CN110811609B - Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion - Google Patents
Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion Download PDFInfo
- Publication number
- CN110811609B CN110811609B CN201911033704.8A CN201911033704A CN110811609B CN 110811609 B CN110811609 B CN 110811609B CN 201911033704 A CN201911033704 A CN 201911033704A CN 110811609 B CN110811609 B CN 110811609B
- Authority
- CN
- China
- Prior art keywords
- spike
- template matching
- machine learning
- electroencephalogram
- spike detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
- 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
-
- 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
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Surgery (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Psychiatry (AREA)
- Animal Behavior & Ethology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Psychology (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention provides an intelligent epileptic spike detection device based on self-adaptive template matching and machine learning algorithm fusion, which comprises: an acquisition module: is used for collecting electroencephalogram signals; a preprocessing module: for data preprocessing; performing Butterworth band-pass filtering on the acquired original EEG data to obtain a standard EEG signal; self-adaptation template matching spike detection module: the system is used for executing adaptive template matching spike detection and obtaining a result; machine learning spike detection module: for performing machine learning spike detection and obtaining a result; an output module: and the detection result is fused and output.
Description
Technical Field
The invention relates to the field of computers, in particular to an intelligent epileptic spike detection device based on self-adaptive template matching and machine learning algorithm fusion.
Background
Epilepsy is a chronic disease in which sudden abnormal discharges in cerebral neurons lead to transient cerebral dysfunction. Over sixty five million people worldwide suffer from epilepsy, with about nine million people in chinese. Seizures are paroxysmal, repetitive and unpredictable, and may occur at any age.
An Electroencephalogram (EEG) is a potential signal generated by the discharge of neurons in the brain, reflects the rhythm and activity rule of bioelectricity in the brain, and contains a large amount of physiological and disease information. In clinical medicine, EEG signal processing can not only provide a diagnosis basis for some brain diseases, but also provide an effective treatment means for some brain diseases, and plays an important role in the detection of epilepsy.
Spike waves are typical epilepsy characteristic waveforms, are usually recorded in electroencephalograms, are sharp relative to background waveforms, have high amplitude and transient characteristics, and clinically, current epilepsy examination mainly identifies spike waves of electroencephalogram signals through human eye detection. At present, clinical examination of epilepsia electroencephalogram is mainly to identify spike waves in electroencephalogram signals through manual detection, but the efficiency is low, the subjectivity is strong, the accuracy of results cannot be guaranteed, and therefore the spike wave automatic detection technology receives more and more attention in recent years.
The method for identifying the spike waves is various, and a wavelet analysis method is commonly used for performing wavelet decomposition on time-frequency characteristics of epileptic electroencephalogram signals, and wavelet coefficients are used as input signals of a machine learning classifier and a neural network to detect the spike waves. However, because the characteristics of the mother wavelet and the spike wave are different, the background electroencephalogram inhibition is poor in the signals obtained by decomposition and reconstruction, and the spike wave extraction effect is not ideal. The method for detecting the singular point of the signal of the wavelet transform modulus maximum is another commonly used method for spike detection, but the method can only detect positive-phase spike and has higher false positive rate when detecting negative-phase spike. Morphological filtering is another approach to study spike extraction, which uses predefined structural elements to match signals based on their geometric features to extract signals with similar morphology. The method has the characteristics of easy algorithm gradual change, definite physical significance, practicality, effectiveness and the like, can decompose a signal containing complex components into parts with different physical significances, separate the signal from a background and keep the global or local main morphological characteristics of the signal, but a single opening-closing (OC) or closing-opening (CO) operation can cause a statistical bias phenomenon, so that the detected spike and an actual spike have certain deviation on the waveform and the position.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent epileptic spike detection device based on the fusion of adaptive template matching and a machine learning algorithm, so as to improve the identification rate of epileptic spikes.
In order to achieve the purpose, the invention is realized by the following scheme:
epilepsy spike intelligent detection device based on adaptive template matching and machine learning algorithm fusion comprises the following modules:
the brain electricity collection equipment: is used for collecting electroencephalogram signals; selecting an experimental object, acquiring electroencephalogram data of an epileptic patient by using electroencephalogram acquisition equipment, and establishing an experimental database;
a preprocessing module: for data preprocessing; performing Butterworth band-pass filtering on the acquired original EEG data to obtain a standard EEG signal;
self-adaptation template matching spike detection module: the system is used for executing adaptive template matching spike detection and obtaining a result; firstly, defining a universal template according to the waveform characteristics of the epileptic spike, and carrying out universal template matching to obtain a candidate spike signal; then clustering the candidate spikes by using a K-means algorithm to obtain a plurality of classes; counting the number of candidate spikes in each class, and if the number of spikes is less than 5% of the total number of spikes, rejecting the class; respectively using the screened class centers as new templates to perform self-adaptive template matching, and adding all matching results to obtain spike detection results;
machine learning spike detection module: for performing machine learning spike detection and obtaining a result; firstly, segmenting a brain electrical signal into electroencephalogram segments with the length of 1s, then extracting time domain and frequency domain features in each electroencephalogram segment, and constructing spike feature vectors; training a random forest classification model by using the feature vectors to obtain a spike detection result based on machine learning;
a fusion module: fusing the detection results; if the adaptive template matching spike detection module and the machine learning spike detection module detect spike signals in the same segment, the final result is recorded as the existence of the spike signals in the segment, and the segment is regarded as epileptic spike;
the invention also discloses an intelligent epileptic spike detection method based on the fusion of adaptive template matching and a machine learning algorithm, which comprises the following steps:
step S1: acquiring electroencephalogram signals, selecting experimental objects, establishing an epilepsia electroencephalogram database, and marking spike waves in each channel of the electroencephalogram signals;
step S2: preprocessing the EEG signal, and removing high-frequency components and artifacts by using a 5-order Butterworth band-pass filter.
And step S3: and (3) detecting the epileptic spike by adopting a self-adaptive template matching method.
And step S4: and detecting the epileptic spike by adopting a machine learning method.
Step S5: and fusing the detection results of the step S3 and the step S4 to obtain a final spike detection result.
According to an embodiment of the invention, the sampling frequency in step S1 is 500Hz, and a large amount of electroencephalogram data is required to be taken as an experimental sample, and the experimental subject includes people of different sexes and different ages.
According to an embodiment of the invention, in the adaptive template matching spike detection process, morphological characteristics such as rising edge slope, falling edge slope, amplitude and duration of manually marked spikes are counted to establish a universal spike template.
According to an embodiment of the present invention, in the process of detecting epilepsy spike by using an adaptive template matching method, the method includes:
s31, counting the characteristics of rising edge slope, falling edge slope, amplitude height, duration and the like of a spike waveform in electroencephalogram data, and defining a universal template;
step S32, setting the window width to be 300, and carrying out general template matching operation on the electroencephalogram signals according to the time sequence to obtain candidate spike signals;
s33, performing K-means clustering on the candidate spikes, and dividing the candidate spikes into different classes according to different waveforms;
step S34, counting the number of candidate spikes in each spike cluster, if the number is less than 5% of the total number of the candidate spikes, rejecting the class, and finally taking the centroid of the rest classes as a new template;
and S35, respectively using the mass center of each class as a template to perform new template matching, and superposing the results to obtain a spike detection result.
According to an embodiment of the present invention, the K-means clustering includes:
step S331: randomly selecting k samples in a sample set as an initial clustering centroid;
step S332: and calculating the distance between each sample and the initial centroid, reclassifying according to the minimum distance, and classifying each candidate spike into the class of the centroid closest to the candidate spike to obtain a clustering result.
Step S333: and averaging the obtained samples in each class to be used as the centroid of the next clustering.
Step S334: and repeating the steps S332 and S333 until the position of the centroid is not changed any more and the clustering is finished.
According to an embodiment of the present invention, in the process of detecting epilepsy spike by using a machine learning method, the method includes:
step S41: the electroencephalogram signal is divided into single-channel segments with the length of 1s, if the segments have spikes, the segments are marked as 1, and if the segments do not have spikes, the segments are marked as 2. And respectively extracting time domain characteristics and frequency domain characteristics of the electroencephalogram fragments, and constructing an epilepsy spike characteristic vector with strong robustness.
Step S42: and randomly dividing the feature vectors into a training set and a testing set, and training a plurality of decision trees in a random forest classifier by using a plurality of electroencephalogram signal samples in the training set to form a random forest model.
Step S43: and inputting the data in the test set into the trained random forest model to obtain a spike detection result based on the machine learning method.
According to an embodiment of the invention, in the training of the random forest model, the method comprises the following steps:
step S421: the new training set with the same number of samples as the training set is extracted and put back in the training sample set.
Step S422: randomly sampling without playback in the feature vector set to form a feature vector set to be selected;
step S423: and (4) according to the candidate feature training set obtained in the step (S422), calculating the optimal splitting mode of each node and splitting the node without pruning until the impurity degree of each leaf node reaches the specified requirement to form a decision tree.
Step S424: and repeating the steps S421 to S423 until all decision trees are generated and integrated to obtain the random forest model.
By adopting the technical scheme of the invention, the epileptic spike detection is carried out by fusing the self-adaptive template matching and the machine learning, so that the identification rate of the epileptic spike is greatly improved.
Drawings
Fig. 1 is a general flowchart of the intelligent epileptic spike detection device based on adaptive template matching and machine learning algorithm fusion according to the present invention.
FIG. 2 is a flow chart of adaptive template matching spike detection according to the present invention.
FIG. 3 is a K-means clustering flow chart of the present invention.
FIG. 4 is a flowchart illustrating a machine learning spike detection process according to the present invention.
FIG. 5 is a flow chart of the training of a random forest model for machine learning spike detection in accordance with the present invention.
Detailed Description
Electroencephalogram signals generally contain a lot of physiological information about human diseases, and especially play an important role in the aspect of epilepsy detection. The electroencephalogram signals contain many epileptic characteristic waves, and spike waves are typical waveforms in the electroencephalogram signals. Therefore, spike detection is required to be carried out on the epileptic brain electrical signals for better research. The existing spike method is difficult to completely and accurately determine the spike position, so that the research on epileptic diseases is greatly influenced. In view of this, the present embodiment provides an intelligent epileptic spike detection method based on adaptive template matching and machine learning algorithm fusion.
In order to further highlight the objects, implementations and novel features of the present invention, the present invention will be further described in detail with reference to the accompanying drawings and embodiments.
Fig. 1 is a general flowchart of an intelligent epileptic spike detection device based on adaptive template matching and machine learning algorithm fusion, which includes the following modules:
the electroencephalogram acquisition equipment comprises: is used for collecting electroencephalogram signals; selecting an experimental object, acquiring electroencephalogram data of an epileptic patient by using electroencephalogram acquisition equipment, and establishing an experimental database;
a preprocessing module: for data preprocessing; performing Butterworth band-pass filtering on the acquired original EEG data to obtain a standard EEG signal;
self-adaptation template matching spike detection module: the adaptive template matching spike detection module is used for executing adaptive template matching spike detection and obtaining a result; firstly, defining a universal template according to the waveform characteristics of the epileptic spike, and carrying out universal template matching to obtain a candidate spike signal; then clustering the candidate spikes by using a K-means algorithm to obtain a plurality of classes; counting the number of candidate spikes in each class, and if the number of spikes is less than 5% of the total number of spikes, rejecting the class; respectively using the screened class centers as new templates to perform self-adaptive template matching, and adding all matching results to obtain spike detection results;
machine learning spike detection module: for performing machine learning spike detection and obtaining a result; firstly, segmenting a brain electrical signal into electroencephalogram segments with the length of 1s, then extracting time domain and frequency domain features in each electroencephalogram segment, and constructing spike feature vectors; training a random forest classification model by using the feature vectors to obtain a spike detection result based on machine learning;
a fusion module: fusing the detection results; if the adaptive template matching spike detection module and the machine learning spike detection module detect spike signals in the same segment, the final result is recorded as the existence of the spike signals in the segment, and the segment is regarded as epileptic spike;
the intelligent epileptic spike detection device based on adaptive template matching and machine learning algorithm fusion proposed in the present embodiment is described in detail below with reference to fig. 1 to 5. The technical scheme of the invention comprises the following steps:
step S1: acquiring an electroencephalogram signal: selecting an experimental object, acquiring electroencephalogram data of an epileptic by using electroencephalogram acquisition equipment, and establishing an experimental database;
step S2: data preprocessing: carrying out Butterworth band-pass filtering on the acquired original EEG data to obtain a standard EEG signal;
and step S3: self-adaptive template matching spike detection: firstly, defining a universal template according to waveform characteristics of epileptic spikes, and carrying out universal template matching to obtain candidate spike signals; then clustering the candidate spikes by using a K-means algorithm to obtain a plurality of classes; counting the number of candidate spikes in each class, and if the number of spikes is less than 5% of the total number of spikes, rejecting the class; and respectively using the screened class centers as new templates to perform self-adaptive template matching, and adding all matching results to obtain spike detection results.
And step S4: machine learning spike detection: firstly, segmenting a brain electrical signal into electroencephalogram segments with the length of 1s, then extracting time domain and frequency domain characteristics in each electroencephalogram segment, and constructing spike characteristic vectors; and training a random forest classification model by using the feature vectors to obtain a spike detection result based on machine learning.
Step S5: and (3) fusing detection results: and fusing the S3 spike detection method and the S4 spike detection method, and if the spike is detected as the spike by the S3 and the S4 at the same time, regarding the spike as the epileptic spike.
The intelligent epilepsia spike detection device based on the self-adaptive template matching and machine learning algorithm fusion starts in step S1, a multi-lead electroencephalograph is used for collecting long-range monitoring electroencephalograms of a patient in the step, the sampling frequency is 500Hz, the electrode distribution adopts the international 10-20 electroencephalogram collection standard, 19-channel electroencephalogram data are collected in total, and a large number of electroencephalograms of experimental bodies of different genders and different ages are collected to obtain a plurality of electroencephalogram signal samples. A plurality of electroencephalogram signal samples are marked by a professional doctor, and spike waveforms in each channel of the electroencephalogram signals are marked.
And then, executing the step S2 to carry out preprocessing operation on the electroencephalogram. A5-order Butterworth band-pass filter is adopted to filter frequency components above 32Hz and below 0.5Hz, and the interference of noise and artifacts is reduced.
And S3, carrying out self-adaptive template matching on the electroencephalogram signals subjected to the preprocessing operation to obtain a spike detection result. The adaptive template matching spike detection method will be described in detail below with reference to fig. 2.
Firstly, statistical analysis is carried out on spike waveforms marked in the electroencephalogram signal, and the average values of the rising edge slope, the falling edge slope, the peak value and the duration of all marked spike waveforms are respectively obtained and used as standards to establish a universal template (step S31). Then, setting the window width to 300, and performing a general template matching operation on the electroencephalogram signals in time sequence to obtain candidate spike signals (step S32). And performing K-means clustering on the candidate spikes, and dividing the candidate spikes into different classes according to different waveforms (step S33). And (5) counting the number of candidate spikes in each spike cluster, if the number is less than 5% of the total number of the candidate spikes, rejecting the class, and finally taking the centroid of the rest classes as a new template (step S34). And (5) respectively using the centroid of each class as a template to perform new template matching, and superposing the results to obtain a spike detection result (step S35).
