Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals

• Published in: Information fusion Vol. 96; pp. 252 - 268
• Main Authors: Tasci, Irem, Tasci, Burak, Barua, Prabal D., Dogan, Sengul, Tuncer, Turker, Palmer, Elizabeth Emma, Fujita, Hamido, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: Epilepsy detection; Feature fusion; Feature selection; Fusion-based feature engineering; Hypercube pattern; Feature selection; Feature fusion; Epilepsy detection; Fusion-based feature engineering; Hypercube pattern
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2023.03.022

•Automated detection of epilepsy using EEG signals from 121 participants.•Hypercube-based feature extractor and multilevel discrete wavelet transform techniques are employed.•Neighborhood component analysis (NCA) is used as a feature selector.•Attained 87.78% classification accuracy using voting and 79.07% with LOSO CV. Epilepsy is one of the most commonly seen neurologic disorders worldwide and has generally caused seizures. Electroencephalography (EEG) is widely used in seizure diagnosis. To detect epilepsy automatically, various machine learning (ML) models have been introduced in the literature, but the used EEG signal datasets for epilepsy detection are relatively small. Our main objective is to present a large EEG signal dataset and investigate the detection ability of a new hypercube pattern-based framework using the EEG signals. This study collected a large EEG signal dataset (10,356 EEG signals) from 121 participants. We proposed a new information fusion-based feature engineering framework to get high classification performance from this dataset. The dataset consists of 35 channels, and our proposed feature engineering model extracts features from each channel. A new hypercube-based feature extractor has been proposed to generate two feature vectors in the feature extraction phase. Various statistical parameters of the signals have been used to create a feature vector. Multilevel discrete wavelet transform (MDWT) has been applied to develop a multileveled feature extraction function, and seven feature vectors have been extracted. In this work, we have extracted 245 (=35 × 7) feature vectors, and the most valuable features from these vectors have been selected using the neighborhood component analysis (NCA) selector. Finally, these selected features were fed to the k nearest neighbors (kNN) classifier with the leave one subject out (LOSO) cross-validation (CV) strategy. These results have been voted/fused to obtain the highest classification performance. In this work, we have attained 87.78% classification accuracy using voting these vectors and 79.07% with LOSO CV with the EEG signals. The proposed fusion-based feature engineering model achieved satisfactory classification performance using the largest EEG signal datasets for epilepsy detection.