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Title: | Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection | Authors: | BECKER, Thijs Vandecasteele, Kaat Chatzichristos, Christos Van Paesschen, Wim VALKENBORG, Dirk Van Huffel, Sabine De Vos, Maarten |
Issue Date: | 2021 | Publisher: | MDPI | Source: | SENSORS, 21 (4) (Art N° 1046) | 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-h EEG recording typically takes one to two hours. Reliable automated seizure detection algorithms will be crucial to reduce this analysis. We investigated such algorithms on a dataset of behind-the-ear EEG measurements. Our first aim was to develop a methodology where part of the data is deferred to a human expert, who performs perfectly, with the goal of obtaining an (almost) perfect 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 deferring around 10% (40%) of the data. Perfect DS can be achieved when deferring 50% of the data. Our second contribution demonstrates that a common modelling strategy, where predictions from several short EEG segments are combined 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. | Notes: | Becker, T (corresponding author), Hasselt Univ, Data Sci Inst, I Biostat, B-3500 Hasselt, Belgium. thijs.becker@uhasselt.be; kaat.vandecasteele@esat.kuleuven.be; christos.chatzichristos@esat.kuleuven.be; wim.vanpaesschen@uzleuven.be; dirk.valkenborg@uhasselt.be; sabine.vanhuffel@esat.kuleuven.be; maarten.devos@kuleuven.be |
Other: | Becker, T (corresponding author), Hasselt Univ, Data Sci Inst, I Biostat, B-3500 Hasselt, Belgium. thijs.becker@uhasselt.be; kaat.vandecasteele@esat.kuleuven.be; christos.chatzichristos@esat.kuleuven.be; wim.vanpaesschen@uzleuven.be; dirk.valkenborg@uhasselt.be; sabine.vanhuffel@esat.kuleuven.be; maarten.devos@kuleuven.be | Keywords: | epilepsy;seizure detection;electroencephalography;classification with a deferral option;home monitoring;long-term monitoring;wearables | Document URI: | http://hdl.handle.net/1942/33884 | e-ISSN: | 1424-8220 | DOI: | 10.3390/s21041046 | ISI #: | WOS:000624699700001 | Rights: | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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sensors-21-01046-v2.pdf | Published version | 5.79 MB | Adobe PDF | View/Open |
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