Relative wavelet energy and wavelet entropy based epileptic brain signals classification

Kumar, Yatindra; Dewal, M. L.; Anand, R. S.

doi:10.1007/s13534-012-0066-7

Cited by 41 publications

(20 citation statements)

References 34 publications

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“…In this study, the entropy of the wavelet coefficients (denoted as wavelet entropy) with different scales is calculated to reveal more information carried by the DWT coefficients. Wavelet entropy measures the regularity degree of signals and indicates the dynamical characteristics of nonlinear systems [34]. Detailed information and approximations can be obtained from the given EEG time series X by applying multilevel wavelet decomposition using the db4 wavelet.…”

Section: Feature Extraction Via Nonlinear Analysismentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

216

106

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Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

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Section: Feature Extraction Via Nonlinear Analysismentioning

confidence: 99%

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Wang

Xue

et al. 2017

Entropy

216

106

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“…According to Lina Wang et al [5], multiresolution analysis (MRA) of feature engineering produced better EEG signal processing results. Wavelet Energy and entropy are considered as the prime features for wavelet analysis as reported by Yatindra et al [8].…”

Section: Wavelet Transformmentioning

confidence: 99%

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Martin¹,

Sujatha²,

Swapna³

2018

IJCA

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In biomedical engineering, many attempts are being reported over the years for automated diagnosis of various brain disorders by classifying EEG (Electroencephalography) signals. Various machine learning algorithms are adopted to address different scenarios in EEG classifications. Feature engineering is playing a vital role in order to enhance the classification efficiency particularly in signal processing applications. This paper elucidates multi-resolution analysis (MRA) of feature engineering and demonstrates how the distinctive features are being engineered in wavelet domain. The implementation results are placed in the form of feature distribution diagrams and provide clear indications in feature selection for epilepsy seizure detection through classification.

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“…The data used is artifact free EEG time series data and is widespreadly used by the ongoing research on epilepsy [1,3,7,15,17,20]. The complete data set contains five sets marked as (A-E) each set has 100 single channel EEG segment having duration of 23.6-sec.…”

Section: Clinical Datamentioning

confidence: 99%

“…Support vector machine (SVM) has been used in several EEG signal classification problems [17,20] and was first introduced in 1995. SVMs belong to the family of kernel-based classifiers and are extremely powerful classifiers.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Classification of patient by analyzing EEG signal using DWT and least square support vector machine

Zuhair¹,

Thomas²

2017

Adv. sci. technol. eng. syst. j.

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Epilepsy is a neurological disorder which is most widespread in human DWT decomposes the signal into approximation and detail coefficients, the ApEn values the coefficients were computed using pattern length (m= 2 and 3) as an input feature for the Least square support vector machine (LS-SVM). The classification is done using LS-SVM and the resultswere compared using RBF and linear kernels. The proposed model has used the EEG data consisting of 5 classes and compared with using the approximate and detailed coefficients combined and individually. The classification accuracy of the LS-SVM using the RBF and Linear kernel with ApEn using different cases is compared and it is found that the best accuracy percentage is 100% with RBF kernel.

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Relative wavelet energy and wavelet entropy based epileptic brain signals classification

Cited by 41 publications

References 34 publications

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

Classification of patient by analyzing EEG signal using DWT and least square support vector machine

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