Feature extraction from EEG spectrograms for epileptic seizure detection

• Published in: Pattern recognition letters Vol. 133; pp. 202 - 209
• Main Authors: Ramos-Aguilar, Ricardo, Olvera-López, J. Arturo, Olmos-Pineda, Ivan, Sánchez-Urrieta, Susana
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier Science Ltd
• Subjects: EEG; EEG signals; Electroencephalography; Epilepsy; Feature Extraction; Human-computer interface; Interfaces; Multilayer perceptrons; Seizure Classification; Seizures; Short-time Fourier Transform; Spectrograms; Support vector machines; Feature Extraction; Short-time Fourier Transform; Seizure Classification; EEG signals; Spectrograms
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2020.03.006

•Three different approaches to extracting features from EEG spectrograms are proposed with relevant results.•Type window and overlapping from short time Fourier Transform is justified based on spectrum energy.•A method for selecting the window length from short-time Fourier transform based on signal frequency is proposed.•For the two-classes problem case the here proposed method is competitive in accuracy when compared against the literature.•This approach requires fewer features than others. Identification of EEG signals is currently an open problem where performance analysis in terms of accuracy is relevant in several fields, such as biomedicine and brain computer interfaces. Nevertheless, performance depends on the feature extraction phase, where the aim is to find relevant patterns related to different mental activities. Thus, in this work, an approach to extract features from EEG signals is proposed based on spectrograms: Firstly, STFT is applied to EEG to obtain time-frequency representations, where parameters such as window length and type are experimented based on the EEG signal frequency. After that, spectral peaks are found to be used as reference in order to obtain descriptors per spectrogram. Three ways for extracting features from EEG are presented, the first based on frequency and surfaces, the second using K-means to extract features and the adaptation of local ternary pattern, and finally, a third using maximum peaks. The extracted descriptors are evaluated by means of a multilayer perceptron, support vector machines, and k-nearest neighbors. The proposed approach was evaluated using the dataset from Bonn University, identifying a healthy person and an epileptic attack classes as main task. According to the experimental results, the proposed method obtains acceptable accuracy (100%) in several cases by considering fewer features than those extracted by other related works.