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Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study
University of Gothenburg, Sweden; Sahlgrenska University Hospital, Sweden.
RISE - Research Institutes of Sweden, ICT, Acreo.
University of Gothenburg, Sweden; Sahlgrenska University Hospital, Sweden.
University of Gothenburg, Sweden; Sahlgrenska University Hospital, Sweden.
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2019 (English)In: Seizure, ISSN 1059-1311, E-ISSN 1532-2688, Vol. 65, p. 48-54Article in journal (Refereed) Published
Abstract [en]

Purpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. Methods: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. Results: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). Conclusions: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management.

Place, publisher, year, edition, pages
2019. Vol. 65, p. 48-54
Keywords [en]
Epilepsy, Machine learning, Seizure detection devices, Tonic-clonic seizure, Wrist-worn sensors
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-37013DOI: 10.1016/j.seizure.2018.12.024Scopus ID: 2-s2.0-85059320330OAI: oai:DiVA.org:ri-37013DiVA, id: diva2:1280984
Note

Funding details: Stiftelsen för Strategisk Forskning, SBE 13-0086; Funding details: ALFGBG-429901;

Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically approved

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