Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting

Early detection of epileptic seizures is still a challenge in the state-of-the-art. The proposed method exploits multiresolution sample entropy for both seizure detection and fingerprinting. First, a SVM classifier is used to detect the seizures' onset with high temporal accuracy, then the seizures fingerprints across the subband structure are derived exploiting sample entropy non stationarity. Over 8 hours of EEG data recordings from patients suffering from temporal lobe epilepsy were used for training and testing the system, and validation was performed based on annotation by one expert neurophysiologist. All the seizures were successfully detected and provides an effective time-scale fingerprinting of their evolution. A prominent impact in high (γ) frequency band was observed whose neurophysiological ground is currently under investigation.