A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures
This addresses the need for timely seizure prediction to enable precautionary actions for individuals with drug-resistant epilepsy, representing a strong specific gain in a domain-specific application.
The paper tackles the problem of predicting seizures in epilepsy patients by developing a deep learning framework that uses non-invasive EEG and ECG data to provide progressive alerts up to an hour before onset, achieving 95% sensitivity, 98% specificity, and 97% accuracy across 29 patients.
This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multi-modal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacological interventions. To address this challenge, we advocate for predictive systems that provide timely alerts to individuals at risk, enabling them to take precautionary actions. Our framework employs advanced DL techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors, thereby enhancing prediction accuracy. The algorithms are optimized for real-time processing on edge devices, mitigating privacy concerns and minimizing data transmission overhead inherent in cloud-based solutions, ultimately preserving battery energy. Additionally, our system predicts the countdown time to seizures (with 15-minute intervals up to an hour prior to the onset), offering critical lead time for preventive actions. Our multi-modal model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.