Veni, Vidi, Vici—When Is Home Video Seizure Monitoring Helpful?

Abstract

Seizure detection is vital for managing epilepsy as seizures can lead to injury and even death, in addition to impacting quality of life. Prompt detection of seizures and intervention can help prevent injury and improve outcomes for individuals with epilepsy. Wearable sensors show promising results for automated detection of certain seizures, but they have limitations such as patient tolerance, impracticality for newborns, and the need for recharging. Non-contact video and audio-based technologies have become available, but a comprehensive literature review on these methods is lacking. This scoping literature review provides an overview of video and audio-based seizure detection, highlighting their potential benefits and challenges. It encompasses a thorough search and evaluation of relevant articles, summarizing methods and performances of these systems. The primary aim of this review is to examine and analyze existing research to identify patterns and gaps and establish a foundation for future advancements. We screened 7 databases using a set of standardized search criteria to minimize any potential missed articles. Four thousand four hundred eighty-seven deduplicated abstracts were screened and narrowed down to 34 studies that varied in design, algorithm methods, types of seizures detected, and performance metrics. Seizure detection sensitivity ranged from 100% to 0%, with optical flow analysis showing the highest sensitivity. The specificity of all included articles ranged from 97.7% to 60%. While limited studies reported accuracy, the highest reported was 100% using Radon Transform based technique on Dual Tree Complex Wavelet coefficients. Video and audio-based tools offer novel, noncontact approaches for detecting and monitoring seizures. Available studies are limited in sample sizes, dataset diversity, and standardized evaluation protocols, impacting the generalizability of results. Future research focusing on larger-scale investigations with diverse datasets, standardized evaluation protocols, and consistent reporting metrics is needed.