CN113855047A - FVEP signal enhancement method based on abnormal segment recognition - Google Patents
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Abstract
An FVEP signal enhancement method based on abnormal segment identification is characterized by comprising the steps of (1) preprocessing acquired vision-induced original electroencephalogram signals, (2) segmenting the preprocessed original electroencephalogram signals into segments according to stimulation time points of periodic flash stimulation, and (3) screening out segments with higher quality; (4) and carrying out superposition averaging on the screened high-quality fragments to obtain an enhanced FVEP signal. Experiments prove that the method can greatly enhance the repeatability of the extracted FVEP signal.
Description
Technical Field
The application relates to an FVEP signal enhancement method based on abnormal segment identification.
Flash Visual Evoked Potential (FVEP), as an Event Related Potential (ERP), refers to an electroencephalogram potential generated at the position of the occipital bone of the back of a human by the transmission of optic nerves after both eyes of the human are stimulated by non-modal diffuse flash light, and a typical waveform thereof is shown in fig. 3. Some studies show that when a human is in a state of raised intracranial pressure, the brain visual pathway of the human is damaged, and the conduction of visual nerve signals is influenced, so that the time delay between the N2 wave in the FVEP and stimulation is higher than that of a normal human, and therefore, the FVEP is widely applied to hydrocephalus, cerebral hemorrhage and other clinical researches as an important noninvasive intracranial pressure monitoring means.
However, FVEP is very weak, peaking only on the order of 10 μ V, so it is swamped in the resting brain electrical and background noise, and cannot be directly identified by the naked eye as to whether it is present. Therefore, how to extract the FVEP signal with higher quality from the original brain electrical signal is a topic worthy of research. The present method is directed to this subject.
Background
In the aspect of the FVEP signal enhancement technology, the mainstream practice in the industry at present is to enhance the FVEP signal by using the superposition average FVEP (afvep), and the method mainly comprises three stages: preprocessing, slicing and superposing and averaging. The method comprises the steps of firstly carrying out preprocessing such as filtering on an electroencephalogram signal generated by a subject under the stimulation of a flash with a certain frequency within a period of time, then segmenting a series of FVEP segments according to the time points of periodic stimulation, and finally carrying out superposition averaging on the segmented FVEP segments to obtain an enhanced FVEP signal, wherein the interference of resting electroencephalograms and noise which are irrelevant to stimulation is inhibited.
However, in this method, some low-quality abnormal FVEP segments may affect the signal quality of the FVEP signal averaged over the entire superposition, and just because the FVEP cannot be directly identified by naked eyes before the superposition, such abnormal segments are not easily screened out and rejected in time, so that the quality of the enhanced FVEP signal obtained by the superposition averaging is reduced, and in the worst case, the measurement may be performed again, which increases the burden on medical care personnel and patients, and wastes other effective segments participating in the averaging. Therefore, there is a need for a method to identify anomalous segments prior to superposition averaging, which can be removed to improve the FVEP signal quality.
Regarding the identification of abnormal outlier fragments, one can easily think of methods are: the average of all the segments is used as a template, then each segment is used to sum the template to calculate the correlation coefficient, and the segments with low correlation coefficient are removed as low quality segments. However, this method is not suitable for the problem of identification of the FVEP abnormal fragment, because the FVEP signal itself is very weak and the magnitude of the interference is often larger, the template calculated finally has a larger error, and cannot be used as a basis for judging the outlier fragment.
In summary, the characteristics of the FVEP signal objectively require to provide a new abnormal segment identification method, and the related method is not reported in the literature at present and has a large research space.
Disclosure of Invention
Object of the Invention
Compared with the traditional method, the novel FVEP signal enhancement method based on abnormal segment identification can obtain higher FVEP signal quality and has stronger robustness.
Technical scheme
An FVEP signal enhancement method based on abnormal segment identification is characterized by comprising the steps of (1) preprocessing acquired vision-induced original electroencephalogram signals, (2) segmenting the preprocessed original electroencephalogram signals into segments according to stimulation time points of periodic flash stimulation, and (3) screening out segments with higher quality; (4) and carrying out superposition averaging on the screened high-quality fragments to obtain an enhanced FVEP signal.
