CN106333679A - Electroencephalogram preprocessing method and electroencephalogram preprocessing system in sleep state analysis - Google Patents

Electroencephalogram preprocessing method and electroencephalogram preprocessing system in sleep state analysis Download PDF

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CN106333679A
CN106333679A CN201610840503.9A CN201610840503A CN106333679A CN 106333679 A CN106333679 A CN 106333679A CN 201610840503 A CN201610840503 A CN 201610840503A CN 106333679 A CN106333679 A CN 106333679A
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CN106333679B (en
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赵巍
胡静
韩志
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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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]
    • A—HUMAN NECESSITIES
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    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
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    • A—HUMAN NECESSITIES
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    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
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    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
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    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • 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
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

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Abstract

The invention relates to an electroencephalogram preprocessing method and an electroencephalogram preprocessing system in sleep state analysis. The method comprises the following steps of: acquiring an original electroencephalogram generated during the sleep process of a user; performing median filtering for the original electroencephalogram according to a preset window length of median filtering, and filtering baseline drift; self-adaptively adjusting the window length of the median filtering according to the frequency and an amplitude of the filtered electroencephalogram until an limit value point of the frequency of the filtered electroencephalogram is disposed in a set frequency band and an absolute value of an average value of the amplitude of the electroencephalogram is minimum; and outputting the electroencephalogram with the baseline drift being filtered. According to the technical scheme, on the basis of median filtering the baseline drift and remaining useful information of the electroencephalogram, the frequency of the filtered signal can better meet the clinical requirement by self-adaptively adjusting the window length of the median filtering, and the average value is minimal.

