Pavei, J., Heinzen, R.G, Novakova, B. et al. (5 more authors) (2017) Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics. Frontiers in Physiology, 8. 765. ISSN 1664-042X
Abstract
Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 Pavei, Heinzen, Novakova, Walz, Serra, Reuber, Ponnusamy and Marques. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | electrocardiogram; heart rate variability; epilepsy; epileptic seizure prediction; support vector machines |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Division of Genomic Medicine (Sheffield) > Department of Oncology and Metabolism (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2017 10:37 |
Last Modified: | 29 Nov 2017 10:37 |
Published Version: | https://doi.org/10.3389/fphys.2017.00765 |
Status: | Published |
Publisher: | Frontiers Media |
Refereed: | Yes |
Identification Number: | 10.3389/fphys.2017.00765 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124542 |
Download
Filename: Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics.pdf
Licence: CC-BY 4.0