is as follows:
blinkratio=blinkarea/in-blinkarea
In formula, blinkratioIndicate acuity parameter, in-blinkareaIndicate the area of non-peak portion.
3. waking state detection method in sleep state analysis according to claim 2, which is characterized in that the calculating is slided
The step of duration of electro-ocular signal waveform spike, includes: in dynamic window
According to the direction of electro-ocular signal waveform spike described in the upper area area and lower area areal calculation, calculation formula
It is as follows:
In formula, blinkdirectionIt indicates spike direction, indicates that spike is downward labeled as 1, indicate that spike is upward labeled as -1；
When spike is downward, the duration of spike is calculated according to the Local modulus maxima of spike two sides；Or it is upward in spike
When, the duration of spike is calculated according to the local minizing point of spike two sides；Calculation formula is as follows:
blinkts=vertexright-vertexleft,
In formula, blinktsIndicate the duration of spike, vertexrightAt the right side vertex moment for indicating spike, indicate spike
Leftmost vertices moment, right_min_loca indicate the local minizing point on the right side of spike, and right_max_loca indicates spike
The Local modulus maxima on right side, left_min_loca indicate the local minizing point on the left of spike, and left_max_loca is indicated
Local modulus maxima on the left of spike, s.t. indicate constraint condition.
4. waking state detection method in sleep state analysis according to claim 3, which is characterized in that the sliding window
Mouthful amplitude be 75 microvolts to 300 microvolts, the correlation coefficient threshold when blink between electro-ocular signal and EEG signals is
0.9, the acuity parameter threshold is 0.3, and the duration threshold is 0.3 second.
5. waking state detection method in sleep state analysis according to any one of claims 1 to 4, which is characterized in that
Further include:
In setting time T after starting to acquire real-time EEG signals and real-time electro-ocular signal, it is awake to determine that user is constantly in
State.
6. waking state detection method in sleep state analysis according to claim 1, which is characterized in that further include:
The Sample Entropy is compared, if described by the Sample Entropy for calculating the EEG signals with the sample entropy threshold precalculated
Sample Entropy is greater than the sample entropy threshold, then determines that user is currently at waking state.
7. waking state detection method in sleep state analysis according to claim 6, which is characterized in that the Sample Entropy
The calculation formula of threshold value is as follows:
sampen_vali=sampen (y [p_start:p_end])
P_start=(i-1) * time_length*fs+1
P_end=p_start+time_length*fs-1
P_end < Tfs
In formula, wherein sampen_thre is sample entropy threshold, sampen_valiFor the sample of i-th of sample in Sample Entropy set
Entropy, sampen are the operation for seeking Sample Entropy, and input y [p_start:p_end] is that EEG signals y starts in pth _ start point
Part until pth _ end point, time_length are the time span for calculating each sample of Sample Entropy, and fs is brain telecommunications
Number sample rate, T be start acquire EEG signals after setting time, v is setup parameter.
8. waking state detection method in sleep state analysis according to claim 7, which is characterized in that the parameter v
Value it is as follows:
Wherein,
X=v
In formula, X indicates Sample Entropy set, XiIndicate that i-th of element in Sample Entropy set X, Φ (x) indicate standardized normal distribution letter
Several integrals, P (X≤x) indicate that the value in the set X of Sample Entropy is less than the probability of x.
9. waking state detection system in a kind of sleep state analysis characterized by comprising
Signal acquisition module, for after user starts sleep procedure, acquire user real-time electro-ocular signal and real-time brain telecommunications
Number；
Signal reconstruction module is used for respectively to the real-time electro-ocular signal and real-time EEG signals progress wavelet decomposition, and according to
The wavelet coefficient of setting low-frequency range carries out signal reconstruction and obtains electro-ocular signal and EEG signals；
Blink detection module, for according to the correlations of synchronization EEG signals and electro-ocular signal and blink eye electrical waveform
Feature detects blink activity on electro-ocular signal；The blink detection module is also used to, using with setting signal amplitude and when
Between length sliding window intercept low frequency electro-ocular signal；Calculate separately electro-ocular signal and synchronization EEG signals in sliding window
Waveform related coefficient, the acuity parameter of electro-ocular signal waveform spike and the duration of spike in sliding window；
If the related coefficient, acuity parameter and duration meet preset correlation coefficient threshold, acuity ginseng respectively
Number threshold value and duration threshold, judge that there are blink activities for electro-ocular signal in the sliding window；