Description

EEG signals preprocess method in sleep state analysis and system
Technical field
The present invention relates to assisting sleep technical field, the EEG signals in more particularly to a kind of sleep state analysis are located in advance Reason method and system.
Background technology
In sleep, human body has carried out the process self loosened and recover, and therefore good sleep is to maintain healthy A primary condition；But due to the reason such as operating pressure is big, daily life system is irregular, result in the sleep matter of part population Amount is not good enough, shows as insomnia, midnight wakes up with a start.
There are some equipment at present on the market to help people to fall asleep, improved sleep quality.For example specific sleep a certain Pass through the manual intervention such as sound, optical signal, it is to avoid wake user etc. under the state of sleeping soundly under dormancy state.Assisting sleep is set For standby, in order to be really achieved the purpose improving user's sleep quality, the sleep state of correct identifying user is extremely important 's.
And want the sleep state of identifying user, presently mainly utilize polysomnogram (polysomnography, psg), Also known as sleep electroencephalogram, polysomnogram is analyzed to sleep using multiple vital signs, in these sign, brain electricity It is in core status；Using brain wave 4 species rhythm: δ ripple (1-3hz), θ ripple (4-7hz), α ripple (8-12hz), β ripple (14-30hz) Carry out correlation analysiss, due to there is the situation of baseline drift, the maximum frequency range of EEG signals energy with its 1 second in concussion time Several and not quite identical, the energy that baseline drift result in EEG signals low-frequency range is far above normal level, and computer is analyzed The frequency spectrum of sleep cerebral electricity signal interferes.So before calculating the frequency spectrum of EEG signals, needing EEG signals are carried out pre- Process, to exclude the interference that baseline drift brings, and because EEG signals belong to stochastic signal, electrocardiosignal is periodic signal, So it is adaptable to the signal processing method of electrocardiosignal is difficult to use in the processing procedure of EEG signals.
Traditional EEG signals preprocess method, typically using going trend/go Mean Method or the method based on frequency domain (such as Fft, wavelet transformation etc.), remove baseline lifting.But go trend/go this method of Mean Method that the process of EEG signals is imitated Fruit is simultaneously inconspicuous；Based on the method for frequency domain, because the frequency of baseline is generally very low, there is overlapping part with the frequency range of brain wave, After process, easily distortion in signal, does not meet clinical needs.
Content of the invention
Based on this it is necessary to be directed to the problems referred to above, provide the EEG signals preprocess method in a kind of sleep state analysis And system, effectively improve the extraction efficiency to signal characteristic.
A kind of EEG signals preprocess method in sleep state analysis, comprising:
The original EEG signals that collection user produces in sleep procedure；
According to the length of window of default medium filtering, medium filtering is carried out to original EEG signals, filters baseline drift；
According to the filtered EEG signals frequency and amplitude length of window self-adaptative adjustment to medium filtering, until filtering The extreme point of EEG signals frequency afterwards is located in setting frequency range, and the mean absolute value of EEG signals amplitude is minimum；
Output filters the EEG signals of baseline drift.
A kind of EEG signals pretreatment system in sleep state analysis, comprising:
Electroencephalogramsignal signal acquisition module, for gathering the original EEG signals that user produces in sleep procedure；
Original EEG signals, according to the length of window of default medium filtering, are carried out medium filtering by medium filtering module, Filter baseline drift；
Length of window adjusting module, for long to the window of medium filtering according to filtered EEG signals frequency and amplitude Degree self-adaptative adjustment, until the extreme point of filtered EEG signals frequency is located in setting frequency range, and EEG signals amplitude Mean absolute value is minimum；
Signal output module, filters the EEG signals of baseline drift for output.
EEG signals preprocess method in above-mentioned sleep state analysis and system, the original EEG signals of collection user are entered Row medium filtering, filters baseline drift, adaptive to the length of window of medium filtering according to the EEG signals frequency obtaining and amplitude Should adjust, until the extreme point of filtered EEG signals frequency is located in setting frequency range, and the average of EEG signals amplitude is exhausted When minimum to value, export EEG signals；Filter the basis of baseline drift, the useful information of reservation EEG signals in medium filtering On, by the length of window of the medium filtering of self-adaptative adjustment so that filtered signal frequency more conforms to clinical needs, and Average is minimum.
Brief description
Fig. 1 is the flow chart of the EEG signals preprocess method in the sleep state analysis of an embodiment；
Fig. 2 is the frame diagram of the medium filtering of self-adaptative adjustment；
Fig. 3 is the EEG signals effect contrast figure filtering before and after baseline drift of an example；
Fig. 4 is the spectrogram of the EEG signals filtering after baseline drift of an example；
Fig. 5 is the EEG signals pretreatment system structural representation in the sleep state analysis of an embodiment.
Specific embodiment
The reality of the EEG signals preprocess method in the sleep state analysis of the elaboration present invention and system below in conjunction with the accompanying drawings Apply example.
With reference to shown in Fig. 1, Fig. 1 is the flow chart of the EEG signals preprocess method in the sleep state analysis of the present invention, Including:
S101, the original EEG signals that collection user produces in sleep procedure；
In this step, as when assisting sleep is carried out to user, related transducer equipment is worn by user, detect user EEG signals, generally, can receive EEG signals 30s after, start to carry out pretreatment to EEG signals.
Original EEG signals, according to the length of window of default medium filtering, are carried out medium filtering, filter baseline by s102 Drift；
