<scp>E</scp>pileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine

Chen, Shanen; Zhang, Xi; Zhixian, Yang

doi:10.1002/ima.22240

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…It contains classification methods such as support vector [ 19 ], naive Bayes [ 20 ], neural network [ 21 ], and fuzzy logic system [ 22 ]. It also includes principal component analysis (PCA) [ 23 ], wavelet packet decomposition (WPD) [ 24 ], and the higher order crossings (HOC) [ 25 ]. These methods first feature extraction from the original features.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Sun

Chen

2022

Oxidative Medicine and Cellular Longevity

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Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Sun

Chen

2022

Oxidative Medicine and Cellular Longevity

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“…The frequency of the detail signals d1 and d2 is outside the frequency range of EEG signals during epileptic seizures. According to our previous study [12], detail signals with 3, 4, and 5 channels could well represent the characteristics of EEG signals during seizures while keeping high real-time performance of the algorithm. Thus, we use the channels 3, 4, and 5 (d3, d4, and d5) to calculate entropy features in the following section.…”

Section: B Signal Pre-processingmentioning

confidence: 99%

“…include linear kernel, polynomial kernel, multilayer perceptron (MLP) kernel and radial basis function (RBF) kernel. This study uses the RBF kernel, which has been approved to produce the best results in seizure detection [12]. The mathematical representation of the RBF kernel is as follows:…”

Section: E Classification and Cross Validation 1) Least Squares Support Vector Machine (Ls-svm)mentioning

confidence: 99%

“…As for seizure detection, clinically, it is critical to decide whether or not a patient is in normal, ictal or interictal seizure activities through EEG signal monitoring. At present, the general method for seizure detection of epileptic activity is mainly based on visual inspection of huge amounts of EEG signals by physicians, which commonly is time consuming and error prone [11], [12]. Therefore, a robust and reliable computer-aided automatic seizure detection system must be developed to discriminate normal (period do not contain any seizure), ictal (actual seizure period), and interictal (period between seizures) activities in EEG signals [13].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Diagnosis of Epileptic Seizure in Electroencephalography Signals Using Nonlinear Dynamics Features

et al. 2019

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Epilepsy is one of the most common neurological disorders generally characterized by sudden and recurrent unprovoked seizures, which is commonly diagnosed through visual inspection of electroencephalography (EEG) signals. However, since EEG signals are highly complex, nonlinear and nonstationary in nature, both visual inspection and the existing computed aid detection approaches fail to capture the intrinsic dynamics of seizure events, leading to unsatisfactory detection performance. Therefore, an accurate computer-aided automatic epileptic seizure diagnosis system is valuable and urgently needed in clinical application. In this study, we propose an automatic classification method using nonlinear dynamics features and nonlinear classifiers for determining whether the EEG signals are in interictal or ictal activities, respectively. The main novelty of this paper is that investigating the performance of discernable entropybased nonlinear dynamics features used for automatic diagnosis of an epileptic seizure. First, the discrete wavelet transform is applied to obtain de-noised and sub-band signals. Second, the nonlinear dynamics features are extracted based on information entropy theory. Subsequently, one-way analysis of variance and forward sequential feature selection technique are employed to identify the most explainable features for seizure detection. Then the selected features are fed to six different classifiers, namely, least squaresupport vector machine (LS-SVM), K-nearest neighbors, logistic regression, linear discriminant analysis, Naive Bayes classifier, and random forest, for automatic diagnosis of the epileptic seizure. Finally, a public EEG dataset from Bonn University, Germany, is utilized to investigate the performance of the proposed method. The experimental results show that the LS-SVM yields the optimal performance with an accuracy of 99.50%, a sensitivity of 100.00%, and a specificity of 99.40%. Our proposed method shows great potential for real-time diagnosis of the epileptic seizures, which can be deployed in hospitals to assist clinicians in accurate diagnosis of seizures.

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Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Nagabushanam

George

Dolly

et al. 2020

Circuit Theory & Apps

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Electroencephalogram (EEG) and its sub-bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG-like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi-orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

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Epileptic seizure detection by combining robust‐principal component analysis and least square‐support vector machine

Cited by 7 publications

References 23 publications

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Automatic Diagnosis of Epileptic Seizure in Electroencephalography Signals Using Nonlinear Dynamics Features

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

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