Adaptive Seizure Onset Detection Framework Using a Hybrid PCA-CSP Approach

Epilepsy is one of the most common neurological disorders in the world. Prompt detection of seizure onset from electroencephalogram (EEG) signals can improve the treatment of epileptic patients. This paper presents a new adaptive patient-specific seizure onset detection framework that dynamically selects a feature from enhanced EEG signals to discriminate seizures from normal brain activity. The proposed framework employs principal component analysis and common spatial patterns to enhance the EEG signals and uses the extracted discriminative feature as an input for adaptive distance-based change point detector to identify the seizure onsets. Experimental results from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset show the computational efficiency of the proposed method (analyzing EEG signals in a time window of 3 s within 0.1 s using a Core i7 PC) while providing comparable results to the existing methods in terms of average sensitivity, latency, and false detection rate. The proposed method is advantageous for real-time monitoring of epileptic patients and could be used to improve early diagnosis and treatment of patients suffering from recurrent seizures.