Epilepsy cannot be successfully treated through medications or resection in about 30% of patients. Furthermore, an estimated 0.1 percent of epileptic patients suffer sudden deaths resulting from injuries sustained during seizures. For this reason, patients with intractable seizures need alternative therapeutic approaches. An engineered device tailored toward seizure prediction that warns patients of an impending seizure or that intervenes to prevent its occurrence may significantly decrease the burden of epilepsy. Although much research efforts have been directed at the development of a seizure prediction algorithm, a therapeutic or warning device that meets stringent clinical requirements is still elusive. In the present study a novel patient-specific seizure prediction method is proposed. The method is based on time-frequency analysis of scalp electroencephalogram (sEEG) and the use of state-of-the-art unsupervised feature representation learning techniques: reconstruction independent component analysis and sparse filtering. In a moving window analysis, a novel engineered bivariate EEG characterizing measure named Normalized Logarithmic Wavelet Packet Coefficient Energy Ratios (NLWPCER) was extracted from all possible combination of EEG channels and relevant frequency sub - bands. Thereafter unsupervised representation learning algorithm adapted to each patient through Bayesian optimization procedure was used to learn NLWPCER features representation or transformation suitable for data classification task. Two classification models: Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed and trained to learn preictal (pre-seizure) and interictal (normal) EEG feature vector patterns. The output of the classifiers was regularized through a post processing operation aimed at reducing false prediction rate (FPR) and making decision on the generation of prediction alarms. The proposed method was evaluated using approximately 545 h CHB-MIT scalp EEG recording of 17 patients with a total of 43 leading seizures. On the average, with SVM classifier the proposed seizure prediction algorithm achieved a sensitivity of 87.26% and false prediction rate of 0.08h-1 while with ANN classifier the algorithm achieved average sensitivity and false prediction rate of 75.49% and 0.13h-1 respectively. The proposed method was validated using an Analytic Random Predictor (ARP). The results obtained in this work opens a pathway for a robust and consistent real-time portable seizure prediction device suitable for clinical applications.
Hafeez A. Agboola, Colette Solebo, David S. Aribike, Afolabi E. Lesi and Alfred A. Susu*
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