Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
This addresses the need for affordable, long-term epilepsy monitoring for patients and caregivers, but it is incremental as it builds on existing classification methods and datasets.
The paper tackled seizure detection using minimal EEG channels on a low-power wearable platform, achieving 100% sensitivity and zero false positives with subject-specific approaches on the CHB-MIT dataset, and enabling 300 hours of continuous monitoring on a small battery.
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/post-processing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patient and caregiver requirements.