CN106333674B - Sleep cycle detection method and system in sleep state analysis - Google Patents
Sleep cycle detection method and system in sleep state analysis Download PDFInfo
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- CN106333674B CN106333674B CN201610839398.7A CN201610839398A CN106333674B CN 106333674 B CN106333674 B CN 106333674B CN 201610839398 A CN201610839398 A CN 201610839398A CN 106333674 B CN106333674 B CN 106333674B
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- 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]
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- 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
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- 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]
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- 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
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- 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
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- 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
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- 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
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- 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
Abstract
The present invention relates to sleep cycle detection method and systems in a kind of analysis of sleep state, the method comprise the steps that carrying out wavelet decomposition to the EEG signals that user generates in sleep procedure, and EEG signals are rebuild according to the wavelet coefficient of preset low-frequency range, obtain low frequency EEG signals；Brain wave is extracted from the low frequency EEG signals of reconstruction；K complex wave is detected from the brain wave according to the wave character of K complex wave, when detecting K complex wave, judges that user is currently at the S2 period of non-dynamic sleep of being sharp-eyed；δ wave, quantity of the statistic mixed-state to δ wave are detected from the brain wave according to δ wave wave character；And S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user is determined according to the quantity of δ wave.Technology of the invention can be interfered influence to avoid EEG signals, can accurately detect which that user is currently at the non-dynamic sleep S2-S4 that is sharp-eyed in period, have higher accuracy rate.
Description
Technical field
The present invention relates to assisting sleep technical fields, more particularly to sleep cycle detection side in a kind of analysis of sleep state
Method and system.
Background technique
There are some equipment that people is helped to fall asleep on the market at present, has improved sleep quality.Sleep state is analyzed
The important means of user's sleep quality is solved, and in the process, it needs to detect user's sleep state, accurately to know use
Whether fall asleep at family.
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).
Traditional detection sleep state is to be identified by training identification model to EEG signals, such as non-eye
When 4 periods of snap-action sleep, by carrying out wavelet function feedback to EEG signals, to four kinds of frequency range (δ waves of EEG signals
Frequency range, θ wave frequency section, α wave frequency section and β wave frequency section) signal, by by these types of eeg signal feature input identification model into
Row identification.
Since a human specific of EEG signals is very strong, and the intensity of brain electricity is very weak that (brain electricity is microvolt rank, and electrocardio is
Millivolt rank), it is easily interfered by outer signals in signal acquisition.Therefore, it is slept using computer to EEG signals
When analysis and research, traditional method is easy the influence that is interfered, it is difficult to accurately detect user and be currently at non-dynamic sleep of being sharp-eyed
Which of S2-S4, accuracy rate lower in period.
Summary of the invention
Based on this, it is necessary in view of the above-mentioned problems, providing sleep cycle detection method in a kind of analysis of sleep state and being
System effectively improves the accuracy rate of sleep state identification.
Sleep cycle detection method in a kind of analysis of sleep state, comprising:
Wavelet decomposition is carried out to the EEG signals that user generates in sleep procedure, and according to the small echo of preset low-frequency range
Coefficient reconstruction EEG signals obtain low frequency EEG signals；
Brain wave is extracted from the low frequency EEG signals of reconstruction；
K complex wave is detected from the brain wave according to the wave character of K complex wave, when detecting K complex wave, judgement
User is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
δ wave, quantity of the statistic mixed-state to δ wave are detected from the brain wave according to δ wave wave character；And according to δ wave
Quantity determines S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user.
Sleep cycle detection system in a kind of analysis of sleep state, comprising:
Low frequency signal obtains module, and the EEG signals for generating in sleep procedure to user carry out wavelet decomposition, and
EEG signals are rebuild according to the wavelet coefficient of preset low-frequency range, obtain low frequency EEG signals；
Brain wave extraction module, for extracting brain wave from the low frequency EEG signals of reconstruction；
S2 cycle detection module detects K complex wave for the wave character according to K complex wave from the brain wave, when
When detecting K complex wave, judge that user is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
S3-S4 cycle detection module, for detecting δ wave from the brain wave according to δ wave wave character, statistic mixed-state is arrived
The quantity of δ wave；And S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user is determined according to the quantity of δ wave.
