In a recent study, Tamas Karacsony and peers utilized infrared (IR) and depth (3D) videos to investigate the feasibility of a deep learning (DL) system for 24/7 seizure type detection and classification in patients with frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and non-epileptic events. The DL system in the study outperformed all previously published methods for seizure type detection and classification, the findings were published in Scientific Reports.

Seizure semiology is a method used to classify epileptic seizure types based on their characteristics and symptoms. However, this method requires a significant amount of resources, as it involves long-term video-Electroencephalography monitoring and visual analysis by trained professionals. To address this issue, researchers are exploring the use of computer vision-based diagnosis support tools as an alternative approach.
The highlights of the study were:
The DL system was trained on the largest 3D video-EEG database containing around 115 seizures/+680,000 video-frames/427GB.
The researchers found that their DL system achieved a promising cross-subject validation f1-score of 0.833±0.061 for the 2-class (FLE vs. TLE) case and 0.763±0.083 for the 3-class (FLE vs. TLE vs. non-epileptic) case, based on 2-second samples.
The DL system was able to accurately differentiate between seizure types with the use of an automated, semi-specialized depth-based cropping pipeline (accuracy of 95.65%) and a Mask R-CNN-based cropping pipeline (accuracy of 96.52%).
The Authors found that these results demonstrate the feasibility of using DL for 24/7 epilepsy monitoring and suggest that it may be a promising alternative to traditional seizure semiology methods. Overall, this study highlights the potential of computer vision-based tools to support the diagnosis and management of epilepsy.
Source:
Karácsony, T., Loesch-Biffar, A. M., Vollmar, C., Rémi, J., Noachtar, S., & Cunha, J. P. S. (2022). Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification. In Scientific Reports (Vol. 12, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41598-022-23133-9