As shown in fig. 3, the process of K-means clustering is as follows:
k samples are randomly selected among the n candidate spikes as an initial cluster centroid (step S331). And calculating the distance between each candidate spike and each initial centroid, reclassifying according to the minimum distance, and classifying each candidate spike into the class of the closest centroid to obtain a clustering result (step S332). The candidate spikes in each cluster are averaged as the centroid of the next cluster (step S333). And repeating the steps S332 and S333 until the position of the centroid is not changed any more or the clustering frequency reaches the requirement, finishing clustering and obtaining a clustering result (step S334). In this embodiment, the number of initial centroids k = n, that is, each candidate spike is clustered as one centroid, and finally n clustering results are obtained.
And S4, performing spike extraction on the preprocessed electroencephalogram signals by adopting a machine learning method. The machine learning spike detection method will be described in detail below with reference to fig. 3.
Firstly, segmenting the electroencephalogram signal of each channel into segments with the length of 1S, extracting a plurality of characteristic parameters of each segment, wherein the characteristic parameters comprise time domain characteristic parameters and frequency domain characteristic parameters, and constructing a characteristic vector corresponding to each electroencephalogram segment (step S41). The feature vectors are then divided into a training set and a test set, and the random forest classification model is trained using the data in the training set (step S42). The data in the test set is input into the random forest model, and the output result obtained after voting is the spike detection result, so that whether a spike exists in the segment can be detected (step S43).
The electroencephalogram segment obtained by segmentation in the step S41 is recorded as x (N), N =1,2, …, and N, N is the length of the electroencephalogram segment, and the sampling frequency of the electroencephalogram signal is 500Hz in the invention, so that N =500. Extracting by wavelet packet transformation before feature extractionTaking rhythm waves, because the spike wave frequency range is above 14Hz, using db6 wavelet basis function to decompose and reconstruct signals to obtain beta wave and gamma wave, respectively recording as x 1 (n) and x 2 (n)。
The time domain characteristic parameters extracted in the step S41 comprise an original electroencephalogram signal x (n) and two rhythm wave signals x 1 (n) and x 2 (n) minimum, maximum, mean, standard deviation, kurtosis, skewness, and Hjorth parameters. The minimum value Min and the maximum value Max are respectively the maximum value of the signal amplitude, the average value Mean is the electroencephalogram signal amplitude trend, and the formula is as follows:
the standard deviation SD reflects the difference between the amplitude and the average value of each sample point, and the formula is as follows:
wherein x (N) is the electroencephalogram signal, N is the number of sampling points of x (N), and is the average value of the amplitudes of all sampling points in x (N).
Kurtosis Kur represents the peak level of the data frequency distribution curve, as follows:
skewness Skaew represents the characteristic of amplitude asymmetry degree of the electroencephalogram signals, and the formula is as follows:
the Hjorth parameters include Hjorth mobility and Hjorth complexity:
hjorth mobility can be represented by the following equation:
the Hjorth complexity can be represented by the following equation:
wherein
dnf n ＝x(n)-x(n-1)。
The frequency domain characteristic parameters extracted in step S41 comprise the energy E of two rhythm waves i Two rhythm wave energy to total signal energy ratio R i 。
Extracting rhythm wave by wavelet packet transformation, performing five-layer wavelet decomposition on the EEG signal by using db6 wavelet function to obtain beta wave and gamma wave, which are respectively marked as x 1 (n) and x 2 (n)。
Two rhythm wave energy E i Obtained from the following equation:
total energy E of signal all The formula of (1) is as follows:
further, the energy ratio, R, of the rhythm wave can be calculated i ＝E i /E all ，i＝1，2。
Step S42 is a training process of the random forest model, and the random forest classifier includes a plurality of decision trees, and its output category is determined by the maximum votes in the results of all the trees. And repeatedly and randomly selecting M samples from the M samples in the original training sample set by using a bootstrap resampling technology to generate a new training sample set, and then generating a random forest by using M individual decision tree classifiers. The essence of the random forest classifier is an improvement on a decision tree algorithm, a plurality of decision trees are combined together, and each tree is established by independent randomly extracted samples. Each tree in the forest has the same distribution, and the classification error depends on the classification capability of each tree and the relevance of each tree.
The data set is divided into a training set and a testing set, and the random forest model training process is described below with reference to fig. 5:
step S421: firstly, M times of samples with the return are adopted from all the feature vector sets to form a feature set to be selected, and the number of samples in the feature set to be selected is the same as that of the samples in the original feature vector set.
Step S422: secondly, randomly selecting a certain number of feature vectors from the features to be selected, and selecting the optimal features.
Step S423: and (4) according to the candidate feature training set obtained in the step (S422), calculating the optimal splitting mode of each node and splitting the node without pruning until the impurity degree of each leaf node reaches the specified requirement to form a decision tree.
Step S424: and repeating the steps S421 to S423 until all the decision trees stop growing, and generating a random forest.
And S43, inputting the electroencephalogram data in the test set into a random forest model, obtaining a spike wave detection result after voting selection by a decision tree, determining an electroencephalogram segment where a spike wave is located, and further determining an electroencephalogram channel where the spike wave is located and a time point.
In step S5, the self-adaptive template matching is performed on the data in the test set to obtain a spike detection result. And meanwhile, inputting the test set into a random forest model for classification to obtain a spike detection result. The results of the two methods are then fusion compared and if detected by both methods simultaneously as a spike, this is considered an epileptic spike.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core idea. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. Epilepsia spike wave intelligent detection device based on self-adaptation template matching and machine learning algorithm fusion, its characterized in that includes following module:
the brain electricity collection equipment: is used for collecting electroencephalogram signals; selecting an experimental object, acquiring electroencephalogram data of an epileptic patient by using electroencephalogram acquisition equipment, and establishing an experimental database;
a preprocessing module: for data preprocessing; performing Butterworth band-pass filtering on the acquired original EEG data to obtain a standard EEG signal;
self-adaptation template matching spike detection module: the system is used for executing adaptive template matching spike detection and obtaining a result; firstly, defining a universal template according to waveform characteristics of epileptic spike, and performing universal template matching to obtain a candidate spike signal; then clustering the candidate spikes by using a K-means algorithm to obtain a plurality of classes; counting the number of candidate spikes in each class, and if the number of spikes is less than 5% of the total number of spikes, rejecting the class; respectively using the screened class centers as new templates to perform self-adaptive template matching, and adding all matching results to obtain spike detection results;
machine learning spike detection module: for performing machine learning spike detection and obtaining a result; firstly, segmenting a brain electrical signal into electroencephalogram segments with the length of 1s, then extracting time domain and frequency domain features in each electroencephalogram segment, and constructing spike feature vectors; training a random forest classification model by using the feature vectors to obtain a spike detection result based on machine learning;
a fusion module: for fusing the detection results; the method comprises the following steps that a spike detection result obtained by an adaptive template matching spike detection module and a spike detection result obtained by a machine learning spike detection module are fused, if the adaptive template matching spike detection module and the machine learning spike detection module detect spike signals in the same segment, the final result is recorded as the existence of the spike signals in the segment, and the segment is regarded as epileptic spike;
wherein the adaptive template matching spike detection module performs:
s31, counting the rising edge slope, the falling edge slope, the amplitude height and the duration of a spike waveform in the electroencephalogram data, and defining a universal template;
step S32, setting the window width to be 300, and carrying out general template matching operation on the electroencephalogram signals according to the time sequence to obtain candidate spike signals;
s33, performing K-means clustering on the candidate spikes, and dividing the candidate spikes into different classes according to different waveforms;
step S34, counting the number of candidate spikes in each spike cluster, if the number is less than 5% of the total number of the candidate spikes, rejecting the class, and finally taking the centroid of the rest classes as a new template;
step S35, carrying out new template matching by respectively using the mass center of each class as a template, and superposing the results to obtain a spike detection result;
the machine learning spike detection module performs:
s41, dividing each channel of the electroencephalogram signal into segments with the length of 1S, extracting time domain characteristics and frequency domain characteristics of each segment, and constructing a characteristic vector of each electroencephalogram segment;
s42, dividing the characteristic vectors into a training set and a test set, and training a random forest classification model by using data in the training set;
and S43, inputting the data in the test set into the random forest model, wherein the obtained output result is a spike detection result, and whether a spike exists in the segment can be detected.