Because the key point of median filtering algorithm is to select suitable length of window, length of window is long, does not reach suppression The target of baseline drift processed, and amount of calculation is excessive；Length of window is too short, then EEG signals can gross distortion；Here, first with pre- If length of window medium filtering is carried out to EEG signals.
S103, according to the filtered EEG signals frequency and amplitude length of window self-adaptative adjustment to medium filtering, directly Extreme point to filtered EEG signals frequency is located in setting frequency range, and the mean absolute value of EEG signals amplitude is minimum；
Due to employing medium filtering, and EEG signals are the stochastic signals of aperiodicity, non-stationary, are directed to brain telecommunications Number different frequency sections filtering when, find optimum length of window, to obtain more preferable filter effect.
For this reason, the present invention takes adaptive method to find optimum length of window, according to filtered EEG signals frequency The rate and amplitude length of window to medium filtering, be provided with optimum length of window condition:
Condition one: the maximum point filtering the frequency of the EEG signals after baseline drift is located at setting frequency range；
Condition two: the absolute value filtering the amplitude average of the EEG signals after baseline drift is minimum.
For example, it is contemplated that being α ripple to the brain many ripples of electricity when preparing Sleep stages, and the average of ideal signal is close to 0, because This, the maximum point filtering the frequency of the EEG signals after baseline drift is located at α wave frequency section (8～13hz), the width of EEG signals The absolute value of value average is close to 0uv.
In one embodiment, the self-adaptative adjustment process of step s103, comprises the steps:
(1) set up Optimization goal function:
n = m i n | 1 m σ m y i | , i = 0 , 1 , 2 , ...
Constraints:
s.t.max psd(y)∈[fl,fh]
In formula, n represents length of window, and min represents and minimize, y represents sampled point amplitude, and i represents sampled point sequence number, m Represent the length of EEG signals, psd is the power spectrum of EEG signals, flRepresent lower-frequency limit, fhRepresent upper frequency limit, max Represent maximizing；
As an embodiment, during self-adaptative adjustment, the self-adaptative adjustment interval of the length of window of medium filtering can To be set to k fs；Wherein, k represents constant, and fs represents sample frequency；Generally, the span of described k can be [0.01,0.5], adjusts length of window in this interval.
In above-described embodiment, prepare Sleep stages when α ripple wave band, described fl=8hz, fh=13hz.
(2) solve described Optimization goal function and obtain optimal solution, determine the length of window of medium filtering according to optimal solution；? In practical application, Optimization goal function described in grid software test Algorithm for Solving can be adopted.
For the formula of medium filtering, can be expressed as follows:
xi'=xi-bi,bi=med { xi-n,xi-n+1..., xi,xi+n-1,xi+n, i=1 ..., m
In formula, x represents original EEG signals, and x ' represents the EEG signals after removing baseline drift, biRepresent and filtered by intermediate value The baseline that ripple extracts, m represents the length of EEG signals, med represent the element in window is sorted by size after take intermediate value Computing, n represents the length of window of medium filtering.
S104, output filters the EEG signals of baseline drift.
In this step, the signal of medium filtering after the length of window of self-adaptative adjustment is exported, in intermediate value Filter baseline drift, retain EEG signals useful information on the basis of, by the window of the medium filtering of self-adaptative adjustment Mouth length is so that filtered signal frequency more conforms to clinical needs, and average is minimum.
With reference to shown in Fig. 2, Fig. 2 is the frame diagram of the medium filtering of self-adaptative adjustment, by gathering the original brain electricity of user Signal, input median filter is filtered, and starts to adopt default length of window during filtering, filters baseline drift, then certainly Adapt to adjustment length of window, the EEG signals after baseline drift are filtered by test, meet above-mentioned condition one and condition two Under the conditions of, search the length of window of optimum, and corresponding EEG signals are exported, complete EEG signals pretreatment, EEG signals For in sleep state analysis.
In an example, when sample rate is 512hz, using 0.05 sampling rate, according to public q=f × 0.05, f is to adopt Sample frequency, q is length of window, and can be calculated length of window is 25 points, removes baseline drift by medium filtering, filters After baseline drift, the center of oscillation of EEG signals is held in 0uv.With reference to shown in Fig. 3, Fig. 3 is filtering of an example EEG signals effect contrast figure before and after baseline drift, transverse axis is the time, and the longitudinal axis is amplitude, in figure be 1. filter baseline drift before Original figure, be 2. figure after filtering baseline drift it is seen that waveform is more steady after filtering baseline drift, Fig. 4 is one The spectrogram of the EEG signals filtering after baseline drift of example.
Clinically, due to there are the feelings of baseline drift in the number of times that the frequency of brain wave rhythm was shaken in 1 second equal to it EEG signals are converted to after frequency domain from time domain using computer by condition, the maximum frequency range of EEG signals energy with its 1 second in shake The number of times that swings is simultaneously not quite identical, according to one section of EEG signals during waking state and its Spectrum Relationship, during waking state Brain wave rhythm should be based on α ripple and β ripple, that is, the energy of α ripple and β ripple is higher.But baseline drift leads to this frequency range EEG signals Low-frequency range energy be far above normal level, to computer analyze sleep cerebral electricity signal frequency spectrum interfere.By this EEG signals, before calculating the frequency spectrum of EEG signals, are carried out pretreatment, are brought with excluding baseline drift by the technology of bright offer Interference, identify EEG signals type, be easy to subsequent analysis process.
With reference to shown in Fig. 5, Fig. 5 is that the EEG signals pretreatment system structure in the sleep state analysis of an embodiment is shown It is intended to, comprising:
Electroencephalogramsignal signal acquisition module 101, for gathering the original EEG signals that user produces in sleep procedure；
Original EEG signals, according to the length of window of default medium filtering, are carried out intermediate value filter by medium filtering module 102 Ripple, filters baseline drift；
Length of window adjusting module 103, for the window to medium filtering according to filtered EEG signals frequency and amplitude Mouth length self-adaptative adjustment, until the extreme point of filtered EEG signals frequency is located in setting frequency range, and EEG signals width The mean absolute value of degree is minimum；
Signal output module 104, filters the EEG signals of baseline drift for output.
During the sleep state of the EEG signals pretreatment system in the sleep state analysis of the present invention and the present invention is analyzed EEG signals preprocess method corresponds, and the embodiment of the EEG signals preprocess method in the analysis of above-mentioned sleep state is explained The technical characteristic stated and its advantage all in the embodiment of the EEG signals pretreatment system 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. the EEG signals preprocess method in a kind of sleep state analysis is it is characterised in that include:
The original EEG signals that collection user produces in sleep procedure；
According to the length of window of default medium filtering, medium filtering is carried out to original EEG signals, filters baseline drift；
According to the filtered EEG signals frequency and amplitude length of window self-adaptative adjustment to medium filtering, until filtered The extreme point of EEG signals frequency is located in setting frequency range, and the mean absolute value of EEG signals amplitude is minimum；
Output filters the EEG signals of baseline drift.
2. the EEG signals preprocess method in sleep state analysis according to claim 1 is it is characterised in that described set Determine frequency range α wave frequency section.
3. the EEG signals preprocess method in sleep state according to claim 2 analysis is it is characterised in that according to filter EEG signals frequency after the ripple and amplitude length of window self-adaptative adjustment to medium filtering, until filtered EEG signals frequency The extreme point of rate is located in setting frequency range, and the step of the mean absolute value minimum of EEG signals amplitude includes:
Set up Optimization goal function:
n = m i n | 1 m σ m y i | , i = 0 , 1 , 2 , ...
s.t.max psd(y)∈[fl,fh]
In formula, n represents length of window, and min represents and minimize, y represents sampled point amplitude, and i represents sampled point sequence number, and m represents Represent the length of EEG signals, psd is the power spectrum of EEG signals, flRepresent lower-frequency limit, fhRepresent upper frequency limit, max represents Maximizing；
Solve described Optimization goal function and obtain optimal solution, determine the length of window of medium filtering according to optimal solution.
4. the EEG signals preprocess method in sleep state analysis according to claim 3 is it is characterised in that adopt net Lattice testing algorithm solves described Optimization goal function.
5. the EEG signals preprocess method in sleep state according to claim 3 analysis is it is characterised in that in described The self-adaptative adjustment of the length of window of value filtering is interval to be k fs；Wherein, k represents constant, and fs represents sample frequency.
6. the EEG signals preprocess method in sleep state according to claim 5 analysis is it is characterised in that described k Span be [0.01,0.5].
7. the EEG signals preprocess method in sleep state according to claim 3 analysis is it is characterised in that its feature It is, described fl=8hz, fh=13hz.
8. the EEG signals preprocess method in sleep state analysis according to claim 1 is it is characterised in that gather institute The length stating EEG signals is more than 30s.
9. the EEG signals preprocess method in sleep state according to claim 1 analysis is it is characterised in that in described The formula of value filtering is as follows:
x′i=xi-bi,bi=med { xi-n,xi-n+1,...,xi,xi+n-1,xi+n, i=1 ..., m
In formula, x represents original EEG signals, and x ' represents the EEG signals after removing baseline drift, biRepresent and extracted by medium filtering The baseline going out, m represents the length of EEG signals, med represent the element in window is sorted by size after take the computing of intermediate value, n Represent the length of window of medium filtering.
10. the EEG signals pretreatment system in a kind of sleep state analysis is it is characterised in that include:
Electroencephalogramsignal signal acquisition module, for gathering the original EEG signals that user produces in sleep procedure；
Original EEG signals, according to the length of window of default medium filtering, are carried out medium filtering, filter by medium filtering module Baseline drift；
Length of window adjusting module, for according to filtered EEG signals frequency and amplitude to the length of window of medium filtering from Adapt to adjustment, until the extreme point of filtered EEG signals frequency is located in setting frequency range, and the average of EEG signals amplitude Absolute value is minimum；
Signal output module, filters the EEG signals of baseline drift for output.
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CN115989998A (en) * 2022-11-22 2023-04-21 常州瑞神安医疗器械有限公司 Method for detecting sleep stage of parkinsonism patient
CN116548928A (en) * 2023-07-11 2023-08-08 西安浩阳志德医疗科技有限公司 Nursing service system based on internet

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CN101966080A (en) * 2010-10-26 2011-02-09 东北大学 Portable active electroencephalogram monitor and control method thereof
CN102479383A (en) * 2010-11-30 2012-05-30 上海银晨智能识别科技有限公司 Method and device for removing salt and pepper noise
CN103150733A (en) * 2013-03-25 2013-06-12 中国矿业大学(北京) Self-adapting multi-stage weighted median filtering algorithm applied to digital images
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CN115989998A (en) * 2022-11-22 2023-04-21 常州瑞神安医疗器械有限公司 Method for detecting sleep stage of parkinsonism patient
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CN116548928A (en) * 2023-07-11 2023-08-08 西安浩阳志德医疗科技有限公司 Nursing service system based on internet
CN116548928B (en) * 2023-07-11 2023-09-08 西安浩阳志德医疗科技有限公司 Nursing service system based on internet

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