According to the FVEP signal enhancement method based on abnormal segment identification, the step (3) screens out the segment with higher quality, which is characterized by comprising the following steps: (1) firstly, extracting the characteristics of each segment by utilizing Discrete Wavelet Transform (DWT); (2) screening out the segment X with the highest degree of outlier by using a classification support vector machine (OCSVM) based on the extracted features, (3) carrying out outlier confirmation on the screened segment X with the highest degree of outlier if the segment X is close to the average waveform T power of other segments and the correlation coefficient is larger than a threshold th1And the absolute value of the logarithm of the ratio of the signal energies is smaller than another threshold th2If yes, the fragment is not obviously clustered but not excluded, and the process is terminated, otherwise, the outlier fragment X is excluded, and whether the total number of the screened fragments reaches the set upper limit N is judged, if yes, the process is terminated, otherwise, the step (2) is carried out to continue the processing process of the rest fragments. Parameter th1、th2And N may be determined empirically by an expert in the field.
According to the FVEP signal enhancement method based on abnormal segment identification, the step (3) selects the step (3) of the fragments with higher quality, and the step (3) confirms the outlier of the selected fragment with the highest outlier, and is characterized in that the absolute values of the logarithm of the ratio between the correlation coefficient and the signal energy of the average waveform T of the selected signal segment X and the other fragments except X are respectively defined as
And
has the beneficial effects.
At present, no literature report is available on an FVEP signal enhancement method based on abnormal fragment recognition.
Because the general data volume of the FVEP is not very large (otherwise, a large physiological burden is brought to a tested person), and the signals are subjected to the feature extraction of wavelet transform, the data is compressed, the data volume is usually small, and the time overhead of the algorithm can be ignored.
After tens of experiments, compared with the general moving average method, the method can significantly improve the signal quality of the FVEP signals with strong interference, as shown in fig. 2.
The better the FVEP correlation for the same subject, measured at different times and under the same conditions, indicates the better reproducibility of the method. Applicants have conducted experiments on ten subjects: the FVEP signals of each subject under the same state are measured at five different times, and the FVEP correlation coefficient among five groups of data is calculated to be used as a performance evaluation index of the method and used as a control group by using a traditional method. Table 1 gives the experimental control results.
As can be seen from table 1, the data for 40% of the subjects showed significant enhancement in signal quality (greater than 10% enhancement) after treatment with the method, and the data for 60% of the subjects did not change, which is clearly a good result considering that not every subject had many fragments with poor signal quality.
Table 1: comparison of experimental results for the reproducibility of the method of the invention
| Test subject | Conventional methods | The method of the invention | Improvement of |
| 1 | 0.62 | 0.64 | 0.02 |
| 2 | 0.57 | 0.72 | 0.15 |
| 3 | 0.59 | 0.54 | -0.04 |
| 4 | 0.48 | 0.48 | 0 |
| 5 | 0.3 | 0.37 | 0.06 |
| 6 | 0.49 | 0.76 | 0.26 |
| 7 | 0.45 | 0.64 | 0.19 |
| 8 | 0.49 | 0.52 | 0.03 |
| 9 | 0.69 | 0.76 | 0.07 |
| 10 | 0.32 | 0.54 | 0.22 |
Drawings
FIG. 1 is a flow chart of the present application, a FVEP signal enhancement method based on abnormal segment identification.
Fig. 2 is a comparison graph of FVEP signals obtained by processing a certain data using the present method and the conventional method.
Fig. 3, a typical FVEP signal, where P1, P2, P3 represent the first, second and third positive going waves and N1, N2, N3 represent the first, second and third negative going waves.
Detailed Description
Examples are given. A hardware device and a software system for acquiring the FVEP signals are built, 10 subjects are tested, and the test results are shown in the beneficial effect part.