State determination module, for when detecting blink activity, determining that user is currently at waking state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610843516.1A CN106344008B (en) | 2016-09-21 | 2016-09-21 | Waking state detection method and system in sleep state analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610843516.1A CN106344008B (en) | 2016-09-21 | 2016-09-21 | Waking state detection method and system in sleep state analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106344008A CN106344008A (en) | 2017-01-25 |
| CN106344008B true CN106344008B (en) | 2019-05-07 |
Family
ID=57859227
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610843516.1A Active CN106344008B (en) | 2016-09-21 | 2016-09-21 | Waking state detection method and system in sleep state analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106344008B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107361745B (en) * | 2017-08-08 | 2021-01-01 | 浙江纽若思医疗科技有限公司 | Supervised sleep electroencephalogram and electrooculogram mixed signal stage interpretation method |
| CN109697831A (en) * | 2019-02-25 | 2019-04-30 | 湖北亿咖通科技有限公司 | Fatigue driving monitoring method, device and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU654910B2 (en) * | 1993-02-22 | 1994-11-24 | Yong-xin SHI | Method and apparatus for ambulatory monitoring of wake or sleep status |
| EP1983364A1 (en) * | 2007-04-18 | 2008-10-22 | National Yang-Ming University | Sunglass Type Sleep Detecting and Preventing Device |
| CN101583313A (en) * | 2007-01-19 | 2009-11-18 | 旭化成株式会社 | Awake state judging model making device, awake state judging device, and warning device |
| CN101599127A (en) * | 2009-06-26 | 2009-12-09 | 安徽大学 | The feature extraction of electro-ocular signal and recognition methods |
| CN105105714A (en) * | 2015-08-26 | 2015-12-02 | 吴建平 | Sleep period separating method and system |
-
2016
- 2016-09-21 CN CN201610843516.1A patent/CN106344008B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU654910B2 (en) * | 1993-02-22 | 1994-11-24 | Yong-xin SHI | Method and apparatus for ambulatory monitoring of wake or sleep status |
| CN101583313A (en) * | 2007-01-19 | 2009-11-18 | 旭化成株式会社 | Awake state judging model making device, awake state judging device, and warning device |
| EP1983364A1 (en) * | 2007-04-18 | 2008-10-22 | National Yang-Ming University | Sunglass Type Sleep Detecting and Preventing Device |
| CN101599127A (en) * | 2009-06-26 | 2009-12-09 | 安徽大学 | The feature extraction of electro-ocular signal and recognition methods |
| CN105105714A (en) * | 2015-08-26 | 2015-12-02 | 吴建平 | Sleep period separating method and system |
Non-Patent Citations (1)
| Title |
|---|
| 基于样本熵的睡眠脑电分期;和卫星 等;《江苏大学学报（自然科学版）》;20090930;第30卷(第5期);第501-504页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106344008A (en) | 2017-01-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106175754B (en) | Waking state detection device in sleep state analysis | |
| WO2020119245A1 (en) | Wearable bracelet-based emotion recognition system and method | |
| CN106419869A (en) | Real-time sleep staging detection method based on piezoelectric sensor and device for realizing method | |
| CN104173064B (en) | Lie detecting method based on analysis of heart rate variability and device of detecting a lie | |
| CN110135285B (en) | Electroencephalogram resting state identity authentication method and device using single-lead equipment | |
| US20110257556A1 (en) | Systems and methods for quantitatively characterizing slow wave activities and states in sleep | |
| US20220280096A1 (en) | Ssvep-based attention evaluation method, training method, and brain-computer interface | |
| CN104720746A (en) | Sleeping stage determination method and system | |
| CN106333676B (en) | The annotation equipment of EEG signals data type under waking state | |
| CN109199411B (en) | Case-conscious person identification method based on model fusion | |
| CN106344008B (en) | Waking state detection method and system in sleep state analysis | |
| CN106361327B (en) | Waking state detection method and system in sleep state analysis | |
| CN106175698B (en) | Sleep cycle detection device in sleep state analysis | |
| CN114145717A (en) | Sleep state analysis method based on PPG heart rate characteristic parameters and motion quantity | |
| CN108392176A (en) | A kind of Sleep architecture detection method based on the acquisition of heart impact signal | |
| CN106333674B (en) | Sleep cycle detection method and system in sleep state analysis | |
| CN106333677A (en) | Blinking activity detection method and blinking activity detection system in sleep state analysis | |
| CN106388812B (en) | The mask method and system of EEG signals data type under waking state | |
| CN106333675B (en) | The mask method and system of EEG signals data type under waking state | |
| CN106333678A (en) | Method and system for detecting brain wave at sleep state in electroencephalogram | |
| TW202108077A (en) | Focus automatic detection method and system | |
| CN106377250B (en) | The tagging equipment of EEG signals data type under sleep state | |
| CN106175755B (en) | Sleep state detector for sleep state analysis | |
| CN106236084B (en) | The mask method and system of EEG signals data type under waking state | |
| CN106377249B (en) | Sleep state detection method and system in sleep state analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |