Novel quadratic time-frequency features in EEG signals for robust detection of epileptic seizure

Purpose: Epilepsy is a chronic neurological disorder characterized by recurrent convulsions. Therapists seek to recognize epilepsy patterns in Electroencephalogram (EEG) signals through visual inspection. However, this procedure is costly, time-consuming, and sensitive to bias. Therefore, in this proof-of-concept investigation, we developed an automatic method of epileptic seizure detection. The deployed approach includes novel time-frequency features of EEG data. Methods: We evaluated the performance of the developed algorithm using the CHB-MIT scalp EEG database and the TUH EEG Corpus database. We compute time-frequency representations of EEG data through Quadratic Time-Frequency Distributions (QTFDs), namely, the Smoothed Pseudo Wigner-Ville distribution (SPWVD), the Choi-Williams distribution (CWD), and the spectrogram (SP). We developed an approach for detecting epileptical brain activity by extracting relevant features from the obtained time-frequency representation. We analyzed EEG signals in four frequency bands in accordance to brainwaves bandwidths, namely, Delta (0.4 up to 4 Hz), Theta (4 up to 8 Hz), Alpha (8 up to 12 Hz), Beta (12 up to 30 Hz), and Gamma (above 30 Hz). Thus, we located the contour of the time-frequency area to define features within the time-frequency plane. We provided the extracted time-frequency features as inputs for two classifiers: the support vector machine (SVM) with RBF kernel function and the k-nearest neighbors (kNN) for comparison purposes. We defined seven statistical parameters, sensitivity (Se), specificity (Sp), the overall classification accuracy (Acc), precision (Prec), False alarm rate (FPR), F-measure score (F1-score), and Area under the curve (AUC). Results: The experimental results obtained from the both databases show that the proposed approach provides very encouraging results. Indeed, we can reach precision rates of 99.29% when using the Smoothed Pseudo Wigner-Ville distribution (SPWVD) combined with the kNN classifier and a precision rate of 97.67% for the Choi-Williams distribution (CWD) combined with a SVM classifier, with an average area under the curve (AUCs) of 0.9911 and 0.9701 respectively. The spectrogram (SP) yields fewer performance results for both classifiers at a rate not exceeding 95.88% with an area under the curve of 0.9542. Conclusion: We developed an efficient approach for recognizing epileptic seizures. This approach localizes the contour area at five levels on time-frequency representations of EEG signals using three quadratic time-frequency distributions. Compared to the Spectrogram (SP) and the Smoothed Pseudo Wigner-Ville distribution (SPWVD), the Choi-Williams distribution (CWD) has proved its effectiveness with high precision and complete sensitivity, indicating a powerful method for identifying epileptic seizures from EEG signals. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.