Automatic seizure detection using diffusion distance and BLDA in intracranial EEG

Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75 h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.