CN107095668B - A kind of detection method of the adaptive eeg signal exception based on time-domain analysis - Google Patents
A kind of detection method of the adaptive eeg signal exception based on time-domain analysis Download PDFInfo
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Abstract
The detection method for the adaptive eeg signal exception based on time-domain analysis that the invention discloses a kind of, scheme are as follows: acquire eeg signal to be detected, carry out noise-removed filtering；It is segmented according to time span；Calculate each section of average value and variance；Choose sensitivity parameter；Calculate each eeg signal section weighted average and weighted variance；Calculate the adaptive weighted threshold value of EEG signals；Choose patient time；The EEG signals for being more than threshold value are counted, count value is greater than Realtime Alerts when setting certain value；Alarm times are counted, number is more, and it is bigger which is in abnormal conditions probability.The present invention have the characteristics that conveniently, safely, inexpensively, it is noninvasive, but also can in real time, dynamically observe measurand brain working condition, provide effective adjuvant treatment for clinical medicine.
Description
Technical field
The present invention relates to eeg signal detections and identification technology field, and in particular to a kind of based on the adaptive of time-domain analysis
Answer the detection method of eeg signal exception.
Background technique
Brain is the maincenter of human perception processing external environment information, is the control of the functions such as human body everything, language
Center, is issued to body parts as medium using the nerve cell of whole body and is instructed, these command informations pass through EEG signals
It passes out, and EEG signals can show that certain is corresponding, has rule with the thinking activities of people or environmental stimuli etc.
The wish of the variation of rule, i.e. people expressed by abstract brain activity can be characterized by actual physics electric signal.Currently,
Research about eeg signal is in laboratory stage mostly, and the field of practical application is less, is concentrated mainly on medical neck
Domain.In medical field, eeg signal is predominantly had a normal thinking and the patient of physical handicaps provides foreign exchanges and sets to outside
Standby control, such as intelligent wheel chair, virtual typing and robot manipulation.
In the prior art, eeg signal detection is generally divided into subjective detection technique and objective detection technique.Subjectivity detection
Technology refers to that attending physician or brain section spirit scholar observe collected eeg signal for a long time, is determined greatly with this
Whether brain is normal.Objective detection technique refers to using existing Time-Frequency Analysis method, is detected automatically to eeg signal,
Provide the result with certain confidence level value.But frequency domain model so far, is concentrated mainly on about the research of eeg signal
In the research enclosed, and actual clinical passes through the brain telecommunications of noise-removed filtering the study found that when brain is in abnormal operation
Number amplitude be often required to be higher than normal EEG signals, using the eeg signal collected, after noise-removed filtering, in real time
Weighted Threshold is calculated, can adaptively EEG signals be changed, and then eeg signal is detected, according to beyond threshold value
Number, to judge the working condition of brain.
Summary of the invention
The detection method for the adaptive eeg signal exception based on time-domain analysis that the purpose of the present invention is to provide a kind of,
It realizes to the real-time detection and real-time display of eeg signal exception, provides auxiliary therapy for clinical medicine.
The present invention is achieved by the following technical programs:
A kind of detection method of the adaptive eeg signal exception based on time-domain analysis, by the brain for acquiring measurand
Electric signal, the abnormality detection of eeg signal is carried out according to the Time Domain Amplitude features of tested EEG signals, thus determines to be tested
The working condition of object brain, detection method include the following steps:
Step 1. acquires the eeg signal of detected object, chooses the EEG signals to be analyzed that length is k and is denoised
Filtering, then the EEG signals of k length are segmented as unit of signal length n, wherein n < k, and n, k belong to it is just whole
Number；
Step 2. calculates the average value m (k) and variance v (k) of each eeg signal section after segmentation, the average value m (k)
It is calculated with variance v (k) using following formula:
Wherein, k indicates the length of analyzed EEG signals, and n indicates signal length unit, x(i)Indicate EEG signals；
Step 3. chooses sensitivity parameter ζ, and sensitivity parameter ζ is calculated using following formula:
ζ=σ * v (k)/m (k) (2)
Wherein, σ is adjustment parameter, σ ∈ (0,1)；
Step 4. calculates the weighted average of each section of EEG signals according to sensitivity parameter ζAnd weighted varianceThe weighted averageAnd weighted varianceIt is calculated using following formula:
Step 5. calculates the adaptive weighted threshold value T (k) of EEG signals, and the Weighted Threshold T (k) utilizes following formula meter
It calculates:
Wherein, tβIndicate the quantile that the t that probability is β is distributed；
Step 6. selected threshold patient time tro, the threshold value patient time troIt is calculated using following formula:
tro=α t (5)
Wherein, α=0.5,0.6, t is brain wave renewal time；
Step 7. carries out counting statistics to the eeg signal beyond Weighted Threshold, if the brain wave beyond Weighted Threshold is believed
Number statistical value be greater than the set value, then generate Realtime Alerts, and counting statistics are carried out to Realtime Alerts number m；
The value of step 8. Realtime Alerts number m is bigger, then collected EEG signals be in abnormal conditions probability it is bigger,
Setting abnormal alarm number is l, when the value of Realtime Alerts number m is greater than or equal to the value of abnormal alarm number l, then determines to work as
Preceding EEG signals are in abnormal.
