Privacy-Centric Seizure Detection Using Surface Normals, Pose and Segmentation Masks

Kishore Gandhi Ponnambalam, Talha Ilyas, Shobi Sivathamboo, Zongyuan Ge, Patrick Kwan, Levin Kuhlmann, Deval Mehta

Abstract: Continuous video-based seizure detection remains clinically challenging owing to occlusions, environmental variations, and subtle seizure manifestations. We introduce a privacy-centric, non-invasive video-based seizure detection system that leverages dense surface normals to encode geometric features. This approach achieves superior generalization as the features remain invariant to patient appearance. This work explores the first application of surface normal analysis to seizure detection, demonstrating that geometry-based features not only preserve privacy but also outperform traditional pose-based methods. Through a rigorous evaluation on 821 clinical video clips from 7 patients, we systematically compare surface normals against pose estimation, semantic segmentation, and multi-modal fusion approaches under both patient-dependent (5-fold CV) and patient-independent (LOPO CV) validation protocols. Raw surface normals achieve \(89.4\%\) accuracy, significantly outperforming pose estimation (\(82.8\%\)) with a \(26.8\%\) relative improvement in F1-score. Critically, it maintains exceptional robustness in scenarios where semantic methods catastrophically fail, including multi-person interactions and severe occlusion, where its F1-score is robustly maintained at 0.833 while the pose-based F1-score drops to 0.0. Our proposed geometry-based, privacy-centric approach enables continuous monitoring in both clinical and home settings without compromising patient privacy.

External IDs:dblp:conf/ausai/PonnambalamISGKKM25