Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals

Epilepsy is the second common brain disorder affecting 70 million people worldwide. Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures. However, most of the existing EEG-based seizure detection methods cannot maintain robust performance in real life conditions. This is where the EEG data are corrupted with different sources of noise and artifacts. This study presents a robust seizure detection system that works efficiently under the real life conditions as well as the ideal conditions. A feature learning method based on L1-penalized robust regression is developed and applied to the EEG spectra to recognize the most prominent features pertinent to epileptic seizures. The extracted features are then fed into the random forest classifier for seizure detection. Results on a public benchmark dataset show that the performance of this seizure detection system is superior to prior work. It first achieves seizure detection rates of 100.00% sensitivity, 100.00% specificity, and 100.00% classification accuracy under the ideal conditions. The proposed method is also proven to be robust in the presence of white noise and EEG artifacts mainly those arising from muscle activities and eyes-blinking. It is found to achieve seizure detection accuracies in the range of 90.00-100.00% when applied to noisy EEG data corrupted with high noise levels. To the best of our knowledge, there exists no work in the literature that detects seizures under these conditions.