AU2021102053A4 - Processing and identification method for spike-and-slow-wave complex in electroencephalogram (eeg) - Google Patents
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Abstract
The present disclosure provides a processing and identification method for a spike-and-slow
wave complex in an electroencephalogram (EEG). The method includes the following steps: Si:
inputting an original EEG (intracranial, epicranium, or other EEG) signal, and performing
wavelet transform to obtain a wavelet coefficient; S2: setting a threshold, and performing
denoising by using a wavelet method; S3: performing wavelet reconstruction to obtain a denoised
signal; S4: finding an extremum of the denoised signal, and decomposing the original EEG signal
into 350 ms segments by using the extremum as a center; S5: intercepting N (for example, N =
100) pieces of 250 ms spike-and-slow-wave complex data; S6: performing superimposing,
averaging, and fitting to obtain a spike-and-slow-wave complex template function f(t); S7:
constructing a mother wavelet function '(t); S8: performing wavelet transform to obtain a
wavelet coefficient; and S9: setting a threshold, detecting a spike-and-slow-wave complex, and
outputting a detection result. The processing and identification method and apparatus for a spike
and-slow-wave complex in an EEG in the present disclosure are characterized by a strong anti
interference capability, high identification efficiency, accurate identification, weak dependence
on manual operations, and other advantages.
1/2
Input EEG data Train a data set
Perform wavelet transform
(two-order symlet wavelet) Nspik- 7 1ow
wave complexes from training
data
Set a threshold, and perform
wavelet reconstruction for
denoising
Perform superposing,
averaging, and fitting to obtain
a spike-and-slow-wave complex
template function, and construct
Decompose an original a mother wavelet function
signal into signal segments
(350 ms)
Detect a spike-and-slow
wave complex
[Outputan identification
result
FIG. 1
Description
1/2
Input EEG data Train a data set
Perform wavelet transform (two-order symlet wavelet) Nspik- 7 1ow wave complexes from training data
Set a threshold, and perform wavelet reconstruction for denoising Perform superposing, averaging, and fitting to obtain a spike-and-slow-wave complex template function, and construct Decompose an original a mother wavelet function signal into signal segments (350 ms)
Detect a spike-and-slow wave complex
[Outputan identification result
FIG. 1
TECHNICAL FIELD The present disclosure relates to a processing and identification method for a spike-and-slow wave complex in an electroencephalogram (EEG).
BACKGROUND Epilepsy is a nervous system disease, and affects nearly 1% of the world's population. An epileptic attack is a clinical manifestation of excessive discharge of cranial nerves. Such discharge is usually referred to as a "paroxysmal activity" and occurs in an ictal phase or in an interictal phase. A spike-and-slow-wave complex is a main waveform of epileptiform discharge, lasting 150 ms to 350 ms. A main component of the spike-and-slow-wave complex is a negative phase, with a steep waveform and a variable amplitude. A typical spike wave has a steep ascending branch and a slightly gentle descending branch, and is followed by a slow wave. The spike-and-slow wave complex is usually a pathological wave. Identification of the spike-and-slow-wave complex can help medical staff to make auxiliary diagnosis for epilepsy, and provide a basis for further classification and development of a corresponding treatment scheme. In clinical practice, the spike-and-slow-wave complex is mainly identified manually, which is inefficient and makes it difficult to perform large-scale analysis. In addition, due to subjective factors, results of identifying same data by different persons may be different. As a result, an identification result of the spike-and-slow-wave complex depends closely on subjective experience of a person performing the identification. Automatic detection of abnormal EEG discharge in an epileptic patient can promote long term monitoring in diagnosis, enable a doctor to monitor the epileptic patient in real time, and test and evaluate benefits of different drugs to quantitatively measure an epileptic activity. An existing automatic detection technology is mainly used to transform data into a specific domain for feature extraction and classification. Features are extracted in time domain, frequency domain, and time-frequency domain, such as wavelet transform. An EEG signal is non-stationary. Therefore, the time-frequency domain method usually has a higher success rate than the other two methods. Compared with a traditional signal analysis technology, wavelet transform is a powerful tool for analyzing the EEG signal when epileptic attacks are automatically detected.
SUMMARY To resolve the foregoing technical problems, the present disclosure provides a processing and identification method for a spike-and-slow-wave complex in an EEG. The present disclosure is realized by using the following technical solutions: A processing and identification method for a spike-and-slow-wave complex in an EEG includes the following steps: Si: inputting an original signal, and performing wavelet transform to obtain a wavelet coefficient; S2: setting a threshold, and performing denoising by using a wavelet method; S3: performing inverse wavelet transform to obtain a denoised signal; S4: decomposing the original signal into 350 ms segments by using an extremum of the denoised signal as a center; S5: selecting and intercepting N pieces of 250 ms spike-and-slow-wave complex data; S6: performing superposing, averaging, and fitting to obtain a spike-and-slow-wave complex template function f(t); S7: constructing a mother wavelet function'F(t); S8: performing wavelet transform to obtain a wavelet coefficient; and S9: setting a threshold, detecting a spike-and-slow-wave complex, and outputting a detection result. Preferably, in Sl, a method for applying wavelet transform to the signal with noise to obtain the wavelet coefficient that can be used to distinguish between the signal and the noise is as follows: CO
WTwx(a,b) = x (t)0 a,b(t)dt)
where qa,b(0 al - 1/2 (a'), and {a = 2-, bk = 2-k}.
Preferably, in S2, the threshold is set for the wavelet coefficient, and all wavelet coefficients less than the threshold are set to 0. Preferably, in S4, a distance between two adjacent extremums needs to be greater than a set threshold, and if the distance is less than the set threshold, only an extremum with a maximum absolute value is retained, and to-be-processed data is divided into a plurality of wavelet segments. Preferably, N = 100 in S5. Preferably, in S6, a formula for superposing, averaging, and fitting 100 spike-and-slow-wave complexes to obtain the spike-and-slow-wave complex template function f(t) is as follows: I n f(t)=- Yxi (t) n where n = 100, and x(t) presents an intercepted spike-and-slow-wave complex. Preferably, the mother wavelet function (t) constructed in S7 is as follows: y(t)=f(t)-(at2 + Pt + y); and coefficients a, P, and y are obtained by solving the following linear equations: "" s a-e2d -c a f f(t)dt c2 c 1A = f(c) d2 d 1 f(d) where t is set to a value in [c, d], 0 < c < d < 250 ms,P(c)= 0, and T(d)= 0. Preferably, in S8, an absolute value of the wavelet coefficient needs to be greater than a universal threshold estimated based on the wavelet coefficient. A processing and detection apparatus for a spike-and-slow-wave complex in an EEG, using the foregoing processing and identification method for a spike-and-slow-wave complex in an EEG, includes an EEG data importing module, a data denoising module, a data decomposition module, and a spike-and-slow-wave complex detection and output module. The EEG data importing module is configured to import to-be-identified EEG data. The data denoising module is configured to denoise the to-be-identified EEG data. The data decomposition module is configured to divide the original data into short segments to obtain a to-be-detected segment. The spike-and-slow-wave complex detection and output module is configured to detect a spike-and slow-wave complex and output a result. Preferably, the spike-and-slow-wave complex detection and output module constructs a template function by using a superposing and averaging method: 1 100 f W)= Y'xi(t) 100 ya A mother wavelet function P(t) needs to be constructed before the spike-and-slow-wave complex is detected through wavelet transform: (t)=f(t)-(at2 _+ +t Coefficients a, P, and y are obtained by solving the following linear equations: as-3 a-e2d - c a f f(t)dt c2 c 1-= f(c) d2 d 1 f(d)
A wavelet coefficient is then obtained through wavelet transform, where an absolute value of the wavelet coefficient needs to be greater than a universal threshold estimated based on the wavelet coefficient; a maximum time difference between the EEG data and a wavelet extremum is less than or equal to a threshold defined based on experience; and one spike-and-slow-wave complex is output if the condition is satisfied. The present disclosure has the following beneficial effects: The processing and identification method and apparatus for a spike-and-slow-wave complex in an EEG in the present disclosure are characterized by a strong anti-interference capability, high identification efficiency, accurate identification, weak dependence on manual operations, and other advantages. The term "comprise" and variants of the term such as "comprises" or "comprising" are used herein to denote the inclusion of a stated integer or stated integers but not to exclude any other integer or any other integers, unless in the context ofusage an exclusive interpretation of the term is required.
BRIEF DESCRIPTION OF THE DRAWINGS To describe the technical solutions of the present disclosure more clearly, the accompanying drawings required for describing the embodiments or the prior art are briefly described below. Apparently, the accompanying drawings in the following description show merely some embodiments of the present disclosure, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts. FIG. 1 is a structural block diagram of the present disclosure; and FIG. 2 is a schematic diagram showing an output spike-and-slow-wave complex according to the present disclosure.
DETAILED DESCRIPTION The technical solutions in the embodiments of the present disclosure are clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by a person skilled in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure. Embodiment 1 A processing and identification method for a spike-and-slow-wave complex in an EEG includes the following steps: Si: Input an original signal, and perform wavelet transform to obtain a wavelet coefficient according to the following formula: 00
WT.,(a,b)= X (t)0qa,b(t)dt)
where qa,b Ia 1/2 j(tb ), and
{aj = 2- =-bk 2-k}.
S2: Set a threshold, and perform denoising by using a wavelet method. Specifically, the threshold is set for the wavelet coefficient, and all wavelet coefficients less than the threshold are set to 0. S3: Perform inverse wavelet transform to obtain a denoised signal. S4: Find an extremum of the denoised signal, and decompose the original signal into 350 ms segments by using the extremum as a center. S5: An expert reads, selects, and intercepts N (for example, N = 100 herein) pieces of 250 ms spike-and-slow-wave complex data from training data. S6: Superpose, average, and fit 100 spike-and-slow-wave complexes to obtain a spike-and slow-wave complex template function f(t) according to the following formula:
f(t)=- X,(t) =- 1 S7: Construct a mother wavelet functionP(t) according to the following formula: T(t)=f(t) (ct2 +jpt + .
Coefficients a, P, and y are obtained by solving the following linear equations:
sa-e2d - c 2c a f f(t)dt c c2 C_ C_ 1- Y)A A0~
d2 d 1 f( d) where t is set to a value in [c, d], 0 < c < d < 250 ms,P(c)= 0, and T(d)= 0. S8: Perform wavelet transform to obtain a wavelet coefficient. S9: Set a threshold, detect a spike-and-slow-wave complex, and output a detection result. Embodiment 2 As shown in FIG. 1, a processing and detection apparatus for a spike-and-slow-wave complex in an EEG includes an EEG data importing module, a data denoising module, a data decomposition module, and a spike-and-slow-wave complex detection and output module. The EEG data importing module is configured to import to-be-identified EEG data. The data denoising module is configured to denoise the to-be-identified EEG data. The data decomposition module is configured to divide the original data into short segments to obtain a to-be-detected segment. The spike-and-slow-wave complex detection and output module is configured to detect a spike-and-slow-wave complex and output a result. A spike-and-slow-wave complex result output by the processing and detection apparatus for a spike-and-slow-wave complex in an EEG is shown in FIG. 2. The spike-and-slow-wave complex detection and output module constructs a template function by using a superposing and averaging method according to the following formula: 1 100 f (t) = ,i(t) 100 _i A mother wavelet function '(t) needs to be constructed according to the following formula before the spike-and-slow-wave complex is detected through wavelet transform: (t)=f(t)-(at2 _+ + y
Coefficients a, P, and y are obtained by solving the following linear equations: d3-c3 d2-c2 -d - c a fdf (t)dt c 2 c 1 f(c) d2 d 1 f(d) A wavelet coefficient is then obtained through wavelet transform, where an absolute value of the wavelet coefficient needs to be greater than a universal threshold estimated based on the wavelet coefficient; a maximum time difference between the EEG data and a wavelet extremum is less than or equal to a threshold defined based on experience, namely, 7.8 ms; and one spike and-slow-wave complex is output if the condition is satisfied. The above descriptions are merely preferred embodiments of the present disclosure, and should not be construed as excluding other embodiments. It should be understood that the present disclosure is not limited to the form disclosed herein, and can be used in other various combinations, modifications, and environments. Based on the above teachings or techniques or knowledge in related fields, modifications can be made within the scope of the concept described herein. Modifications and changes made by a person skilled in the art without departing from the spirit and scope of the present disclosure should fall within the protection scope of the appended claims of the present disclosure.
Claims (5)
1. A processing and identification method for a spike-and-slow-wave complex in an electroencephalogram (EEG), said method comprising the following steps: SI: inputting an original signal, and performing wavelet transform to obtain a wavelet coefficient; S2: setting a threshold, and performing denoising by using a wavelet method; S3: performing inverse wavelet transform to obtain a denoised signal; S4: decomposing the original signal into 350 ms segments by using an extremum of the denoised signal as a center; S5: selecting and intercepting N pieces of 250 ms spike-and-slow-wave complex data; S6: performing superposing, averaging, and fitting to obtain a spike-and-slow-wave complex template function f(t); S7: constructing a mother wavelet function'F(t); S8: performing wavelet transform to obtain a wavelet coefficient; and S9: setting a threshold, detecting a spike-and-slow-wave complex, and outputting a detection result.
2. The method according to claim 1, wherein in Sl, a method for applying wavelet transform to the signal with noise to obtain the wavelet coefficient that can be used to distinguish between the signal and the noise is as follows: 00
WTwx(a,b) = x (t)0 a,b(t)dt)
wherein qa,b -) 1-1/2 j(a), and{aj = 2-, bk = 2-k}.
3. The method according to claim 1, wherein in S2, the threshold is set for the wavelet coefficient, and all wavelet coefficients less than the threshold are set to 0; wherein in S4, a distance between two adjacent extremums needs to be greater than a set threshold, and if the distance is less than the set threshold, only an extremum with a maximum absolute value is retained, and to-be-processed data is divided into a plurality of wavelet segments; wherein N = 100 in S5; wherein in S6, a formula for superposing, averaging, and fitting 100 spike-and-slow-wave complexes to obtain the spike-and-slow-wave complex template function f(t) is as follows: n f (t)= Yxi (t) wherein n = 100, and x(t) presents an intercepted spike-and-slow-wave complex; wherein the mother wavelet function P(t) constructed in S7 is as follows: y(t)=f(t)-(at2 + Pt + y); and coefficients a, P, and y are obtained by solving the following linear equations: d3-C3 d2-c2 d 3 2d2 - a fc f f(t)dt c2 c 1 =Y) f(c) d2 d 1 f( d) wherein t is set to a value in [c, d], 0 < c < d < 250 ms,P(c)= 0, and T(d) = 0; wherein in S8, an absolute value of the wavelet coefficient needs to be greater than a universal threshold estimated based on the wavelet coefficient.
4. A processing and detection apparatus for a spike-and-slow-wave complex in an EEG, using the processing and identification method for a spike-and-slow-wave complex in an EEG according to claim 1, wherein the apparatus comprises an EEG data importing module, a data denoising module, a data decomposition module, and a spike-and-slow-wave complex detection and output module; the EEG data importing module is configured to import to-be-identified EEG data; the data denoising module is configured to denoise the to-be-identified EEG data; the data decomposition module is configured to divide the original data into short segments to obtain a to-be-detected segment; and the spike-and-slow-wave complex detection and output module is configured to detect a spike-and-slow-wave complex and output a result.
5. The apparatus according to claim 4, wherein the spike-and-slow-wave complex detection and output module constructs a template function by using a superposing and averaging method; 1 100 f (t) = -YXi(t) 100 i
a mother wavelet function P(t) needs to be constructed before the spike-and-slow-wave complex is detected through wavelet transform: (t)=f(t)-(at2 _p coefficients a, P, and y are obtained by solving the following linear equations: d3-C3 d2-c2 d 3 2 2 d - c a fc' f(t)dtan c2 c 1 = (c) ; and d2 d 1 wfe(d) a wavelet coefficient is then obtained through wavelet transform, wherein an absolute value of the wavelet coefficient needs to be greater than a universal threshold estimated based on the wavelet coefficient; a maximum time difference between the EEG data and a wavelet extremum is less than or equal to a threshold defined based on experience; and one spike-and slow-wave complex is output if the condition is satisfied.
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