Energy-Efficient Tree-Based EEG Artifact Detection
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Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological simi-larity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform. The analyses are based on the TUH EEG Artifact Corpus dataset and focus on the temporal electrodes. First, we extract optimal feature models in the frequency domain using an automated machine learning framework, achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%. Then, these algorithms are parallelized and optimized for a PULP platform, achieving a 5.21x improvement of energy-efficient compared to state-of-the-art low-power implementations of artifact detection frameworks. Combining this model with a low-power seizure detection algorithm would allow for 300h of continuous monitoring on a 300 mAh battery in a wearable form factor and power budget. These results pave the way for implementing affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patients' and caregivers' requirements. Clinical relevance– The proposed EEG artifact detection framework can be employed on wearable EEG recording devices, in combination with EEG-based epilepsy detection algorithms, for improved robustness in epileptic seizure detection scenarios.
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Publication status
published
Editor
Book title
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Journal / series
Volume
Pages / Article No.
3723 - 3728
Publisher
IEEE
Event
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Healthcare; Time series classification; Smart edge computing; Machine learning; Deep learning
Organisational unit
03996 - Benini, Luca / Benini, Luca
Notes
Funding
193813 - PEDESITE: Personalized Detection of Epileptic Seizure in the Internet of Things (IoT) Era (SNF)