Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

中文翻译：

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用于癫痫视频分析的深度学习方法：综述

癫痫事件可以表现为运动控制的短暂中断，这些运动控制可能以不同的行为序列组织，伴有或不伴有其他可观察到的特征，例如面部表情的改变。当专家在临床环境中评估视频记录的事件时，对这些临床症状的分析（称为符号学）会受到观察者的变化。为了提高评估的准确性和一致性，计算机辅助癫痫发作视频分析已成为一种自然途径。在医疗应用领域，深度学习和计算机视觉方法推动了重大进步。从历史上看，这些方法已被用于使用诊断数据进行疾病检测、分类和预测。然而，对其在临床癫痫学环境中评估基于视频的运动检测的应用的探索有限。虽然基于视觉的技术并不旨在取代临床专业知识，但它们可以通过提供定量证据和决策支持，为医疗决策和患者护理做出重大贡献。行为监控工具具有多种优势，例如提供客观信息、检测难以观察的事件、减少记录工作以及将评估功能扩展到专业知识有限的领域。这些的主要应用可能是（1）改进癫痫检测方法； (2)精细的符号学分析，用于预测癫痫发作类型和大脑定位。在本文中，我们详细介绍了基于视觉的系统在癫痫视频分析中使用的基础技术，强调了它们在符号学检测和分析方面的成功，重点关注过去 7 年发表的工作。我们系统地介绍了这些方法，并指出了如何采用深度学习来分析癫痫发作的视频记录。此外，我们还说明了如何通过基于视频的符号学分析的集成系统互连现有技术。随着深度学习方法的发展，每个模块都可以通过采用更准确、更强大的深度学习方法来定制和改进。最后，我们讨论了未来研究的挑战和研究方向。