A hybrid unsupervised approach toward EEG epileptic spikes detection

• Main Authors: Khosropanah, Pegah, Ramli, Abdul Rahman, Abbasi, Mohammad Reza, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon
• Format: Article
• Language: English; English
• Published: Springer Verlag (Germany) 2018
• Subjects: QA75 Electronic computers. Computer science; QM Human anatomy; QP Physiology; R Medicine (General); RZ Other systems of medicine; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/19662/1/10.1007%40s00521-018-3797-2.pdf; http://eprints.um.edu.my/19662/2/10.1007%40s00521-018-3797-2.pdf; http://eprints.um.edu.my/19662/; http://dx.doi.org/10.1007/s00521-018-3797-2
• Institution: Universiti Malaya

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy.