Enhancing Automated Patient Detection and Tracking in Epilepsy Monitoring Unit Videos

Continuous patient monitoring using automated algorithms has the potential to reduce risks associated with seizures by accelerating interventions. Video monitoring offers a practical alternative; however, seizure detection algorithms could learn to associate incidental events with seizure activity, introducing biases during both the training and the testing phases. By incorporating patient tracking into seizure detection algorithms, patients can be isolated within video frames, reducing biases and improving specificity. As no existing object detection models are pre-trained to identify patients as unique class of individuals, a YOLOv8 model was fine-tuned using 1,590 manually labeled frames from 131 patients admitted to our epilepsy monitoring unit, capturing daily activities, seizures, and body occlusions in both daytime and nighttime. Hyperparameter optimization was performed using a grid-search with a five-fold cross-validation on the training and validation set (85% of the data), while 15% was reserved as a held-out test set. Data were split on a patient-wise basis to prevent data leakage across patients. Additionally, a novel post-processing pipeline for videos was developed to further improve performances. The fine-tuned model achieved 97.6% recall and 96.8% precision, detecting patients under diverse conditions. The post-processing pipeline was evaluated on four new seizure videos, correcting 577 out of 580 misclassified or missed detections in these videos. Combined with the novel post-processing step, the proposed pipeline offers a promising tool for enhancing video-based seizure detection.Clinical relevance- This approach reduces misattributions in video-based seizure detection, leading to more reliable, biasfree monitoring. Thus, it enables faster and more accurate interventions, improving patient safety.