Deep learning approaches for seizure video analysis: A review.

Area of Science:

• Neurology
• Computer Science
• Medical Imaging

Background:

• Seizure semiology analysis is subjective and prone to observer variability.
• Traditional methods struggle with objective, consistent evaluation of seizure events.
• Advancements in deep learning and computer vision offer potential for objective analysis.

Purpose of the Study:

• To review vision-based technologies for seizure video analysis.
• To highlight the application of deep learning in semiology detection and analysis.
• To propose an integrated system for video-based semiology analysis.

Main Methods:

• Systematic review of vision-based seizure analysis technologies (last 7 years).
• Focus on deep learning and computer vision approaches.
• Discussion of integrating modular vision-based systems.

Main Results:

• Vision-based technologies show success in semiology detection and analysis.
• Deep learning offers a robust approach for analyzing seizure videos.
• Integrated systems can enhance seizure detection and semiology refinement.

Conclusions:

• Computer-aided video analysis provides objective data for clinical decision-making.
• Deep learning adoption can significantly improve seizure analysis accuracy.
• Future research should focus on integrated systems and advanced deep learning models.