Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Thorir Mar Ingolfsson; Andrea Cossettini; Xiaying Wang; Enrico Tabanelli; Giuseppe Tagliavini; Philippe Ryvlin; Luca Benini; Simone Benatti

arXiv · 2106.08008

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Thorir Mar Ingolfsson·Andrea Cossettini·Xiaying Wang·Enrico Tabanelli·Giuseppe Tagliavini·Philippe Ryvlin·Luca Benini·Simone Benatti

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Abstract

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.

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Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang, Enrico Tabanelli, Giuseppe Tagliavini, Philippe Ryvlin, Luca Benini, Simone Benatti. 2021-06-15. Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices. https://arxiv.org/abs/2106.08008

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