Cardiac autonomic changes in epilepsy

The term "Epilepsy" encompasses a broad spectrum of medical and social disorders that affect about 65 million people worldwide and is commonly defined as a tendency to suffer recurrent seizures. In patients with epilepsy, ictal discharges that occur in (or propagate to) the anterior cingulate, insular, posterior orbito-frontal, and the pre-frontal cortices, along with the amygdala and hypothalamus play a key role in influencing the autonomic nervous system (ANS) at the cortical level. In turn, this can result in cardiac effects which are widespread and range from subtle changes in heart rate variability (HRV) to ictal sinus arrest, and from QT-interval shortening to atrial fibrillation. In addition, cardiac events are the main hypothesized mechanisms underlying sudden unexpected death in epilepsy (SUDEP), which occurs in absence of a known structural cause. Patients with epilepsy also experience long-lasting changes in the regulation of the ANS and target organs. Heart rate (HR) and HRV can be easily measured/estimated when compared to other biomarkers that are commonly associated with seizures (i.e., long-term EEG), and are therefore potentially valuable biomarkers when it comes to characterizing seizures. In this context, a number of linear and nonlinear analysis techniques have been applied in order to detect and characterize epilepsy-related ANS changes. While the physiological and clinical applicability of nonlinear analyses like fractal and complexity measures of HR dynamics are not yet completely understood, in view of recent experimental findings it is reasonable to assume that such indices highlight abnormal patterns of RR interval behaviour that are not easily detected by commonly used moment statistics of HR variation. These findings may provide new insight regarding physiological and seizure- induced states of the complex brain-heart network underlying epilepsy and related autonomic modifications. A better understanding of the autonomic manifestations of seizures would provide practical added value to clinical epileptologists dealing with differential diagnosis of epilepsy and related disorders, as well as aiding in designing more sensitive seizure detection and prediction algorithms.