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 8 s 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.

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices / Ingolfsson, T. M.; Cossettini, A.; Wang, X.; Tabanelli, E.; Tagliavini, G.; Ryvlin, P.; Benini, L.; Benatti, S.. - (2021), pp. 01-04. (Intervento presentato al convegno 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 tenutosi a deu nel 2021) [10.1109/BioCAS49922.2021.9644949].

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

Benatti S.
2021

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 8 s 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.
2021
2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021
deu
2021
01
04
Ingolfsson, T. M.; Cossettini, A.; Wang, X.; Tabanelli, E.; Tagliavini, G.; Ryvlin, P.; Benini, L.; Benatti, S.
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices / Ingolfsson, T. M.; Cossettini, A.; Wang, X.; Tabanelli, E.; Tagliavini, G.; Ryvlin, P.; Benini, L.; Benatti, S.. - (2021), pp. 01-04. (Intervento presentato al convegno 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 tenutosi a deu nel 2021) [10.1109/BioCAS49922.2021.9644949].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1264952
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