[Paper Review] Deep Learning Approaches for Seizure Video Analysis: A Review

David Ahmedt‐Aristizabal, Mohammad Ali Armin+6 more|arXiv (Cornell University)|Dec 18, 2023

EEG and Brain-Computer Interfaces4 citations

TL;DR

This review synthesizes deep learning approaches for automated seizure video analysis, focusing on action recognition and semiology detection from 2016 onward. It proposes an integrated pipeline using convolutional and graph-based neural networks to quantify motor seizures, improving detection and localization accuracy while enhancing model interpretability through attention and visualization techniques.

ABSTRACT

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.

Motivation & Objective

• To address observer variability in clinical seizure semiology assessment by developing automated, objective video analysis tools.
• To systematize recent advances in deep learning for video-based seizure detection and classification over the past seven years.
• To propose an integrated, modular framework for automated seizure video analysis that supports clinical decision-making.
• To improve the interpretability of deep learning models in epilepsy by incorporating attention mechanisms and spatio-temporal visualization.
• To identify key challenges such as data scarcity and model generalization, and to chart future research directions in multi-modal epilepsy phenotyping.

Proposed method

• Utilizes 3D convolutional neural networks (3D-CNNs) and two-stream networks to extract spatio-temporal features from video sequences of seizures.
• Employs graph-based models to represent body joint interactions and model dynamic motion sequences with spatial and temporal dependencies.
• Applies motion-guided sampling (MGSampler) to prioritize frames with high motion salience for improved temporal representation.
• Integrates attention mechanisms and class activation mapping (CAM) to visualize and interpret model decisions in spatial and temporal dimensions.
• Combines predictive modeling of future frames to assess model focus and detect biases in training data.
• Proposes a modular, extensible pipeline that integrates video analysis with clinical data, including potential integration with SEEG and neuroimaging.

Experimental results

Research questions

• RQ1 How can deep learning models improve the accuracy and consistency of seizure semiology detection from video recordings compared to clinical assessment?
• RQ2 What deep learning architectures are most effective for capturing spatio-temporal patterns in seizure-related motor behaviors?
• RQ3 How can model interpretability be enhanced to support clinical trust and usability in epilepsy monitoring?
• RQ4 What are the key challenges in deploying video-based seizure analysis in real-world clinical and home settings?
• RQ5 How can multi-modal data (video, EEG, neuroimaging) be integrated to improve seizure type classification and cerebral localization prediction?

Key findings

• Deep learning models, particularly 3D-CNNs and graph-based networks, have shown strong performance in detecting and classifying seizure semiology from video.
• Motion-guided sampling improves frame selection by focusing on high-motion salience segments, enhancing model efficiency and accuracy.
• Attention mechanisms and visualization techniques like CAM provide interpretable insights into model predictions, increasing transparency.
• Graph-based models effectively capture spatial relationships between body joints and temporal dynamics, improving action recognition in complex motor patterns.
• Predictive modeling of future frames helps identify model biases and training focus, supporting robustness evaluation.
• Integration of video-based analysis with clinical data such as SEEG holds promise for refining seizure localization and classification, though large, diverse datasets remain a key bottleneck.