Time and frequency domain pre-processing for epileptic seizure classification of epileptic EEG signals

Article type: Research Article

Authors: Dutta, Kusumika Krori^{a; *} | Manohar, Premila^b | Indira, K.^c

Affiliations: [a] Department of Electrical & Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, Visvesveraya Technological University, Belagavi, India | [b] Department of Electrical & Electronics Engineering, Nitte Meenakshi Institute of Technology, Bangalore, Visvesveraya Technological University, Belagavi, India | [c] Department of Electronics & Communication Engineering, M S Ramaiah Institute of Technology, Bangalore, Visvesveraya Technological University, Belagavi, India

Correspondence: [*] Corresponding author. Kusumika Krori Dutta, Department of Electrical & Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, Visvesveraya Technological University, Belagavi, India. E-mail: [email protected].

Abstract: Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it’s side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure’s ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all.

Keywords: Seizure classification, TUHEEG, ABSZ, CPSZ, FNSZ, GNSZ, SPSZ, TNSZ, TCSZ, SVM, KNN, LSTM, EEG

DOI: 10.3233/JIFS-224570

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8217-8226, 2023