Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection

Version 1 : Received: 18 December 2020 / Approved: 21 December 2020 / Online: 21 December 2020 (13:40:43 CET)

How to cite: Becker, T.; Vandecasteele, K.; Chatzichristos, C.; Van Paesschen, W.; Valkenborg, D.; Van Huffel, S.; De Vos, M. Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection. Preprints 2020, 2020120527 (doi: 10.20944/preprints202012.0527.v1). Becker, T.; Vandecasteele, K.; Chatzichristos, C.; Van Paesschen, W.; Valkenborg, D.; Van Huffel, S.; De Vos, M. Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection. Preprints 2020, 2020120527 (doi: 10.20944/preprints202012.0527.v1).

Abstract

Wearable technology will become available and allow prolonged electroencephalography (EEG) monitoring in the home environment of patients with epilepsy. Neurologists analyse the EEG visually and annotate all seizures, which patients often under report. Visual analysis of a 24 hour EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We study a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology to reduce the EEG dataset by classifying part of the data automatically, while retaining 100% detection sensitivity (DS). Prediction confidences are determined by temperature scaling of the classification model outputs and trust scores. A DS of approximately 90% (99%) can be achieved when automatically classifying around 90% (60%) of the data. Perfect DS can be achieved when automatically classifying 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are used to obtain a final prediction, can be improved by filtering out untrustworthy segments with low trust scores. The false detection rate shows a relative decrease between 21% and 43%, and the DS shows a small increase or decrease.

Subject Areas

epilepsy; seizure detection; electroencephalography; classification with a deferral option; home monitoring; long-term monitoring; wearables

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