Sleep cycle detection method and system in above-mentioned sleep state analysis carry out wavelet decomposition, and root to EEG signals
EEG signals are rebuild according to the wavelet coefficient of low-frequency range, according to K complex wave in low frequency part, determine S2 weeks of non-dynamic sleep of being sharp-eyed
Phase, then detect δ wave and statistical magnitude in low frequency part again, and by the quantity determine the S3 of the non-dynamic sleep of being sharp-eyed of user with
The S4 period.The program can be interfered influence to avoid EEG signals, can accurately detect user be currently at it is non-be sharp-eyed it is dynamic
It sleeps which of S2-S4 in period, there is higher accuracy rate.
Detailed description of the invention
Fig. 1 is the flow chart of sleep cycle detection method in the sleep state analysis of one embodiment；
Fig. 2 is the waveform diagram of K complex wave；
Fig. 3 is the waveform diagram of δ wave；
Fig. 4 is the waveform diagram for being judged as brain wave；
Fig. 5 is the testing result schematic diagram of K complex wave on the EEG signals of non-dynamic sleep S2 phase of being sharp-eyed；
Fig. 6 is the testing result schematic diagram of δ wave on the EEG signals of non-dynamic sleep S3 phase of being sharp-eyed；
Fig. 7 is the flow chart in the S2-S4 period of the non-dynamic sleep of being sharp-eyed of detection；
Fig. 8 is sleep cycle detection system structure in the sleep state analysis of one embodiment.
Specific embodiment
The embodiment of sleep cycle 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 sleep cycle detection method in the sleep state analysis of one embodiment,
Include:
S101 carries out wavelet decomposition to the EEG signals that user generates in sleep procedure, and according to preset low-frequency range
Wavelet coefficient rebuild EEG signals, obtain low frequency EEG signals；
Above-mentioned steps are carrying out in the analysis of the sleep states such as assisting sleep user, are wearing related transducer by user and set
EEG signals standby, that acquisition user generates in sleep procedure can be that a frame is adopted with 30s when acquiring EEG signals
Collection carries out subsequent processing to every frame EEG signals.
Clinically, the appearance of K complex wave is to enter dormant typical characteristics, and the frequency of K complex wave is lower.Therefore
EEG signals can be analyzed in low frequency part, to exclude High-frequency Interference, here, carrying out small echo to EEG signals first
It decomposes, and EEG signals is rebuild according to the wavelet coefficient of low-frequency range, then extract brain wave on the low frequency EEG signals of reconstruction；
Above-mentioned preset low-frequency range at least chooses the range of 0~2Hz frequency range, K complex wave is detected in the frequency range.In addition, if examining
Consider it is subsequent be also required to handle δ wave, selection 0~4Hz frequency range can be fixed.
S102 extracts brain wave from the low frequency EEG signals of reconstruction；
It is that brain wave is extracted from the low frequency EEG signals of reconstruction according to wave character in this step.Referring to figs. 2 and 3
Shown, Fig. 2 is the waveform diagram of K complex wave, and Fig. 3 is the waveform diagram of δ wave；It can be seen that K complex wave is a kind of with Gao Bo
The compound two-phase of width or the slow wave of multiphase, duration are about 1~2s, and wave amplitude is about 200~300 microvolts (uV)；δ wave activity
Frequency be 1~3Hz, wave amplitude is about 20~200 microvolts.Here, needing after by wavelet decomposition and low-frequency reconfiguration from low frequency brain
Brain wave is extracted in electric signal.
As one embodiment, there is compound two-phase or multiphase on waveform in conjunction with K complex wave and wave amplitude is higher
Feature is simplified to have high wave crest, deep trough, the brain wave that the duration is 1~2s or so herein.δ wave has wave amplitude
Feature higher, frequency is lower is simplified to the brain wave for having high amplitude, the duration is 0.5s~2s or so.
In one embodiment, the process that brain wave is extracted from low frequency EEG signals, may include steps of:
Local minizing point is found out from the waveform of low frequency EEG signals, and is marked as trough；By two neighboring wave
Maximum Local modulus maxima is labeled as wave crest between paddy；Sentenced according to the height of the left right trough pair of trough-wave crest-each on waveform
Disconnected brain wave out；
Refering to what is shown in Fig. 4, Fig. 4 is the waveform diagram for being judged as brain wave, in the judgment process, each left side is calculated first
The height of the right trough pair of trough-wave crest-is then divided into following three kinds of modes:
(1) if the difference in height of two troughs in left and right is less than preset difference threshold, which is judged to a brain wave；
Specifically, as shown in Fig. 4 (a), if being judged to a brain wave when difference in height of two troughs is less than threshold value (such as 10 microvolts).
(2) if the difference in height of two troughs in left and right is greater than difference threshold, and left trough-ascending branch wave crest height is less than wave
The waveform is then judged to a brain wave by the half of the right trough of peak-decent；Specifically, as shown in Fig. 4 (b), if two waves
The difference in height of paddy is greater than threshold value, and the height of left trough-wave crest (ascending branch) is less than the half of the right trough of wave crest-(decent),
Then it is judged to a brain wave.
(3) if the difference in height of two troughs in left and right is greater than difference threshold, and the height of ascending branch is greater than the half of decent,
It then abandons right trough and finds next second trough on waveform, re-start judgement；Specifically, such as Fig. 4 (c) institute
Show, if the difference in height of two troughs is greater than threshold value, and the height of ascending branch is greater than the half of decent, then abandons right trough simultaneously
Next second trough is found, calculating and judgement are re-started.
In the scheme of above-described embodiment, in the detection K complex wave stage, preset low-frequency range can be chosen into 0~2Hz frequency range
Range, K complex wave is detected in the frequency range.The range that preset low-frequency range can be chosen to 0~4Hz frequency range, in the frequency
δ wave is detected in section.
S103 detects K complex wave according to the wave character of K complex wave from the brain wave, when detecting K complex wave
When, judge that user is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
As described above, K complex wave is a kind of with the compound two-phase of high wave amplitude or the slow wave of multiphase, duration are about
1~2s, wave amplitude are about 200~300 microvolts.After extracting brain wave on the EEG signals of low-frequency range, according to the wave of K complex wave
Shape feature detects K complex wave.
In this step, the amplitude threshold of K complex wave can first be set, in conjunction with extraction according to the wave character of K complex wave
Brain wave sample rate, from brain wave detect K complex wave.
In one embodiment, the step of K complex wave is detected from the brain wave, may include following formula:
In formula, ptrough_rightWith ptrough_leftThe coordinate of left and right trough data point is respectively indicated, fs is the sampling of brain wave
Rate,WithEEG signals and electro-ocular signal are respectively indicated in section
[ptrough_right,ptrough_left] on maximum value subtract the value of minimum value, peak_threEEGWith trough_threEEGTable respectively
Show the wave crest threshold value and trough threshold value of K complex wave, wkIndicate that K complex wave, true indicate that judging result is that very, if indicates satisfaction
Condition.
In above-described embodiment, wave crest threshold value and the trough threshold value of K complex wave can take+100 microvolts and -100 micro- respectively
Volt.
Due to being to carry out detection on non-primary EEG signals on the EEG signals after wavelet decomposition, K complex wave
Amplitude threshold can suitably be reduced compared with clinical criteria.The threshold value peak_thre of the wave crest of K complex waveEEGWith the threshold value of trough
trough_threEEGIt can be set to positive and negative 100 microvolt；Above-mentioned threshold value setting can be detected preferably by verifying
Effect.
As one embodiment, for the above-mentioned K complex wave detected, in order to avoid being interfered caused by electro-ocular signal,
In the time window for detecting K complex wave, the amplitude of electro-ocular signal is detected, when the amplitude of electro-ocular signal is more than default width
It spends threshold value (such as 75 microvolts), then determines that the K complex wave detected belongs to pseudo- positive (false positive) as a result, not being
Real K complex wave, abandons the testing result, excludes the interference of eye electricity artefact.
Refering to what is shown in Fig. 5, Fig. 5 is the testing result schematic diagram of K complex wave on the EEG signals of non-dynamic sleep S2 phase of being sharp-eyed；
The biggish waveform of fluctuation is original EEG signals in Fig. 5 (a), and fluctuating lesser waveform is the EEG signals that low-frequency range is rebuild；Fig. 5
(b) waveform portion is the EEG signals that low-frequency range is rebuild in, and encircled portion is the K complex wave of detection.
