Research Area:  Machine Learning

Abstract:

Electrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

Keywords:  

Author(s) Name:  Adriana Leal, Mauro F. Pinto, Fábio Lopes, Anna M. Bianchi, Jorge Henriques, Maria G. Ruano, Paulo de Carvalho, António Dourado & César A. Teixeira

Publisher name:  Springer Nature

DOI:  10.1038/s41598-021-85350-y

Volume Information:  volume 11

Paper Link:   https://www.nature.com/articles/s41598-021-85350-y