  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. Additionally, we
illustrate how existing technologies can be interconnected through an
integrated system for video-based semiology analysis.