S104 detects δ wave, quantity of the statistic mixed-state to δ wave according to δ wave wave character from the brain wave；And according to δ
The quantity of wave determines S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user.
In this step, after detecting K complex wave, shows that user has come into sleep state, δ can be detected at this time
Wave judges S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user according to the quantity of δ wave.
As described above, δ wave is that one kind has wave amplitude higher, the lower feature of frequency, the duration is 0.5s~2s or so
Brain wave, after extracting brain wave on the EEG signals of low-frequency range, δ wave is detected according to the wave characteristics of δ wave.
In one embodiment, the step of δ wave is detected from the brain wave, may include following formula:
In formula, ptrough_rightWith ptrough_leftThe coordinate of left and right trough data point is respectively indicated, fs is adopting for brain wave
Sample rate,WithEEG signals and electro-ocular signal are respectively indicated in section
[ptrough_right,ptrough_left] on maximum value subtract the value of minimum value, height_threEEGIndicate the amplitude threshold of δ wave；
wδIndicate that δ wave, true indicate that judging result is condition true, that if expression meets.
In above-described embodiment, the amplitude threshold of δ wave can take 75-150 microvolt.
Due to being to carry out detection on non-primary EEG signals on the EEG signals after wavelet decomposition, the width of δ wave
Degree threshold value can suitably be reduced compared with clinical criteria, the amplitude threshold height_thre of δ waveEEGFor 75 to 150 microvolts；Above-mentioned threshold value is set
It sets by verifying, preferable detection effect can be obtained.
As one embodiment, the above-mentioned δ wave detected is being detected in order to avoid interfering caused by electro-ocular signal
Into the time window of δ wave, the amplitude of electro-ocular signal is detected, when the amplitude of electro-ocular signal is more than predetermined amplitude threshold value
(such as 75 microvolts) then determine that the δ wave detected belongs to pseudo- positive (false positive) as a result, being not real δ wave,
The testing result is abandoned, the interference of eye electricity artefact is excluded.
Refering to what is shown in Fig. 6, Fig. 6 is the testing result schematic diagram of δ wave on the EEG signals of non-dynamic sleep S3 phase of being sharp-eyed；Fig. 6
(a) the biggish waveform of fluctuation is original EEG signals in, and fluctuating lesser waveform is the EEG signals that low-frequency range is rebuild；Fig. 6 (b)
Middle waveform portion is the EEG signals that low-frequency range is rebuild, and encircled portion is the δ wave of detection.
Method for determining S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user according to the quantity of δ wave, mainly foundation
The quantity of δ wave is detected to determine, specific determination can be according to following judgment principle:
When δ wave number amount belongs to [fL(m), fH(m)] when, determine that user is currently at the S3 period of non-dynamic sleep of being sharp-eyed；Work as δ
Wave number amount is more than or equal to f0(m) when, determine user be currently at non-dynamic sleep of being sharp-eyed in the S4 period；
fL(m)=m/t × pL；
fL(m)=m/t × pH；
f0(m)=fH(m)+1；
Wherein, the length for detecting the time window of brain wave is m, and t is the mean value of δ wave duration, (pL,pH) it is that δ wave accounts for
The time range of EEG signals.
The duration of δ wave is generally 0.5~2 second, mostly in 1.5s or so, therefore can be with value for 1.5 seconds, EEG signals
The time window length of detection is 30 seconds, i.e., was analyzed with 30 seconds for a frame.Then in the S3 phase, δ wave account for EEG signals when
Between be 20%~50%, i.e., 6 seconds~15 seconds, it is possible thereby to calculate the quantity f of δ waveL(m)=4, fL(m)=9, f0(m)=10.
Sleep cycle detection method in the sleep state analysis of the embodiment of the present invention, accuracy with higher.Experiment card
Bright, in the detection in the S2-S4 period for the non-dynamic sleep of being sharp-eyed that many cases are carried out using EEG signals, the accuracy rate of detection reaches
95% or more.
Refering to what is shown in Fig. 7, Fig. 7 is the flow chart in the S2-S4 period of the non-dynamic sleep of being sharp-eyed of detection, include the following steps:
S1: acquisition EEG signals (contain electro-ocular signal)；
S2: EEG signals wavelet decomposition；
S3: it rebuilds low-band signal (0-2Hz)；
S4: brain wave is detected from low-band signal；
S5: K complex wave is detected in brain wave；
S6: the eye electrical interference in removal K complex wave detection；
S7: K complex wave is detected? if so, being determined as the S2 period, s8 is executed, otherwise continues to judge；
S8: it rebuilds low-band signal (0-4Hz)；
S9: brain wave is detected from low-band signal；
S10: δ wave is detected in brain wave；
S11: the eye electrical interference in removal δ wave detection；
S12: statistics δ wave number amount, threshold decision；Quantity belongs to [4,9], is determined as the S3 period；Quantity is more than or equal to 10, sentences
It is set to the S4 period.
Refering to what is shown in Fig. 8, Fig. 8 is sleep cycle detection system structural representation in the sleep state analysis of one embodiment
Figure, comprising:
Low frequency signal obtains module 101, and the EEG signals for generating in sleep procedure to user carry out wavelet decomposition,
And EEG signals are rebuild according to the wavelet coefficient of preset low-frequency range, obtain low frequency EEG signals；
Brain wave extraction module 102, for extracting brain wave from the low frequency EEG signals of reconstruction；
S2 cycle detection module 103 detects K complex wave for the wave character according to K complex wave from the brain wave,
When detecting K complex wave, judge that user is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
S3-S4 cycle detection module 104, for detecting δ wave, statistics inspection from the brain wave according to δ wave wave character
Measure the quantity of δ wave；And S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user is determined according to the quantity of δ wave.
It sleeps week in sleep cycle detection system and sleep state of the invention analysis in sleep state analysis of the invention
Phase detection method corresponds, the technical characteristic that the embodiment of sleep cycle detection method illustrates in the analysis of above-mentioned sleep state
And its advantages suitable for sleep state analysis sleep cycle 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. sleep cycle detection method in a kind of sleep state analysis characterized by comprising
Wavelet decomposition is carried out to the EEG signals that user generates in sleep procedure, and according to the wavelet coefficient of preset low-frequency range
EEG signals are rebuild, low frequency EEG signals are obtained；
Brain wave is extracted from the low frequency EEG signals of reconstruction；
K complex wave is detected from the brain wave according to the wave character of K complex wave and judges user when detecting K complex wave
It is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
δ wave, quantity of the statistic mixed-state to δ wave are detected from the brain wave according to δ wave wave character；And according to the quantity of δ wave
Determine S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user；
The step of brain wave is extracted on the low frequency EEG signals from reconstruction includes, from the waveform of low frequency EEG signals
Local minizing point is found out, and is marked as trough；Local modulus maxima maximum between two neighboring trough is labeled as
Wave crest；Brain wave is judged according to the height of the left right trough pair of trough-wave crest-each on waveform；
The step of height according to the left right trough pair of trough-wave crest-each on waveform judges brain wave includes calculating often
The height of a left right trough pair of trough-wave crest-；If the difference in height of two troughs in left and right is less than preset difference threshold, should
Waveform is judged to a brain wave；If the difference in height of two troughs in left and right is greater than difference threshold, and left trough-ascending branch wave crest height
Degree is less than the half of the right trough of wave crest-decent, then the waveform is judged to a brain wave；If the difference in height of two troughs in left and right
Greater than difference threshold, and the height of ascending branch is greater than the half of decent, then abandons right trough and find on waveform following
Second trough, re-start judgement.
2. sleep cycle detection method in sleep state analysis according to claim 1, which is characterized in that described according to K
It includes following formula that the wave character of complex wave detects the step of K complex wave from the brain wave:
In formula, ptrough_rightWith ptrough_leftThe coordinate of left and right trough data point is respectively indicated, fs is the sample rate of brain wave,Indicate electro-ocular signal in section [ptrough_right,ptrough_left] on maximum value subtract minimum value
Value, height_threEOGIndicate predetermined amplitude threshold value,WithRespectively indicate the crest value and wave of brain wave
Valley, peak_threEEGWith trough_threEEGRespectively indicate the wave crest threshold value and trough threshold value of K complex wave, wkIndicate that K is comprehensive
Multiplex, true indicate that judging result is condition true, that if expression meets.
