  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.
