A Simple Distance Based Seizure Onset Detection Algorithm Using Common Spatial Patterns

Existing seizure onset detection methods usually rely on a large number of extracted features regardless of computational efficiency, which reduces their applicability for real-time seizure detection. In this study, a simple distance based seizure onset detection algorithm is proposed to distinguish seizure and non-seizure EEG signals. The proposed framework first applies the common spatial patterns (CSP) method to enhance the signal-to-noise ratio and reduce the dimensionality of EEG signals, and then uses the autocorrelation of the averaged spatially filtered signal to classify incoming signals into a seizure or non-seizure state. The proposed approach was tested using CHB-MIT dataset that contains continuous scalp EEG recordings from 23 patients. The results showed \(\sim \)95.87 % sensitivity with an average latency of 2.98 s and 2.89 % false detection rate. More interestingly, the average process time required to classify each window (1–5 s of EEG signals) was 0.09 s. The outcome of this study has a high potential to improve the automatic seizure onset detection from EEG recordings and could be used as a basis for developing real-time monitoring systems for epileptic patients.