TY - JOUR N2 - Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal. L1 - http://www.journals.pan.pl/Content/119462/PDF/06_01971_Bpast.No.69(3)_23.06.21_Druk.pdf L2 - http://www.journals.pan.pl/Content/119462 PY - 2021 IS - 3 EP - e136921 DO - 10.24425/bpasts.2021.136921 KW - epilepsy KW - seizure detection KW - seizure prediction KW - convolutional neural network KW - deep learning KW - ECG KW - HRV KW - cardiac sympathetic index A1 - Kołodziej, Marcin A1 - Majkowski, Andrzej A1 - Tarnowski, Paweł A1 - Rak, Remigiusz Jan A1 - Rysz, Andrzej VL - 69 DA - 22.03.2021 T1 - A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network SP - e136921 UR - http://www.journals.pan.pl/dlibra/publication/edition/119462 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -