Epileptic seizure prediction based on features extracted from lagged Poincaré plots

Abstract

Objective

The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR).

Methods

In this article, the Poincaré plots with six different lags (1–6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points.

Results

The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor.

Conclusion

The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.

Keywords:

• Angle
• density
• epilepsy
• heart rate variability
• lagged Poincaré plot
• prediction

Acknowledgments

European Project EPILEPSIAE (EU FP7 Grant 211713) and ICIS project CENTRO-07-0224-FEDER-002003 partially funded this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.