3. sleep cycle detection method in sleep state analysis according to claim 1, which is characterized in that described according to δ
It includes following formula that wave wave character detects the step of δ wave from the brain wave:
In formula, ptrough_rightWith ptrough_leftThe coordinate of left and right trough data point is respectively indicated, fs is the sample rate of brain wave,WithEEG signals and electro-ocular signal are respectively indicated in section
[ptrough_right,ptrough_left] on maximum value subtract the value of minimum value, height_threEOGIndicate predetermined amplitude threshold value,
height_threEEGIndicate the amplitude threshold of δ wave；wδIndicate that δ wave, true indicate that judging result is item true, that if expression meets
Part.
4. sleep cycle detection method in sleep state analysis according to any one of claims 1 to 3, which is characterized in that
When detecting K complex wave, further includes:
In the time window for detecting K complex wave, the amplitude of electro-ocular signal is detected, when the amplitude of electro-ocular signal is more than
Predetermined amplitude threshold value then determines that the K complex wave detected belongs to pseudo- positive findings；
Or
Before quantity of the statistic mixed-state to δ wave the step of, further includes:
In the time window for detecting δ wave, the amplitude of electro-ocular signal is detected, when the amplitude of electro-ocular signal is more than default
Amplitude threshold then determines to detect that δ wave belongs to pseudo- positive findings.
5. sleep cycle detection method in sleep state analysis according to claim 4, which is characterized in that the default width
Degree threshold value is 75 microvolts.
6. sleep cycle detection method in sleep state analysis according to claim 2, which is characterized in that the K is comprehensive
The wave crest threshold value and trough threshold value of wave are respectively+100 microvolts and -100 microvolts.
7. sleep cycle detection method in sleep state analysis according to claim 3, which is characterized in that the δ wave
Amplitude threshold is 75-150 microvolt.
8. sleep cycle detection method in sleep state analysis according to claim 1, which is characterized in that described according to δ
The quantity of wave determines that the step of S3 and S4 period of the non-dynamic sleep of being sharp-eyed of user includes:
When δ wave number amount belongs to [fL(m), fH(m)] when, determine that user is currently at the S3 period of non-dynamic sleep of being sharp-eyed；When δ wave number
Amount is more than or equal to f0(m) when, determine user be currently at non-dynamic sleep of being sharp-eyed in the S4 period；
fL(m)=m/t × pL；
fH(m)=m/t × pH；
f0(m)=fH(m)+1；
Wherein, the length for detecting the time window of brain wave is m, and t is the mean value of δ wave duration, (pL,pH) it is that δ wave accounts for brain electricity
The time range of signal.
9. sleep cycle detection system in a kind of sleep state analysis characterized by comprising
Low frequency signal obtains module, the EEG signals progress wavelet decomposition for generating in sleep procedure to user, and according to
The wavelet coefficient of preset low-frequency range rebuilds EEG signals, obtains low frequency EEG signals；
Brain wave extraction module, for extracting brain wave from the low frequency EEG signals of reconstruction；
S2 cycle detection module detects K complex wave for the wave character according to K complex wave from the brain wave, works as detection
When to K complex wave, judge that user is currently at the S2 period of non-dynamic sleep of being sharp-eyed；
S3-S4 cycle detection module, for detecting δ wave, statistic mixed-state to δ wave from the brain wave according to δ wave wave character
Quantity；And S3 the and S4 period of the non-dynamic sleep of being sharp-eyed of user is determined according to the quantity of δ wave；
The brain wave extraction module is also used to, and local minizing point is found out from the waveform of low frequency EEG signals, and marked
It is denoted as trough；Local modulus maxima maximum between two neighboring trough is labeled as wave crest；According to left trough-each on waveform
The height of the right trough pair of wave crest-judges brain wave；
The brain wave extraction module is also used to, and calculates the height of each left right trough pair of trough-wave crest-；If two waves in left and right
The difference in height of paddy is less than preset difference threshold, then the waveform is judged to a brain wave；If the difference in height of two troughs in left and right
Greater than difference threshold, and left trough-ascending branch wave crest height is less than the half of the right trough of wave crest-decent, then by the waveform
It is judged to a brain wave；If the difference in height of two troughs in left and right is greater than difference threshold, and the height of ascending branch is greater than decent
Half then abandons right trough and finds next second trough on waveform, re-starts judgement.
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