Detection of epileptic seizures by means of morphological changes in the ECG

Epilepsy strongly affects the autonomic nervous system. Control mechanisms such as the one regulating the heart rate can be deeply affected during epileptic seizures. This effect of epilepsy can be measured in the ECG signal. In this paper, ECG segments of 35 children suffering from refractory epilepsy are studied. The goal is to determine whether pre-ictal, ictal or post-ictal tachycardia is present in partial and generalized seizures. A new set of features extracted from the ECG is proposed. These features are derived by means of principal component analysis (PCA) of a matrix formed by consecutive QRS-complexes, and they measure the heterogeneity of the QRS along the ECG. This new set of features together with the RR interval series is used to detect seizure onsets. Three approaches are implemented, namely thresholding, k-means and kernel spectral clustering (KSC). The best positive predictive value (PPV) was 85.7% for partial seizures, and 57.3% for generalized seizures.