Affiliations: [a] Department of Electrical Engineering, Islamic Azad University, Garmsar, 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), University of Coimbra, Coimbra, Portugal
Correspondence:
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Corresponding author: Soroor Behbahani, Department of Electrical Engineering, Islamic Azad University, Garmsar, Iran. Tel.: +98 2177874289; Fax: +98 2188675452; E-mail:sor.behbahani@gmail.com
Abstract: OBJECTIVE: Epileptic onsets often affect the autonomic function of
the body during a seizure, whether it is in ictal, interictal or post-ictal
periods. The different effects of localization and lateralization of
seizures on heart rate variability (HRV) emphasize the importance of
autonomic function changes in epileptic patients. On the other hand, the
detection of seizures is of primary interests in evaluating the epileptic
patients. In the current paper, we analyzed the HRV signal to develop a
reliable offline seizure-detection algorithm to focus on the effects of
lateralization on HRV. MATERIALS AND METHODS: We assessed the HRV during 5-min segments of
continuous electrocardiogram (ECG) recording with a total number of 170
seizures occurred in 16 patients, composed of 86 left-sided and 84
right-sided focus seizures. Relatively high and low-frequency components of
the HRV were computed using spectral analysis. Poincaré parameters of
each heart rate time series considered as non-linear features. We
fed these features to the Support Vector Machines (SVMs) to find a robust
classification method to classify epileptic and non-epileptic signals. Leave
One Out Cross-Validation (LOOCV) approach was used to demonstrate the
consistency of the classification results. RESULTS: Our obtained classification accuracy confirms that the
proposed scheme has a potential in classifying HRV signals to epileptic and
non-epileptic classes. The accuracy rates for right-sided and left-sided
focus seizures were obtained as 86.74% and 79.41%, respectively. CONCLUSIONS: The main finding of our study is that the patients
with right-sided focus epilepsy showed more reduction in parasympathetic
activity and more increase in sympathetic activity. It can be a marker of
impaired vagal activity associated with increased cardiovascular risk and
arrhythmias. Our results suggest that lateralization of the seizure onset
zone could exert different influences on heart rate changes. A right-sided
seizure would cause an ictal tachycardia whereas a left-sided seizure would
result in an ictal bradycardia.
Keywords: Epileptic seizures, lateralization, HRV, support vector machines, classification
