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 ∼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.
CITATION STYLE
Khanmohammadi, S., & Chou, C. A. (2016). A simple distance based seizure onset detection algorithm using common spatial patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9919 LNAI, pp. 233–242). Springer Verlag. https://doi.org/10.1007/978-3-319-47103-7_23
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