Automated detection of abnormal EEG signals using localized wavelet filter banks

• Published in: Pattern recognition letters Vol. 133; pp. 188 - 194
• Main Authors: Sharma, Manish, Patel, Sohamkumar, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier Science Ltd
• Subjects: Automation; Central nervous system; Classification; Classifiers; Datasets; EEG; Electroencephalography; Energy localization; Entropy; Epilepsy; Feature extraction; Filter banks; Fractal geometry; Inspection; Seizures; Support vector machine (SVM); Support vector machines; Visual observation; Visual signals; Wavelet-based features; Support vector machine (SVM); Energy localization; EEG; Wavelet-based features
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2020.03.009

•Wavelet-based automated detection of abnormal EEG signals.•The largest publicly available dataset used.•High classification performance•Small number of features used. Epilepsy is a neural disorder that is associated with the central nervous system (CNS) in which the brain activity sometimes becomes abnormal, which may lead to seizures, loss of awareness, unusual sensations, and behavior. Electroencephalograms (EEG) are widely used to detect epilepsy accurately. However, the interpretation of a particular type of abnormality using the EEG signal is a subjective affair and may vary from clinician-to-clinician. Visual inspection of the EEG signal by observing a change in frequency or amplitude in long-duration signals is an arduous task for the clinicians. It may lead to an erroneous classification of EEGs. The proposed methodology focuses on automated detection of epilepsy using a novel stop-band energy (SBE) minimized orthogonal wavelet filter bank. Using the wavelet decomposition, we obtain subbands (SBs) of EEG signals. Subsequently, fuzzy entropy, logarithmic of the squared norm, and fractal dimension are computed for each SB. The different combinations of the extracted features were supplied to various classifiers for the classification of normal and abnormal EEG signals. In the proposed method, we have used a single-channel EEG dataset of Temple University Hospital. The dataset is the most substantial EEG data publicly available, which contains an EEG recording of 2130 distinct subjects. Our proposed system obtained the highest classification accuracy (CACC) of 78.4% and 79.34% during training and evaluation using the SVM classifier. We achieved the highest F1-score of 0.88.