EEG/ECG information fusion for epileptic event detection

This paper addresses the automated false positives-free detection of epileptic events by the fusion of information extracted from simultaneously recorded electroencephalographic- and electrocardiographic time-series. The approach relies on the biomedical prior knowledge for the coupling of the brain- and heart systems through the central autonomic network during temporal lobe epileptic events: neurovegetative manifestations associated with temporal lobe epileptic events consist of alterations to the cardiac rhythm. From a neurophysiological perspective, epileptic episodes are characterised by a loss of complexity of the state of the brain. The description of arrhythmias, from a probabilistic perspective, observed during temporal lobe epileptic events and the description of the complexity of the state of the brain, from an information theory perspective, are integrated in a fusion-of-information framework towards temporal lobe epileptic seizure detection. We show that the biomedical data fusion of simultaneously recorded EEG and ECG time-series leads to the detection of genuine epileptic events and to the dramatic reduction of false-positives.