Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers

• Published in: Scientific reports Vol. 14; no. 1; pp. 2980 - 16
• Main Authors: Ingolfsson, Thorir Mar, Benatti, Simone, Wang, Xiaying, Bernini, Adriano, Ducouret, Pauline, Ryvlin, Philippe, Beniczky, Sandor, Benini, Luca, Cossettini, Andrea
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/166/987; 692/617/375/178; Algorithms; Convulsions & seizures; Datasets; EEG; Electroencephalography; Epilepsy; Epilepsy - diagnosis; False alarms; Humanities and Social Sciences; Humans; Life span; Living conditions; multidisciplinary; Science; Science (multidisciplinary); Seizures; Seizures - diagnosis; Wearable Electronic Devices
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52551-0

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of 65.27 % for 182 seizures from the CHB-MIT dataset and 57.26 % for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of 93.95 % (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms—up to 96 % compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.