A Peak Synchronization Measure for Multiple Signals

TL;DR

This paper introduces a new peak synchronization measure for multiple signals that captures the simultaneity of important events, demonstrating improved modeling of epileptic seizures over traditional methods.

Contribution

A novel peak synchronization measure is proposed, generalized to multiple signals, with demonstrated effectiveness on intracranial EEG data during epileptic seizures.

Findings

Enhanced synchronization detection during seizures

Better modeling than classical correlation methods

Parameter independence of the measure

Abstract

Peaks signify important events in a signal. In a pair of signals how peaks are occurring with mutual correspondence may offer us significant insights into the mutual interdependence between the two signals based on important events. In this work we proposed a novel synchronization measure between two signals, called peak synchronization, which measures the simultaneity of occurrence of peaks in the signals. We subsequently generalized it to more than two signals. We showed that our measure of synchronization is largely independent of the underlying parameter values. A time complexity analysis of the algorithm has also been presented. We applied the measure on intracranial EEG signals of epileptic patients and found that the enhanced synchronization during an epileptic seizure can be modeled better by the new peak synchronization measure than the classical amplitude correlation method.