A boosted cascade for efficient epileptic seizure detection

Abstract

Seizure detection from electroencephalogram (EEG) plays an important role for epilepsy therapy. Due to the diversity of seizure EEG patterns between different individuals, multiple features are necessary for high accuracy since a single feature could hardly encode all types of epileptiform discharges. However, a large feature set inevitably causes the increase of the computational cost. This paper proposes a boosted cascade chain to obtain both high detection performance and high computational efficiency. Sixteen features that are widely used in seizure detection are implemented. Considering the sequential characteristics of EEG signals, the features are extracted on each 1-second segment and its former three segments. Thus, a total of 64 features are used to construct a feature pool. Based on the feature pool, Real AdaBoost is used to select a group of effective features, on which weak classifiers are learned to assemble a strong classifier. The strong classifier is transformed to a cascade classifier by reordering the weak classifiers and learning a threshold for each weak classifier. The cascade classifier still has the similar classification strength to the original strong classifier. More importantly, it is able to reject easy non-seizure samples by the first a few weak classifiers in the cascade, thus high computational efficiency can be obtained. To evaluate our method, 90.6-hour EEG signals from four patients are tested. The experimental results show that our method can achieve an average accuracy of 95.31% and an average detection rate of 91.29% with the false positive rate of 4.68%. On average, only about 4 features are used. Compared with support vector machine (SVM), our method is much more efficient with the similar detection performance.