Heart rate variability as a biomarker for epilepsy seizure prediction

Epilepsy is a neurological disorder that causes seizures of many different types. Recent research has shown that epileptic seizures can be predicted by using the electrocardiogrami instead of the electroencephalogram. In this study, we used the heart rate variability that is generated by the fluctuating balance of sympathetic and parasympathetic nervous systems to predict epileptic seizures.

We studied 11 epilepsy patients to predict the seizure interval. With regar tos the fact that HRV signals are nonstationary, our analysis focused on linear features in the time and frequency domain of HRV signal such as RR Interval (RRI), mean heart rate (HR), high-frequency (HF) (0.15-0.40 Hz) and low-frequency (LF) (0.04-0.15 Hz), as well as LF/HF. Also, quantitative analyses of Poincaré plot features (SD1, SD2, and SD1/SD2 ratio) were performed. HRV signal was divided into intervals of 5 minutes. In each segment linear and nonlinear features were extracted and then the amount of each segment compared to the previous segment using a threshold. Finally, we evaluated the performance of our method using specificity and sensitivity.

During seizures, mean HR, LF/HF, and SD2/SD1 ratio significantly increased while RRI significantly decreased. Significant differences between two groups were identified for several HRV features. Therefore, these parameters can be used as a useful feature to discriminate a seizure from a non-seizure The seizure prediction algorithm proposed based on HRV achieved 88.3% sensitivity and 86.2 % specificity.

These results indicate that the HRV signal contains valuable information and can be a predictor for epilepsy seizure. Although our results in comparison with EEG ares a little bit weaker, the recording of ECG is much easier and faster than EEG. Also, our finding showed the results of this study are considerably better than recent research based on ECG (Tab. 1, Fig. 10, Ref. 17).