2. The intelligent epileptic spike detection device based on adaptive template matching fused with a machine learning algorithm as claimed in claim 1, characterized in that in data preprocessing, a 5 th order IIR butterworth band pass filter with frequency range of 0.5-32Hz is used to remove noise and artifacts in EEG signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911033704.8A CN110811609B (en) | 2019-10-28 | 2019-10-28 | Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911033704.8A CN110811609B (en) | 2019-10-28 | 2019-10-28 | Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110811609A CN110811609A (en) | 2020-02-21 |
| CN110811609B true CN110811609B (en) | 2022-11-22 |
Family
ID=69551174
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911033704.8A Active CN110811609B (en) | 2019-10-28 | 2019-10-28 | Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110811609B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111643076A (en) * | 2020-05-13 | 2020-09-11 | 杭州电子科技大学 | BECT spike intelligent detection method based on multi-channel electroencephalogram signals |
| CN112244871A (en) * | 2020-09-25 | 2021-01-22 | 吉林大学 | Amplitude integration electroencephalogram classification recognition system based on machine learning |
| CN112568868B (en) * | 2020-10-16 | 2022-12-23 | 苏州赛美科基因科技有限公司 | Automatic quantitative analysis method and device for electrophysiological signals of epilepsy model |
| CN112336354A (en) * | 2020-11-06 | 2021-02-09 | 山西三友和智慧信息技术股份有限公司 | Epilepsy monitoring method based on EEG signal |
| CN112641449A (en) * | 2020-12-18 | 2021-04-13 | 浙江大学 | EEG signal-based rapid evaluation method for cranial nerve functional state detection |
| CN112270314B (en) * | 2020-12-22 | 2021-09-21 | 苏州国科康成医疗科技有限公司 | Spike wave identification method and device, electronic equipment and computer readable storage medium |
| CN113197585B (en) * | 2021-04-01 | 2022-02-18 | 燕山大学 | Neuromuscular information interaction model construction and parameter identification optimization method |
| CN113397567B (en) * | 2021-05-19 | 2023-03-21 | 中国航天科工集团第二研究院 | Human behavior electroencephalogram signal classification method and system |
| CN114081508B (en) * | 2021-10-28 | 2024-05-14 | 杭州电子科技大学 | Spike detection method based on fusion of deep neural network and CCA (common cancer cell) characteristics |
| CN113974653B (en) * | 2021-11-30 | 2024-03-22 | 杭州妞诺霄云大数据科技有限公司 | Method and device for detecting optimized spike based on about log index, storage medium and terminal |
| CN114027854A (en) * | 2021-12-01 | 2022-02-11 | 杭州电子科技大学 | BECT spike detection method based on optimal template matching and morphological feature extraction |
| CN114145755B (en) * | 2021-12-21 | 2023-09-01 | 上海理工大学 | Household epileptic seizure interactive intelligent monitoring system and method |
| CN114532994B (en) * | 2022-03-23 | 2023-07-28 | 电子科技大学 | Automatic detection method for unsupervised electroencephalogram high-frequency oscillation signals based on convolution variation self-encoder |
| CN115299946B (en) * | 2022-08-25 | 2024-05-28 | 电子科技大学 | Self-adaptive input signal channel screening circuit introducing detection result feedback |
| CN115670397B (en) * | 2022-11-17 | 2023-06-02 | 北京中科心研科技有限公司 | PPG artifact identification method and device, storage medium and electronic equipment |
| CN117338244A (en) * | 2023-07-17 | 2024-01-05 | 博睿康医疗科技(上海)有限公司 | Abnormal discharge enhancement method based on space-time domain template |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108962391A (en) * | 2018-05-02 | 2018-12-07 | 杭州电子科技大学 | Epileptics prediction technique early period based on wavelet packet character and random forest |
