Classification of ictal and seizure-free EEG signals using fractional linear prediction

Joshi, Varun; Pachori, Ram Bilas; Vijesh, V. Antony

doi:10.1016/j.bspc.2013.08.006

Cited by 236 publications

(62 citation statements)

References 23 publications

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“…They achieved classification accuracy of 94% in classifying seizure and seizure-free EEG signals. Epileptic seizure EEG signal classification has been performed using the fractional LP model in [18]. They computed signal energy and fractional LP error energy as features for the classification of seizure-free and seizure EEG signals with the SVM classifier and obtained 95.33% classification accuracy in classifying seizure and seizure-free EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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“…The least squares support vector machine (LS-SVM) provides the least squares solution of the optimization problem [45]. The classification of EEG signals of different classes has been studied using SVM classifier and LS-SVM classifier in [28,[46][47][48]. The radial basis function (RBF) [28] is used as a kernel in this work to form the decision boundary.…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

“…The radial basis function (RBF) [28] is used as a kernel in this work to form the decision boundary. The width of the RBF kernel function can be controlled using a parameter [47] denoted by σ 2 .…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Sharma

Pachori

Acharya

2015

Entropy

167

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The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student's t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.Entropy 2015, 17 5219

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“…Table II shows a comparison of the proposed method with previous methods tested using the same data, CHB-MIT database [24]. There are several other seizure detection methods such as [32][33] that have been tested using different dataset, namely University of Bonn dataset [34]. However, this dataset is very small and less comprehensive compare with CHB-MIT dataset.…”

Section: Resultsmentioning

confidence: 99%

E EG - Based Epileptic Seizures Detection with Adaptive Learning Capability

Ibrahim¹,

Majzoub²

2017

ijeei

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Epilepsy is considered one of the most common neurological disorders. Epileptic seizures can be a major life disability that might result in loss of consciousness, and/or injury to oneself or others. This research work aims to develop an epileptic seizure detection method using electroencephalography (EEG) signal analysis. We combine discrete wavelet transform (DWT), Shannon entropy, and statistical feature (standard deviation) to extract distinctive features of a given EEG signal. Knearest neighbors (KNN) automatically classifies the EEG signal by comparing the extracted features with the features of the normal and seizure baseline. Adaptive learning is used to continuously update the two baselines based the user feedback, if needed, to improve its performance over the time. Our proposed method achieved an overall 94.5 % sensitivity tested with up to 570 hours of continuous EEG recording from ten patients with total of 55 seizure events taken from CHB-MIT database. With its simplicity and fast processing time, the proposed method is suitable to be implemented in embedded device which has limited processing resource.

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Classification of ictal and seizure-free EEG signals using fractional linear prediction

Cited by 236 publications

References 23 publications

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

E EG - Based Epileptic Seizures Detection with Adaptive Learning Capability

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