Post-Traumatic Epilepsy in Rats: An Algorithm for Detection of Suspicious EEG Activity

Abstract

Due to the fact that there are problems in neurophysiological research on post-traumatic epilepsy in order to find sleep spindles and epileptiform discharges in long-term (day or more) recordings of electroencephalography (EEG) there is a need for algorithms for automatic detection of suspicious EEG activity (we call as suspicious activity any EEG activity that differs from the background activity). There are many methods of signal processing. The most common and straightforward are the methods of transition from the temporal representation of the signal to the time-frequency representation. One of them is the wavelet transform. For the wavelet spectrograms, the ridges of the wavelet spectrograms are calculated. Method of detecting the suspicious activity involves an analysis of points of the ridges. The spectrogram of ridge points are calculated, after which the points of the ridge are divided into two groups: those that relate to the background activities and those that relate to suspicious activity. Suspicious activity that does not meet the requirements of neuroscientists is eliminated.