Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Behbahani, Soroora; * | Dabanloo, Nader Jafarniab | Nasrabadi, Ali Motiec | Dourado, Antoniod
Affiliations: [a] Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Iran | [b] Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | [c] Department of Biomedical Engineering, Shahed University, Tehran, Iran | [d] Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Portugal
Correspondence: [*] Corresponding author: Soroor Behbahani, Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Iran. E-mail:sor.behbahani@gmail.com
Abstract: BACKGROUND: Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis. METHODS: We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds. RESULTS: For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor. CONCLUSION: The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.
Keywords: Epilepsy, HRV, prediction, threshold, circadian rhythm
DOI: 10.3233/THC-161225
Journal: Technology and Health Care, vol. 24, no. 6, pp. 795-810, 2016
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl