CN106344008A - Method and system for detecting waking state in sleep state analysis - Google Patents

Abstract

The invention relates to a method and a system for detecting a waking state in sleep state analysis. The method comprises the following steps: real-time eye electric signals and real-time brain electric signals of a user are collected after the user starts sleep; the real-time eye electric signals and the real-time brain electric signals are subjected to wavelet decomposition respectively, signal reconstruction is performed according to wavelet coefficients of set low-frequency bands, and eye electric signals and brain electric signals are obtained; blink activities are detected on the eye electric signals according to the correlation between the brain electric signals and the eye electric signals at the same moment and features of eye electric waveforms of blinks; when the blink activities are detected, the condition that the user is in the waking state currently is confirmed. With adoption of the technical scheme, the influence of external interference on a waking state detection result can be reduced in a user sleep state analysis process, the waking state of the user is detected more accurately, and the sleep assisting effect is improved effectively.

Description

Waking state detection method and system in sleep state analysis

Technical field

The present invention relates to assisting sleep technical field, waking state detection side in more particularly to a kind of sleep state analysis Method and system.

Background technology

Some auxiliary equipments have been had to carry out assisting people to fall asleep at present on the market, i.e. assisting sleep, to improve user Sleep quality.Sleep state analysis is the important means that auxiliary equipment understands user's sleep quality, and in the process, needs User's sleep state is detected, accurately to know that user is clear-headed or asleep state, then can carry out corresponding Intervening measure.

Polysomnogram (polysomnography, psg), also known as sleep electroencephalogram, is clinically to examine for sleep at present " goldstandard " breaking and analyzing.Polysomnogram is analyzed to sleep using multiple vital signs, in these sign, Brain electricity is in core status；Using brain wave 4 species rhythm: δ ripple (1-3hz), θ ripple (4-7hz), α ripple (8-12hz), β ripple (14- Frequency characteristic 30hz).Brain wave according to the different rhythm and pace of moving things and ocular movement feature, in addition to the clear-headed stage, sleep is permissible 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) cycle.Wherein non-dynamic sleep of being sharp-eyed can be divided into 4 periods again: the s1 phase is (completely The transition stage regained consciousness to sleep), the s2 phase (shallow sleep the stage), the s3 phase (middle deep sleep), the s4 phase (sound sleep phase).

Generally, whether detection user is in waking state, is the signal (δ by using brain wave in 4 frequency ranges Wave frequency section, θ wave frequency section, α wave frequency section and β wave frequency section) train the identification model (grader) of waking state EEG signals are entered Row identification, the universal identification model that these identification models are often trained using other people brain wave, but due to brain electricity The individual human specific of signal is very strong, and the intensity of brain electricity very weak (brain electricity is microvolt rank, and electrocardio is millivolt rank), in signal Easily disturbed by outer signals during collection, thus lead in waking state detection process to be also easily interfered impact it is difficult to Detect the waking state of user exactly, be easily caused the intervening measure performing mistake in assisting sleep, impact user's Sleep quality.

Content of the invention

Based on this it is necessary to be directed to the problems referred to above, waking state detection method is provided in a kind of analysis of sleep state and is System, can detect the waking state of EEG signals exactly, effectively improve the accuracy rate of sleep state identification.

Waking state detection method in a kind of sleep state analysis, comprising:

After user starts sleep procedure, gather the real-time electro-ocular signal of user and real-time EEG signals；

Respectively described real-time electro-ocular signal and real-time EEG signals are carried out with wavelet decomposition, and little according to setting low-frequency range Wave system number carries out signal reconstruction and obtains electro-ocular signal and EEG signals；

Dependency according to synchronization EEG signals and electro-ocular signal and the feature of nictation eye electrical waveform, in eye telecommunications Detection nictation activity on number；

When nictation activity is detected, judge that user is currently at waking state.

Waking state detecting system in a kind of sleep state analysis, comprising:

Signal acquisition module, for, after user starts sleep procedure, gathering the real-time electro-ocular signal of user and real-time brain The signal of telecommunication；

Signal reconstruction module, for respectively wavelet decomposition being carried out to described real-time electro-ocular signal and real-time EEG signals, and Wavelet coefficient according to setting low-frequency range carries out signal reconstruction and obtains electro-ocular signal and EEG signals；

Blink detection module, for the dependency according to synchronization EEG signals and electro-ocular signal and nictation eye electric wave The feature of shape, detects nictation activity on electro-ocular signal；

State determination module, for when nictation activity is detected, judging that user is currently at waking state.

Waking state detection method and system in above-mentioned sleep state analysis, after user starts sleep procedure, collection is used The real-time electro-ocular signal at family and real-time EEG signals, carry out wavelet decomposition and low-frequency range is rebuild and obtained electro-ocular signal and brain telecommunications Number；Dependency according to synchronization EEG signals and electro-ocular signal and the feature of nictation eye electrical waveform, on electro-ocular signal Detection nictation activity is thus judge that user is currently at waking state.The program can user's sleep state analysis during, Reduce the impact to waking state testing result for the external interference, more accurately detect the waking state of user, effectively carry The effect of high assisting sleep.

Brief description

Fig. 1 be an embodiment sleep state analysis in waking state detection method flow chart；

Fig. 2 is one section of common EEG signals in clear-headed period and electro-ocular signal schematic diagram；

Fig. 3 is electro-ocular signal waveform spike area schematic diagram in sliding window；

Fig. 4 is result schematic diagram nictation activity is detected；

Fig. 5 be an embodiment sleep state analysis in waking state detecting system structural representation.

Specific embodiment

Illustrate the embodiment of waking state detection method and system in the sleep state analysis of the present invention below in conjunction with the accompanying drawings.

With reference to shown in Fig. 1, Fig. 1 be an embodiment sleep state analysis in waking state detection method flow chart, Including:

Step s101, after user starts sleep procedure, gathers the real-time electro-ocular signal of user and real-time EEG signals；

In this step, can be in user is carried out with the analysis of the sleep states such as assisting sleep, guarantee that user is clear-headed In the state of, after user starts sleep, the real-time electro-ocular signal to user and real-time eeg signal acquisition, worn by user Related eye electricity sensing equipment and brain electricity sensing equipment, electro-ocular signal and EEG signals that collection user produces in sleep procedure.

When gathering signal, can be acquired for a frame with 30s, subsequently every frame electro-ocular signal and EEG signals be carried out Analyzing and processing.

Step s102, carries out wavelet decomposition to described 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；

Respectively real-time electro-ocular signal and real-time EEG signals are carried out with wavelet decomposition first, and little according to setting low-frequency range Ripple coefficient reconstruction electro-ocular signal and EEG signals, in order to avoid the essential information of the interference stick signal simultaneously of high-frequency noise, I Compared with being analyzed to EEG signals in low-frequency range.For the convenience calculating, the upper frequency limit (0～8hz) of θ ripple can be selected Carry out wavelet decomposition and reconstruction.

For the convenience calculating, the upper frequency limit of the θ ripple (mainly 4-7hz) of EEG signals can be selected to be rebuild, I.e. 0～8hz.

Step s103, the spy of the dependency according to synchronization EEG signals and electro-ocular signal and nictation eye electrical waveform Levy, nictation activity is detected on electro-ocular signal；

In the sleep cycle of normal person, nictation is an activity specific to lucid interval, due to electro-ocular signal during nictation Amplitude higher, EEG signals can be interfered.

With reference to Fig. 2, Fig. 2 is one section of common EEG signals in clear-headed period and electro-ocular signal schematic diagram；In figure solid line is EEG signals, dotted line is electro-ocular signal.By in figure as can be seen that nictation activity all produces in EEG signals and electro-ocular signal Downward spike, nictation activity shows as the waveform with the high peak of short time on electroencephalogram, and this is also nictation eye electricity The feature of waveform.

In above-mentioned detection process, using the dependency between the EEG signals of synchronization and electro-ocular signal, in conjunction with blinking The wave character of eye electro-ocular signal is judged.

In an embodiment, the method that nictation activity is detected on electro-ocular signal, can be such that

(1) intercept described low frequency electro-ocular signal using the sliding window with setting signal amplitude and time span；

The present invention utilizes the similarity degree of the signal amplitude, electro-ocular signal and EEG signals in a sliding window, spike Acuity and spike duration detecting in sliding window whether there is nictation activity, during this sliding window checking electroencephalogram Slide on countershaft, intercept electro-ocular signal waveform.

Because the time of nictation is generally less than 0.4 second, therefore can be in the interior detection nictation electricity of a slightly larger sliding window Signal, such as the sampling time length of 0.6 times of setting, i.e. n=0.6 fs, n are sliding window length, and fs is electro-ocular signal Sample rate.

The amplitude of electro-ocular signal can deduct minima (p by the maximum of electro-ocular signal in sliding window_max-p_min) Mode is obtained, and generally, the amplitude of sliding window could be arranged between 75 microvolts to 300 microvolts.

(2) calculate electro-ocular signal and the correlation coefficient of the waveform of synchronization EEG signals in sliding window respectively, slide The persistent period of the acuity parameter of electro-ocular signal waveform spike and spike in window；

Here acuity parameter be characterize spike feature meet the high peak of short time during nictation waveform strong and weak Parameter.

In one embodiment, calculate the acuity parameter of electro-ocular signal waveform spike in sliding window, can include As follows:

A () calculates upper area area in sliding window for the electro-ocular signal waveform and lower area area respectively, calculate Formula is as follows:

${\mathrm{area}}_{up}=\underset{i=1}{\overset{n}{\σ}}(p_{max}-p_i)$

${\mathrm{area}}_{down}=\underset{i=1}{\overset{n}{\σ}}(p_i-p_{min})$

In formula, p_iFor the electro-ocular signal in sliding window, p_maxFor the maximum of electro-ocular signal in sliding window, p_minFor cunning The minima of electro-ocular signal, area in dynamic window_upRepresent upper area area, area_downRepresent lower area area；

(b) according to the area of described upper area area and electro-ocular signal waveform spike described in lower area areal calculation, Computing formula is as follows:

${\mathrm{blink}}_{area}=\left\{\begin{array}{ccc}{\mathrm{area}}_{up}& if& {\mathrm{area}}_{up}<{\mathrm{area}}_{down}\\ {\mathrm{area}}_{down}& if& {\mathrm{area}}_{up}>{\mathrm{area}}_{down}\end{array}\right.$

In formula, blink_areaRepresent the area of spike, if represents and meets condition；

With reference to 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 Upper and lower part region area as illustrated, left figure spike direction upwards, the spike direction of right figure is downward.

C (), according to spike areal calculation acuity parameter, computing formula is as follows:

blink_ratio=blink_area/in-blink_area

In formula, blink_ratioRepresent acuity parameter, in-blink_areaRepresent the area of non-peak position, here point It is ratio between upper area area and lower area area that sharp extent index can also be converted into.

The method calculating the persistent period of electro-ocular signal waveform spike in sliding window, can include the following:

(d) according to the direction of described upper area area and electro-ocular signal waveform spike described in lower area areal calculation, Computing formula is as follows:

${\mathrm{blink}}_{direction}=\left\{\begin{array}{ccc}1& if& {\mathrm{area}}_{up}<{\mathrm{area}}_{down}\\ -1& if& {\mathrm{area}}_{up}>{\mathrm{area}}_{down}\end{array}\right.$

In formula, blink_directionRepresent spike direction, be labeled as 1 expression spike downwards, be labeled as -1 expression spike to On；

E (), when spike is downward, the Local modulus maxima according to spike both sides calculates the persistent period of spike；Or in point Peak upwards when, calculate the persistent period of spike according to the local local minizing point of spike both sides；Computing formula is as follows:

blink_ts=vertex_right-vertex_left,

$s.t.\left\{\begin{array}{cccc}{\mathrm{vertex}}_{right}=right\_\min \_loca& {\mathrm{vertex}}_{left}=left\_\min \_loca& if& {\mathrm{blink}}_{direction}=1\\ {\mathrm{vertex}}_{right}=right\_\max \_loca& {\mathrm{vertex}}_{left}=left\_\max \_loca& if& {\mathrm{blink}}_{direction}=-1\end{array}\right.$

In formula, blink_tsRepresent the persistent period of spike, vertex_rightRepresent the right side summit moment of spike, represent point In the leftmost vertices moment at peak, right_min_loca represents the local minizing point on the right side of spike, and right_max_loca represents Local modulus maxima on the right side of spike, left_min_loca represents the local minizing point on the left of spike, left_max_loca Represent the Local modulus maxima on the left of spike, s.t. represents constraints.

(3) if described correlation coefficient, acuity parameter and persistent period respectively meet default correlation coefficient threshold, Acuity parameter threshold and duration threshold, judge that in this sliding window, electro-ocular signal has nictation activity；

Specifically, when the area of the similarity degree of the signal amplitude in sliding window, electro-ocular signal and EEG signals, spike When all meeting condition with the persistent period, that is, think that current sliding window mouth has nictation activity；

For correlation coefficient threshold it is considered that, correlation coefficient be more than 0.7 two vectors just substantially may be considered Related it is contemplated that the interference to EEG signals for electro-ocular signal during nictation, the phase between electro-ocular signal and EEG signals during nictation Close coefficient threshold and can be set to 0.9；For acuity parameter threshold, typically could be arranged to 0.3；For spike lasting when Between threshold value, according to wink time be usually the 0.3-0.4 second, therefore, duration threshold can be set to 0.3 second.

Step s104, when nictation activity is detected, judges that user is currently at waking state；

In the sleep cycle of normal person, nictation is an activity specific to lucid interval, therefore, if clear-headed in detection In state procedure, if within certain a period of time, nictation activity has been detected, you can think and be presently at waking state.

With reference to Fig. 4, Fig. 4 is result schematic diagram nictation activity is detected, and in figure solid line and dotted line are respectively through small echo EEG signals after transform reconstruction and electro-ocular signal.On electro-ocular signal, circle has marked the spike that nictation activity is formed, can To find although the nictation amount of activity detecting is less, but false drop rate is extremely low.

Further, in actual applications, in order to avoid flase drop goes out the misrecognition that nictation activity brings, with 30 seconds eyes of a frame As a example the signal of telecommunication, when when at least detecting the nictation activity of 2 or more than 2 in a frame signal, judgement user is currently at Waking state, this makes it possible to detection accuracy is greatly improved.

In one embodiment, waking state detection method in the sleep state analysis of the present invention, can also be from starting to adopt Collect in setting time t after real-time EEG signals and real-time electro-ocular signal, judge that user is constantly in waking state.

Generally, it is contemplated being 10～15 minutes to the normal time for falling asleep of people, when tired/tired out, time for falling asleep is very To shortening, therefore, start to gather the EEG signals of user under the wide-awake state of user it is believed that starting to gather In a setting time section afterwards, user is in waking state, and as embodiment, the time period that the present invention selects is 300 Second (5 minutes), in 300 seconds after that is, user starts to gather EEG signals, all can be judged as waking state.

In order to improve Detection accuracy further, on the basis of the detection nictation activity scheme based on electro-ocular signal, this Invention is directed to the EEG signals of collection, can also be detected by Sample Entropy manner of comparison.

Using technical scheme can be such that

Calculate the Sample Entropy of described EEG signals first, then this Sample Entropy is carried out with precalculated sample entropy threshold Relatively, if described Sample Entropy is more than described sample entropy threshold, judge that user is currently at waking state.

Sample Entropy is a kind of tolerance of time serieses complexity, is widely used in the detection of epilepsy.In sleep cycle 6 stages in, the Sample Entropy highest of the EEG signals in clear-headed stage, the present invention utilize EEG signals Sample Entropy size Lai Judge whether user is waking state, by setting sample entropy threshold, be compared with the Sample Entropy of EEG signals.

Further, in above-mentioned comparison procedure, the selection of sample entropy threshold is also it is critical that a ring.Insane at present The empirical data that the aspects such as the detection of epilepsy obtain, is not appropriate for the accurate judgement to waking state in sleep state analysis.

As in the prior embodiments, in setting time t after starting to gather signal, user is construed as regaining consciousness , therefore, under guaranteeing user's waking state, just can calculate sample using the EEG signals of the user of collection in this period This entropy threshold.

In one embodiment, calculating sample entropy threshold with the following method can be adopted, comprising:

First the EEG signals in setting time are divided into multiple samples, and calculate the sample of each EEG signals sample respectively This entropy, obtains Sample Entropy set；In the process it is assumed that using 30s be a frame, collection 300 seconds EEG signals processed, that Here just there are 10 samples, now can calculate the Sample Entropy set including 10 Sample Entropy.

Then sample entropy threshold is calculated according to described Sample Entropy set；As embodiment, the computing formula of sample entropy threshold Can be such that

$sampen\_thre=\frac{1}{n}\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i+\frac{v}{n}(\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i^2-\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i)$

sampen_val_i=sampen (y [p_start:p_end])

P_start=(i-1) * time_length*fs+1

P_end=t_start+time_length*fs-1

P_end ＜ t fs

In formula, wherein sampen_thre is sample entropy threshold, sampen_val_iFor i-th sample in Sample Entropy set Sample Entropy, sampen is the computing seeking Sample Entropy, and its input y [p_start:p_end] is EEG signals y in pth _ start point Start the part to pth _ end point, time_length is the time span of each sample calculating Sample Entropy, fs is brain The sample rate of the signal of telecommunication, t is to start the setting time after gathering 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, in order to improve recognition accuracy, can be calculated by equation below for parameter v value:

Assume that the set x of Sample Entropy during the clear-headed stage obeys standard normal distribution, in set x, i-th element representation is:

$x_i=\frac{sampen\_{\mathrm{val}}_i-u}{\mathrm{\&sigma;}},i=1,...,n$

Wherein,

Now, x=v

Integration according to Standard Normal Distribution:

Wherein, p (x≤x) represents that the value in the set x of Sample Entropy is less than the probability of x, thus can calculate, with t= As a example 300s, time_length=30s, when parameter v=2.58, the probability that the value of Sample Entropy set x is less than x is 99.5%.

Finally, the Sample Entropy by calculating the EEG signals of present frame is compared with sample entropy threshold, if Sample Entropy is big In sample entropy threshold, you can judge to judge that user is currently at waking state.

It is contemplated that the Sample Entropy of the EEG signals in clear-headed stage is maximum in this step, the sample being calculated based on above-described embodiment This entropy threshold sampen_thre is it is known that the EEG signals that Sample Entropy is more than sampen_thre are judged as that waking state can obtain To compared with high-accuracy.

The scheme of above-described embodiment, on the basis of detection nictation activity, is judged by EEG signals Sample Entropy further, Detection accuracy can be improved further.By to Sample Entropy threshold decision, can avoid further blink activity detection in can The situation of failing to judge that can exist.Taking EEG signals and electro-ocular signal 30s mono- frame as a example, if do not examined in the electro-ocular signal of the same period Measure nictation activity, the function of rechecking with mending-leakage 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 using EEG signals in many cases In waking state test experience, obtain accurate judgement, there is higher accuracy.

The scheme of the embodiment of the present invention, is accurately judged to waking state, can form waking state detector, this inspection If survey device output result is "Yes", that is, judge current state as waking state, if output result is "No", can determine that current State is uncertain state (neither waking state, but nor be considered sleep state).

May be interfered impact with respect to the accuracy rate of traditional method, the technology of the present invention is tighter for part interference The signal of weight, may affect recall rate, but not interfere with accuracy rate, go in sleep state analysis for clear The detection identification of awake state.

With reference to shown in Fig. 5, Fig. 5 be an embodiment sleep state analysis in waking state detecting system structural representation Figure, comprising:

Signal acquisition module 101, for, after user starts sleep procedure, the real-time electro-ocular signal of collection user is with real time EEG signals；

Signal reconstruction module 102, for respectively wavelet decomposition being carried out to described real-time electro-ocular signal and real-time EEG signals, And carry out signal reconstruction and obtain electro-ocular signal and EEG signals according to the wavelet coefficient setting low-frequency range；

Blink detection module 103, for the dependency according to synchronization EEG signals and electro-ocular signal and nictation eye The feature of electrical waveform, detects nictation activity on electro-ocular signal；

State determination module 104, for when nictation activity is detected, judging that user is currently at waking state.

Waking state detecting system and clear-headed shape in the sleep state analysis of the present invention in the sleep state analysis of the present 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 in its advantage all embodiments of waking state detecting system be applied to sleep state analysis, hereby give notice that.

Each technical characteristic of embodiment described above can arbitrarily be combined, for making description succinct, not to above-mentioned reality The all possible combination of each technical characteristic applied in example is all described, as long as however, the combination of these technical characteristics is not deposited In contradiction, all it is considered to be the scope of this specification record.

Embodiment described above only have expressed the several embodiments of the present invention, and its description is more concrete and detailed, but simultaneously Can not 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 Say, without departing from the inventive concept of the premise, some deformation can also be made and improve, these broadly fall into the protection of the present invention Scope.Therefore, the protection domain of patent of the present invention should be defined by claims.

Claims (10)

1. waking state detection method during a kind of sleep state is analyzed is it is characterised in that include:

After user starts sleep procedure, gather the real-time electro-ocular signal of user and real-time EEG signals；

Respectively described real-time electro-ocular signal and real-time EEG signals are carried out with wavelet decomposition, and the wavelet systems according to setting low-frequency range Number carries out signal reconstruction and obtains electro-ocular signal and EEG signals；

Dependency according to synchronization EEG signals and electro-ocular signal and the feature of nictation eye electrical waveform, on electro-ocular signal Detection nictation activity；

When nictation activity is detected, judge that user is currently at waking state.

2. waking state detection method during sleep state according to claim 1 is analyzed is it is characterised in that described basis is same The dependency of one moment EEG signals and electro-ocular signal and the feature of nictation eye electrical waveform, on electro-ocular signal, detection nictation is lived Dynamic step includes:

Intercept described low frequency electro-ocular signal using the sliding window with setting signal amplitude and time span；

Calculate the correlation coefficient of the waveform of electro-ocular signal and synchronization EEG signals in sliding window, eye in sliding window respectively The acuity parameter of electric signal waveform spike and the persistent period of spike；

If described correlation coefficient, acuity parameter and persistent period respectively meet default correlation coefficient threshold, sharp journey Degree parameter threshold and duration threshold, judge that in this sliding window, electro-ocular signal has nictation activity.

3. waking state detection method during sleep state according to claim 2 is analyzed is it is characterised in that described calculating is slided In dynamic window, the step of the acuity parameter of electro-ocular signal waveform spike includes:

Calculate upper area area in sliding window for the electro-ocular signal waveform and lower area area respectively, computing formula is such as Under:

${\mathrm{area}}_{up}=\underset{i=1}{\overset{n}{\&sigma;}}(p_{max}-p_i)$

${\mathrm{area}}_{down}=\underset{i=1}{\overset{n}{\&sigma;}}(p_i-p_{min})$

In formula, p_iFor the electro-ocular signal in sliding window, p_maxFor the maximum of electro-ocular signal in sliding window, p_minFor sliding window The minima of electro-ocular signal, area in mouthful_upRepresent upper area area, area_downRepresent lower area area；

Area according to described upper area area and electro-ocular signal waveform spike described in lower area areal calculation, computing formula As follows:

${\mathrm{blink}}_{area}=\left\{\begin{array}{ccc}{\mathrm{area}}_{up}& if& {\mathrm{area}}_{up}<{\mathrm{area}}_{down}\\ {\mathrm{area}}_{down}& if& {\mathrm{area}}_{up}>{\mathrm{area}}_{down}\end{array}\right.$

In formula, blink_areaRepresent the area of spike, if represents and meets condition；

According to spike areal calculation acuity parameter, computing formula is as follows:

blink_ratio=blink_area/in-blink_area

In formula, blink_ratioRepresent acuity parameter, in-blink_areaRepresent the area of non-peak position.

4. waking state detection method during sleep state according to claim 3 is analyzed is it is characterised in that described calculating is slided In dynamic window, the step of the persistent period of electro-ocular signal waveform spike includes:

Direction according to described upper area area and electro-ocular signal waveform spike described in lower area areal calculation, computing formula As follows:

${\mathrm{blink}}_{direction}=\left\{\begin{array}{ccc}1& if& {\mathrm{area}}_{up}<{\mathrm{area}}_{down}\\ -1& if& {\mathrm{area}}_{up}>{\mathrm{area}}_{down}\end{array}\right.$

In formula, blink_directionRepresent spike direction, be labeled as 1 expression spike downwards, be labeled as -1 expression spike upwards；

