New Approach to the Detection of Epileptiform Activity in Eeg Signals and Methods to Differentiate Epileptic Seizures From Chewing Artifacts

Abstract

The new approach based on Morlet wavelet spectrograms ridges analysis and allowing automatic detecting different activity in long term EEG signals is developed. To distinguish epileptiform activity from chewing artifacts two approaches are proposed. The quantitative characteristics of events wavelet spectrogram ridges were studied, as well as the frequency of broadband peaks at time points corresponding to peak-wave epileptiform activity on the one hand, and the peaks of myographic activity during chewing on the other hand. Signs by which one can qualitatively divide the group containing epileptic discharges from chewing artifacts were found.

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“…In [5], a method for finding epileptic seizures and artifacts of chewing in electroencephalographic signals, based on the analysis of their wavelet spectrograms and the parameters of the ridges of wavelet spectrograms, was proposed. It was found that using the frequency maximum value and the arithmetic mean deviation of the frequency of the ridge fragments of the wavelet spectrogram the event can be attributed to an epileptic seizure or to an artifact of chewing.…”