The hardware device is mainly divided into a stimulator and a sensor:
(1) stimulator part, according to the parameters mentioned in the related studies, this example used a self-made light-emitting eye mask for flash stimulation with a wavelength of 590 ± 5nm, a luminance of 5000cd/m2 and a stimulation field width of up to 50 °. The stimulation frequency was 1.0Hz, the pulse width was 1ms, and the stimulation duration was 1 minute.
(2) In the sensor part, the scalp of a subject is treated by using physiological saline and alcohol, then the data acquisition is carried out by using a wet electrode electroencephalogram cap (two electrodes of O1 and O2 are used and respectively correspond to the left side of the occipital bone and the right side of the occipital bone), and then the electrode impedance is reduced to be below 50k omega by using electroencephalogram paste. The average potential of the left and right ears was used as a zero potential reference point. The collected signals are converted into physiological signals with the sampling rate of 500Hz through a second-order Butterworth filter of 0.1Hz-100Hz and a physiological signal collection chip ADS 1298R.
The application relates to the software part of the system, which, as stated above, is implemented as follows:
(1) preprocessing, removing power frequency interference by a second-order 50Hz Butterworth band elimination filter, and filtering by a second-order 0.8-40Hz Butterworth band-pass filter.
(2) Slicing, the input signal is sliced into a series of segments of length 1s at a stimulation frequency of 1 Hz.
(3) Abnormal segment screening, using three discrete wavelet transforms based on Daubechies 2 wavelets as feature extraction, and then using OCSVM to perform abnormal segment identification, and taking σ as 0.04 and v as 0.5 as key parameters of the OCSVM. In the case of outlier detection, th1 is 0.1, th2 is 3, and N is 10.
(4) And superposing and averaging to obtain the final FVEP signal.
Claims (3)
1. An FVEP signal enhancement method based on abnormal segment identification is characterized by comprising the steps of (1) preprocessing acquired vision-induced original electroencephalogram signals, (2) segmenting the preprocessed original electroencephalogram signals into segments according to stimulation time points of periodic flash stimulation, and (3) screening out segments with higher quality; (4) and carrying out superposition averaging on the screened high-quality fragments to obtain an enhanced FVEP signal.
2. The FVEP signal enhancement method based on abnormal segment identification as claimed in claim 1, wherein the step (3) selects the segment with higher quality, and the method comprises the following steps: (1) firstly, extracting the characteristics of each segment by Discrete Wavelet Transform (DWT), (2) screening the segment X with the highest degree of outlier by using a classification support vector machine (OCSVM) based on the extracted characteristics, and (3) carrying out outlier confirmation on the screened segment X with the highest degree of outlier if the segment X is close to the average waveform T power of other segments and the correlation coefficient is greater than a threshold th1And the absolute value of the logarithm of the ratio of the signal energies is smaller than another threshold th2If yes, the fragment is not obviously clustered but not excluded, and the process is terminated, otherwise, the outlier fragment X is excluded, and whether the total number of the screened fragments reaches the set upper limit N is judged, if yes, the process is terminated, otherwise, the step (2) is carried out to continue the processing process of the rest fragments.
3. The method for FVEP signal enhancement based on abnormal segment identification as claimed in claim 1 and claim 2, wherein step (3) of claim 2, wherein the selected segment with the highest degree of outlier is identified by outlier identification, wherein the logarithm of the ratio of the correlation coefficient and the signal energy of the average waveform T of the selected signal segment X and the segments other than X is defined as the absolute value of the logarithm of the ratio of the correlation coefficient and the signal energy
And
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| CN115115038A (en) * | 2022-08-30 | 2022-09-27 | 合肥心之声健康科技有限公司 | Model construction method based on single lead electrocardiosignal and gender identification method |
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| CN115115038A (en) * | 2022-08-30 | 2022-09-27 | 合肥心之声健康科技有限公司 | Model construction method based on single lead electrocardiosignal and gender identification method |
| CN115115038B (en) * | 2022-08-30 | 2022-11-08 | 合肥心之声健康科技有限公司 | Model construction method based on single lead electrocardiosignal and gender identification method |
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