A Closed-Loop Neuromodulation System Using Machine Learning for Real-Time Prediction and Suppression of Epileptic Seizures

Abstract

Epilepsy is a debilitating neurological disorder affecting over 50 million people worldwide, characterized by recurrent, unprovoked seizures. A significant portion of patients are refractory to pharmacological treatment, necessitating alternative therapies. Closed-loop neuromodulation systems, which deliver electrical stimulation in response to detected seizure activity, represent a 
promising intervention. However, current state-of-the-art systems primarily react to seizures already in progress, often after clinical onset, limiting their therapeutic efficacy and ability to prevent debilitating symptoms. This paper presents the development and validation of a novel closed-loop neuromodulation system that leverages machine learning (ML) for the realtime prediction of impending epileptic seizures, enabling preemptive intervention. Our system architecture integrates long-term intracranial 
electroencephalography (iEEG) data acquisition, a cloud-based model training pipeline, and an implantable, low-power microcontroller unit (MCU) for real time inference. We engineered a compact convolutional neural network (CNN) model capable of extracting spatiotemporal features from multichannel iEEG to 
identify preictal (pre-seizure) states. The model was trained and validated on a large, multi-patient dataset from the NeuroVista and ETHZ iEEG archives, achieving a mean seizure prediction sensitivity of 91.2% with a false prediction rate of 0.12 per hour. The optimized model was deployed on a custom-designed hardware platform featuring ultra-low-power consumption for continuous  monitoring. In a simulated real-time case study with retrospective data, the system successfully predicted 89% of seizures with an average warning time of 58.2 seconds prior to electrographic onset. Upon prediction, the system triggers a tailored, high-frequency stimulation protocol delivered via depth electrodes to the seizure focus, which suppressed 85% of impending seizures in an established rodent kainate model of epilepsy. This work demonstrates a significant paradigm shift from reactive to proactive neuromodulation. By integrating predictive ML models with efficient hardware, this system holds the potential to dramatically improve the quality of life for patients with drugresistant epilepsy, offering a path toward truly preventive neurotherapeutics.