A common spatial pattern approach for scalp EEG seizure detection

This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic seizure intervals. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are used for training a Support Vector Machine (SVM) classifier, which is then employed in the testing phase. A leave-one-out cross validation strategy is adopted in the experiments. The proposed approach was evaluated using 443.55 hours of sEEG including 39 seizures. The experimental results reveal that a patient-specific CSP-based algorithm is capable of detecting epileptic seizures with high accuracy. In particular, the CSP approach has achieved 100% an average sensitivity, 1.17 an average false alarm, and 7.02 s an average detection latency time.