Epilepsy Seizure Detection: A Heavy Tail Approach

Epilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the electroencephalogram (EEG) signal. Typically, the statistical behavior of the EEG signals presents heavy-tail phenomena, therefore their analysis must be particular in order to define a strong framework based on statistical parameters to detect seizures. In this article, the heavy-tail characterization of EEG signals is studied, a simple real-time epilepsy detection using an alpha-stable estimator is proposed, and the false-positive rate is analyzed. The performance of the proposed estimator is compared to others previously reported in the literature, and we show that one of the signal parameters characterized as an alpha-stable distribution, serves as an indicator of epilepsy episodes more efficiently. Furthermore, the proposed algorithm presents low sensitivity to noise below the 3.8 dB.