Methods and Algorithms for Extracting and Classifying Diagnostic Information from Electroencephalograms and Videos

Abstract

This article describes new approaches and methods for analyzing long-term EEG data and synchronous video-EEG monitoring of patients with epilepsy and restoration of cognitive functions after moderate traumatic brain injury. EEG analysis is performed using the ridges of its wavelet spectrograms, the power spectral density, the frequency and phase of which, under certain conditions, corresponds to the square of the amplitude, frequency, and phase of the EEG signal. The results of studies of the frequency characteristics of a video stream when analyzing data from long-term synchronous video-EEG monitoring of patients with epilepsy are presented. Signs were obtained for recognizing epileptic seizures and differentiating them from events of a nonepileptic nature. Periodograms of smoothed optical flow calculated from fragments of patient video recordings were analyzed. Welch’s method was used to obtain periodograms. The values of the power spectral density of the optical flow at selected frequencies were used as features. A joint analysis of interchannel frequency synchronization, power spectral density of wavelet spectrogram ridges, and synchronous video made it possible to identify fragments with epileptic seizures on a long-term EEG, excluding various artifacts from consideration. Interchannel phase connectivity of the ridges makes it possible to observe the dynamics of EEG synchronization in patients with moderate traumatic brain injury during cognitive tests. Analysis of a network of phase-related pairs of EEG channels allows determining the positive dynamics of patient rehabilitation.