The detection method of adaptive eeg signal exception based on time-domain analysis as described above, the signal length n
Selection so that selected EEG signals meet approximate normal distribution, because of the individual difference of acquisition device, frequency acquisition and measurand
Different, signal length n can be set as real-time, tunable parameter according to clinical practice.Preferably, signal length n can be used sliding window algorithm into
Row is chosen, and the update set of EEG signals section is realized using sliding window algorithm are as follows:
[x (tf+1) ..., x (n), x (n+1) ..., x (n+tf)]
Wherein, n≤f, f are acquisition device frequency, and t is brain wave renewal time.
Further, it is preferred that when the value range of σ is 0.4≤σ≤0.8 in the step 2, can preferably adjust threshold
It is worth sensitive effect.The value of abnormal alarm number l may be configured as l >=20 in the step 8, when the value of m is equal to or more than the l of setting
When value, it is detected brain wave and is in abnormality.
In above-mentioned detection process, it is contemplated that the otherness and Different Individual EEG signals of Different Individual brain have different
Amplitude intensity, eeg signal length n, sensitivity parameter ζ, patient time troAnd abnormal alarm number l etc. may be configured as
Clinical implementation adjustable parameter.
The detection method for the adaptive eeg signal exception based on time-domain analysis that the present invention provides a kind of, by using
Above-mentioned technical proposal, the present invention achieve it is following the utility model has the advantages that
1, the present invention is by the EEG signals of collected measurand, according to its Time Domain Amplitude feature to eeg signal into
Row abnormality detection, according to beyond threshold value number judge the working condition of brain, have conveniently, safely, inexpensively, noninvasive spy
Point, and the working condition of measurand brain can be simultaneously and dynamically observed, effective auxiliary, which is provided, for clinical medicine controls
It treats, versatility is preferable, is worth popularizing and applying.
2, this detection method carries out signal length parameter selection using sliding window algorithm, and by eeg signal length n, sensitive
Spend parameter ζ, patient time troAnd abnormal alarm number l etc. is set as clinical implementation adjustable parameter, to realize individual difference
Property eeg signal exception accurate detection, for solving different individual character bodies, improve detection method generalization ability have it is important
Effect.
3, the present invention is based on the detection methods of the adaptive eeg signal exception of time-domain analysis, using Time Domain Analysis
It substitutes common frequency-domain analysis method to detect eeg signal, easy-to-operate improves measuring accuracy, for clinic
It is convenient that the detection treatment of medicine provides.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the EEG signals test result figure of the embodiment of the present invention；
Specific embodiment
It elaborates with reference to the accompanying drawing to the embodiment of the present invention, the present embodiment before being with technical solution of the present invention
It puts and is implemented, give detailed embodiment and process, but protection scope of the present invention is not limited to following embodiments.
Referring to Fig.1, steps are as follows for realization of the invention:
Step 1. acquires the eeg signal of detected object, chooses the EEG signals to be analyzed that length is k and is denoised
Filtering, then the EEG signals of k length are segmented as unit of signal length n, wherein n < k, and n, k belong to it is just whole
Number；
Selecting suitable eeg signal length n is the key that this step, and signal length n should guarantee that selected brain wave is long
Degree meets approximate normal distribution, guarantees that selected eeg signal can reflect the brain work shape of measurand in real time again
State is needed because of the difference of acquisition device, frequency acquisition and measurand according to clinical practice self-setting.Under general case, letter
Number length n can be used sliding window algorithm and be selected, and in the present embodiment, acquisition device frequency is 500Hz, i.e., acquisition 500 per second
It is a, the EEG signals of 60s length are acquired as signal to be analyzed, select at 200 points as an eeg signal section, for one section
Signal, 50 collection points after updating every time, collectively constitutes 200 collection points with original 150~200 collection points, so real
It is the real-time of 0.1s between current, i.e., 0.1s is the brain working condition that unit reflects measurand.Sliding window algorithm realizes brain
The update rule of telecommunications number section are as follows:
[x (tf+1) ..., x (N), x (N+1) ..., x (N+tf)]
Wherein, n=N, 200≤N≤f, f are acquisition device frequency, and t is brain wave renewal time.