When spike is downward, the Local modulus maxima according to spike both sides calculates the persistent period of spike；Or in spike upwards When, the local local minizing point according to spike both sides calculates the persistent period of spike；Computing formula is as follows:

blink_ts=vertex_right-vertex_left,

$s.t.\left\{\begin{array}{cccc}{\mathrm{vertex}}_{right}=right\_\min \_loca& {\mathrm{vertex}}_{left}=left\_\min \_loca& if& {\mathrm{blink}}_{direction}=1\\ {\mathrm{vertex}}_{right}=right\_\max \_loca& {\mathrm{vertex}}_{left}=left\_\max \_loca& if& {\mathrm{blink}}_{direction}=-1\end{array}\right.$

In formula, blink_tsRepresent the persistent period of spike, vertex_rightRepresent the right side summit moment of spike, represent spike In the leftmost vertices moment, right_min_loca represents the local minizing point on the right side of spike, and right_max_loca represents spike The Local modulus maxima on right side, left_min_loca represents the local minizing point on the left of spike, and left_max_loca represents Local modulus maxima on the left of spike, s.t. represents constraints.

5. in sleep state according to claim 4 analysis waking state detection method it is characterised in that described sliding window The amplitude of mouth is 75 microvolts to 300 microvolts, and during described nictation, the correlation coefficient threshold between electro-ocular signal and EEG signals is 0.9, described acuity parameter threshold is 0.3, and described duration threshold is 0.3 second.

6. according to any one of claim 1 to 5 sleep state analysis in waking state detection method it is characterised in that Also include:

In setting time t after starting to gather real-time EEG signals and real-time electro-ocular signal, judge that user is constantly in clear-headed State.

7. during sleep state according to claim 1 is analyzed, waking state detection method is it is characterised in that also include:

Calculate the Sample Entropy of described EEG signals, this Sample Entropy is compared with precalculated sample entropy threshold, if described Sample Entropy is more than described sample entropy threshold, then judge that user is currently at waking state.

8. in sleep state according to claim 7 analysis waking state detection method it is characterised in that described Sample Entropy The computing formula of threshold value is as follows:

$sampen\_thre=\frac{1}{n}\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i+\frac{v}{n}(\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i^2-\underset{i=1}{\overset{n}{\&sigma;}}sampen\_{\mathrm{val}}_i)$

sampen_val_i=sampen (y [p_start:p_end])

P_start=(i-1) * time_length*fs+1

P_end=t_start+time_length*fs-1

P_end ＜ t fs

In formula, wherein sampen_thre is sample entropy threshold, sampen_val_iSample for i-th sample in Sample Entropy set Entropy, sampen is the computing seeking Sample Entropy, and its input y [p_start:p_end] starts in pth _ start point for EEG signals y Part to pth _ end point, time_length is the time span of each sample calculating Sample Entropy, and fs is brain telecommunications Number sample rate, t be start gather EEG signals after setting time, v be setup parameter.

9. in sleep state according to claim 8 analysis waking state detection method it is characterised in that described parameter v Value as follows:

$x_i=\frac{sampen\_{\mathrm{val}}_i-u}{\mathrm{\&sigma;}},i=1,...,n$

Wherein,

X=v

$\mathrm{\&phi;}\left(x\right)={\&integral;}_{-\mathrm{\&infin;}}^x\frac{1}{\sqrt{2\mathrm{\&pi;}}}\exp (-\frac{t^2}{2})dt=p(x\&le;x)$

In formula, x represents Sample Entropy set, x_iRepresent i-th element in Sample Entropy set x, φ (x) represents standard normal distribution letter The integration of number, p (x≤x) represents that the value in the set x of Sample Entropy is less than the probability of x.

10. waking state detecting system during a kind of sleep state is analyzed is it is characterised in that include:

Signal acquisition module, for, after user starts sleep procedure, gathering the real-time electro-ocular signal of user and real-time brain telecommunications Number；

Signal reconstruction module, for respectively wavelet decomposition being carried out to described real-time electro-ocular signal and real-time EEG signals, 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 the dependency according to synchronization EEG signals and electro-ocular signal and eye electrical waveform of blinking Feature, detects nictation activity on electro-ocular signal；

State determination module, for when nictation activity is detected, judging that user is currently at waking state.