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Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study

Journal article
Authors Dongni Johansson
Fredrik Ohlsson
David Krysl
Bertil Rydenhag
Madeleine Czarnecki
Niclas Gustafsson
Jan Wipenmyr
Tomas McKelvey
Kristina Malmgren
Published in Seizure
Volume 65
Pages 48-54
ISSN 1059-1311
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Pages 48-54
Language en
Links doi.org/10.1016/j.seizure.2018.12.0...
Keywords Epilepsy, Machine learning, Seizure detection devices, Tonic-clonic seizure, Wrist-worn sensors
Subject categories Neurology

Abstract

© 2018 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.

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