Step 2. calculates the average value m (k) and variance v (k) of each eeg signal section after segmentation, the average value m (k)
It is calculated with variance v (k) using following formula:
Wherein, k indicates the length of analyzed EEG signals, and n indicates signal length unit, x(i)Indicate EEG signals.
Step 3. chooses sensitivity parameter ζ
The setting principle of sensitivity parameter ζ is so that threshold value has enough sensitivity to measured signal, because of measurand
Individual difference, can be arranged according to actual clinical test, under normal circumstances, sensitivity parameter ζ can using following formula calculate:
ζ=σ * v (k)/m (k) (2)
Wherein σ is adjustment parameter and σ ∈ (0,1), in the present embodiment, chooses adjustment parameter σ=0.8.It is obtained using the method
Sensitivity parameter is obtained, can eliminate because EEG signals variation excessively acutely causes variance excessive, generate the phenomenon that big number eats decimal.
Step 4. calculates the weighted average of each section of EEG signals according to sensitivity parameter ζAnd weighted varianceThe weighted averageAnd weighted varianceIt is calculated using following formula:
Step 5. calculates the adaptive weighted threshold value T (k) of EEG signals, and the Weighted Threshold T (k) utilizes following formula meter
It calculates:
Wherein, tβIndicate the quantile that probability is distributed for the t of β, in the multiple measurement carried out according to laboratory, the present embodiment
In, choose tβ=0.1.
Step 6. selected threshold patient time tro, threshold value patient time troThe experience repeatedly measured in laboratory can be used
Value, can also be used following formula and is calculated:
tro=α t (5)
Wherein, α=0.5,0.6, t is brain wave renewal time.
Step 7. carries out counting statistics to the eeg signal beyond Weighted Threshold, if the brain wave beyond Weighted Threshold is believed
Number statistical value be greater than the set value s, then generate Realtime Alerts, and counting statistics are carried out to Realtime Alerts number m.
Setting value s can be arranged according to actual clinical test, to realize the accurate of individual difference eeg signal exception
Detection, the value of the present embodiment, setting value s are set as 3.
The value of step 8. Realtime Alerts number m is bigger, then collected EEG signals be in abnormal conditions probability it is bigger,
Setting abnormal alarm number is l, when the value of Realtime Alerts number m is greater than or equal to the value of abnormal alarm number l, then determines to work as
Preceding EEG signals are in abnormal.
Abnormal alarm time numerical value can be arranged according to actual clinical test, and l=is arranged under general case and in the present embodiment
20, when the value of Realtime Alerts number m is equal to or more than 20, that is, judge that brain wave is in abnormality.
As shown in Fig. 2, enabling acquisition device frequency is 500Hz, the EEG signals of 60s length are acquired as brain telecommunications to be analyzed
Number, selecting at 200 points as an eeg signal section, brain wave renewal time is 0.1s, and adjustment of sensitivity parameter chooses σ=0.8,
tβ=0.1, α=0.5, setting value s=3, abnormal alarm number l=20, obtain acquisition shown in top half image to
Eeg signal figure is analyzed, corresponding with eeg signal figure to be analyzed, lower half portion image is abnormal EEG signals arteries and veins occur
Punching figure.In figure, in top half image, Weighted Threshold in L1 expression, L2 indicates that lower Weighted Threshold, S indicate eeg signal；
P indicates alarm pulse in the image of lower half portion, when the quantity for the impulse line P that alarms is more than 20 times, that is, determines tested brain wave letter
Number exception.
The technology contents of the not detailed description of the present invention are well-known technique.
Claims (5)
1. a kind of detection method of the adaptive eeg signal exception based on time-domain analysis, which is characterized in that including following step
It is rapid:
Step 1. acquires the eeg signal of detected object, chooses the EEG signals to be analyzed that length is k and carries out noise-removed filtering,
Then the EEG signals of k length are segmented as unit of signal length n, wherein n < k, and n, k belong to positive integer；
Step 2. calculates the average value m (k) and variance v (k) of each eeg signal section after segmentation, the average value m (k) and side
Poor v (k) is calculated using following formula:
Wherein, k indicates the length of analyzed EEG signals, and n indicates signal length unit, and x (i) indicates EEG signals；
Step 3. chooses sensitivity parameter ζ, and sensitivity parameter ζ is calculated using following formula:
ζ=σ * v (k)/m (k) (2)
Wherein, (0,1) adjustment parameter σ ∈；
Step 4. calculates the weighted average of each section of EEG signals according to sensitivity parameter ζAnd weighted varianceInstitute
State weighted averageAnd weighted varianceIt is calculated using following formula:
Step 5. calculates the adaptive weighted threshold value T (k) of EEG signals, and the Weighted Threshold T (k) is calculated using following formula:
Wherein, tβIndicate the quantile that the t that probability is β is distributed；
Step 6. selected threshold patient time tro, the threshold value patient time troIt is calculated using following formula:
tro=α t (5)
Wherein, α=0.5 or 0.6, t are brain wave renewal time；
Step 7. carries out counting statistics to the eeg signal beyond Weighted Threshold, if the eeg signal beyond Weighted Threshold
Statistical value is greater than the set value, then generates Realtime Alerts, and carry out counting statistics to Realtime Alerts number m；
The value of step 8. Realtime Alerts number m is bigger, then collected EEG signals be in abnormal conditions probability it is bigger, setting
Abnormal alarm number is l, when the value of Realtime Alerts number m is greater than or equal to the value of abnormal alarm number l, then determines to work as forebrain
Electric signal is in abnormal.
2. a kind of detection method of adaptive eeg signal exception based on time-domain analysis according to claim 1,
It is characterized in that, the signal length n of eeg signal described in step 1 is chosen using sliding window algorithm.
3. a kind of detection method of adaptive eeg signal exception based on time-domain analysis according to claim 2,
It is characterized in that, the sliding window algorithm realizes the update set of EEG signals section are as follows:
[x (tf+1) ..., x (n), x (n+1) ..., x (n+tf)]
Wherein, n≤f, f are acquisition device frequency, and t is brain wave renewal time.
4. according to right want 1 described in a kind of detection method of the adaptive eeg signal exception based on time-domain analysis, it is special
Sign is that the value range of adjustment parameter σ is 0.4≤σ≤0.8 in the step 3.
5. according to right want 1 described in a kind of detection method of the adaptive eeg signal exception based on time-domain analysis, it is special
Sign is that the value of the abnormal alarm number l is l >=20 in step 8.
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| EP1447045A1 (en) * | 2003-02-17 | 2004-08-18 | Brain Functions Laboratory, Inc. | Method and apparatus for measuring the degree of neuronal impairment in brain cortex |
| CN102940490A (en) * | 2012-10-19 | 2013-02-27 | 西安电子科技大学 | Method for extracting motor imagery electroencephalogram signal feature based on non-linear dynamics |
| CN104173045A (en) * | 2014-08-15 | 2014-12-03 | 浙江大学医学院附属第二医院 | Epileptic seizure prewarning system |
| CN104622467A (en) * | 2015-01-12 | 2015-05-20 | 天津大学 | Method for detecting electroencephalogram signal complexity abnormity of Alzheimer disease |
| CN104869897A (en) * | 2012-10-12 | 2015-08-26 | 通用医疗公司 | System and method for monitoring and controlling a state of a patient during and after administration of anesthetic compound |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1447045A1 (en) * | 2003-02-17 | 2004-08-18 | Brain Functions Laboratory, Inc. | Method and apparatus for measuring the degree of neuronal impairment in brain cortex |
| CN104869897A (en) * | 2012-10-12 | 2015-08-26 | 通用医疗公司 | System and method for monitoring and controlling a state of a patient during and after administration of anesthetic compound |
| CN102940490A (en) * | 2012-10-19 | 2013-02-27 | 西安电子科技大学 | Method for extracting motor imagery electroencephalogram signal feature based on non-linear dynamics |
| CN104173045A (en) * | 2014-08-15 | 2014-12-03 | 浙江大学医学院附属第二医院 | Epileptic seizure prewarning system |
| CN104622467A (en) * | 2015-01-12 | 2015-05-20 | 天津大学 | Method for detecting electroencephalogram signal complexity abnormity of Alzheimer disease |
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