Patient-dependent epilepsy seizure detection using random forest classification over one-dimension transformed EEG data

Abstract

This work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy is the second most common neurological disease impacting between 40 and 50 million of patients in the world and its proper diagnosis using electroencephalographic signals implies a long and expensive process which involves medical specialists. The proposed system is a patient-dependent o ine system which performs an automatic detection of seizures in brainwaves applying a random forest classi er. Features are extracted using one-dimension reduced information from a spectrotemporal transformation of the biosignals which pass through an envelope detector. The performance of this method reached 97.12% of speci city, 99.29% of sensitivity, and a 0.77 h −1 false positive rate. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.