Application of Frequency Features of Optical Flow for Event Detection in Video-EEG Monitoring Data

The work is devoted to the study of the frequency features of the optical flow obtained from the video record of long-term video-electroencephalographic (video-EEG) monitoring data of patients with epilepsy. It is necessary to obtain features to recognize epileptic seizures and differentiate them from non-epileptic events. We propose to analyze the periodograms of the smoothed optical flow computed from the fragments of the patient’s video recordings. We use Welch's method to obtain periodograms. The values of the power spectral density of the optical flow at the selected frequencies are used as features. Using the clustering algorithm, seven groups of events are identified in video recordings and combined into three generalized classes. We train SVM classifier and conduct recognition of events in a test sample of 103 video fragments in four patients. The experiment indicates the accuracy of event classification equal to 90.3%.