Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study
Journal article, 2019

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.

Seizure detection devices

Machine learning

Epilepsy

Tonic-clonic seizure

Wrist-worn sensors

Author

Dongni Johansson

University of Gothenburg

Sahlgrenska University Hospital

Fredrik Ohlsson

RISE Research Institutes of Sweden

David Krýsl

University of Gothenburg

Sahlgrenska University Hospital

Bertil Rydenhag

University of Gothenburg

Sahlgrenska University Hospital

Madeleine Czarnecki

RISE Research Institutes of Sweden

Niclas Gustafsson

RISE Research Institutes of Sweden

Jan Wipenmyr

RISE Research Institutes of Sweden

Tomas McKelvey

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Kristina Malmgren

Sahlgrenska University Hospital

University of Gothenburg

Seizure : the journal of the British Epilepsy Association

1059-1311 (ISSN)

Vol. 65 48-54

Subject Categories

Medical Laboratory and Measurements Technologies

Biomedical Laboratory Science/Technology

Signal Processing

DOI

10.1016/j.seizure.2018.12.024

More information

Latest update

11/22/2019