Epileptic Seizure Prediction Using Heart Rate Variability Signal Analysis

Document Type : Original Article


1 Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Neuroscience Group, Shahid Beheshti University of Medical Sciences, Tehran, Iran


Epilepsy is a neural disorder with unknown nature and epileptic patients suffer from the consequences of unexpected seizures. In this paper, we proposed a new method to predict epileptic seizures using heart rate variability (HRV) signal analysis. During preictal period of epilepsy, increasing in nervous activities of neurons affects the autonomic nervous system that disturbs heart rates. Therefore, epileptic seizures can be predicted through HRV monitoring. In our method, we extracted 12 features of HRV signal from different domains: time, frequency, time-frequency and non-linear domain. We used Multivariate Statistical Process Control (MSPC) algorithm for anomaly detection which is able to detect anomalies that cannot be detected by monitoring each variable independently. This algorithm has been applied to the clinical data collected from 17 patients. The obtained results demonstrated that the proposed method can predict seizure onset with an accuracy of 88.2%. The proposed HRV-based seizure prediction algorithm is more promising than the conventional EEG-based methods from the viewpoint of practical use.


Main Subjects

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