A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures

• URL: http://arxiv.org/abs/2410.20066v2
• Date: Fri, 01 Nov 2024 18:20:51 GMT
• Title: A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures
• Authors: Ali Saeizadeh, Douglas Schonholtz, Joseph S. Neimat, Pedram Johari, Tommaso Melodia,
• Abstract summary: This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures. Our framework employs advanced Deep Learning (DL) techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Our model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.
• Score: 10.250114060511134
• License:
• Abstract: 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.

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