Improving the Extraction of Temporal Motion Strength Signals from Video Recordings of Neonatal Seizures

Automated processing and analysis of video recordings of neonatal seizures can generate novel methods for extracting quantitative information that is relevant only to the seizure [5], [6]. This information can be used to: 1) develop automated mechanisms capable of detecting the beginning of clinical seizures, 2) refine the characterization of repetitive motor behaviors, and 3) facilitate the differentiation of certain clinical seizures from other abnormal paroxysmal behaviors not due to seizures. The development of an automated video analysis system would represent a major advance in seizure surveillance and offers the possibility for earlier identification of potential neurological problems and subsequent intervention. This paper presents a procedure developed to extract quantitative information from video recordings of neonatal seizures in the form of temporal motion strength signals. These signals are obtained by applying nonlinear filtering, segmentation, and morphological filtering on the differences between adjacent frames. The experiments indicate that temporal motion strength signals constitute an effective representation of videotaped clinical events and can be used for seizure recognition and characterization. This paper summarizes the results of a study that aimed at improving the extraction of motion strength signals from video recordings of neonatal seizures. In principle, motion strength signals quantify motion by measuring the area of the frames occupied by moving body parts affected by seizures.