Integrating channel selection and feature selection in a real time epileptic seizure detection system

Automated real time seizure detection is difficult since detection sensitivity, false detection rate and seizure onset detection latency need to be considered simultaneously. Traditional pattern recognition and classification system usually suffers huge performance variation due to patient specificity and algorithm inadaptability. To address this problem, we propose a two stage seizure detection system which integrates off-line channel selection and feature selection before the construction of the final model. This system allows patient specific channel selection and flexible feature set extraction for individual patient, so that a more compact and reliable model could be developed. Employing the two stage scheme not only decreases hardware cost in signal readout and feature extraction, but also remarkably improves detection sensitivity and reduces false detections. Mutual information based method is used for channel selection, while Random Forests and nonlinear SVM-RFE are evaluated for feature selection. The whole system achieves a mean detection latency of 6 seconds and a false detection rate of 0.356 per hour. Based on the test dataset, the sensitivity is found to 74.2% by sample or 98.4% by record with only two detection misses. Our design is also hardware-friendly, which could be implemented as a single chip closed loop neural modulation system.