| CN110338786A (en) * | 2019-06-28 | 2019-10-18 | 北京师范大学 | A kind of identification of epileptiform discharges and classification method, system, device and medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101259016A (en) * | 2007-03-06 | 2008-09-10 | 李小俚 | Method for real time automatically detecting epileptic character wave |
| CN106137185A (en) * | 2016-06-21 | 2016-11-23 | 华南理工大学 | A kind of epileptic chracter wave detecting method based on structure of transvers plate small echo |
| US10398385B2 (en) * | 2016-11-21 | 2019-09-03 | International Business Machines Corporation | Brain wave processing for diagnosis of a subject |
-
2019
- 2019-10-28 CN CN201911033704.8A patent/CN110811609B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108962391A (en) * | 2018-05-02 | 2018-12-07 | 杭州电子科技大学 | Epileptics prediction technique early period based on wavelet packet character and random forest |
| CN110338786A (en) * | 2019-06-28 | 2019-10-18 | 北京师范大学 | A kind of identification of epileptiform discharges and classification method, system, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110811609A (en) | 2020-02-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110811609B (en) | Epileptic spike intelligent detection device based on self-adaptive template matching and machine learning algorithm fusion | |
| CN110338786B (en) | Epileptic discharge identification and classification method, system, device and medium | |
| CN110432898A (en) | A kind of epileptic attack eeg signal classification system based on Nonlinear Dynamical Characteristics | |
| CN109758145B (en) | Automatic sleep staging method based on electroencephalogram causal relationship | |
| Kumari et al. | Seizure detection in EEG using time frequency analysis and SVM | |
| CN112741638B (en) | Medical diagnosis auxiliary system based on EEG signal | |
| Boubchir et al. | A review of feature extraction for EEG epileptic seizure detection and classification | |
| CN109602417A (en) | Sleep stage method and system based on random forest | |
| Mahesh et al. | ECG arrhythmia classification based on logistic model tree | |
| Wu et al. | HFO detection in epilepsy: a stacked denoising autoencoder and sample weight adjusting factors-based method | |
| Toulni et al. | Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier | |
| CN111887811B (en) | Brain abnormal discharge detection method and system based on electroencephalogram signal characteristics | |
| CN111643076A (en) | BECT spike intelligent detection method based on multi-channel electroencephalogram signals | |
| Kaur et al. | Multi-class support vector machine classifier in EMG diagnosis | |
| Hussain et al. | An intelligent system to classify epileptic and non-epileptic EEG signals | |
| Awang et al. | Analysis of EEG signals by eigenvector methods | |
| Biran et al. | Automatic qrs detection and segmentation using short time fourier transform and feature fusion | |
| Cheng et al. | Multiview feature fusion representation for interictal epileptiform spikes detection | |
| Liu et al. | Diagnosis of AF based on time and frequency features by using a hierarchical classifier | |
| CN113974653A (en) | Optimized spike detection method and device based on Joyston index, storage medium and terminal | |
| CN113208633A (en) | Emotion recognition method and system based on EEG brain waves | |
| Zhang et al. | Evaluation of single-lead ECG signal quality with different states of motion | |
| Sanghavi et al. | Detection of atrial fibrillation in electrocardiogram signals using machine learning | |
| Duque-Muñoz et al. | Stochastic relevance analysis of epileptic EEG signals for channel selection and classification | |
| Zehir et al. | Support vector machine for human identification based on non-fiducial features of the ecg |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |