O-AD006. Heart rate variability as a tool for predicting epileptic seizures

Introduction. Epilepsy is a neurological disorder that affects the patient’s quality of life and health care budget. Autonomic involvement, particularly respiratory and cardiac involvement is extremely dangerous. A particularly life-threatening complication is SUDEP. HRV represents ANS activity. We aim to compare the HRV in preictal, interictal and postictal period in patient with various types of epilepsy. Methods. Patients admitted for presurgical evaluation for epilepsy surgery and patients with epilepsy admitted for prolonged EEG monitoring (>8 hours) having various types of seizures were selected. Amongst these 49 patients, 142 seizures with various semiologies were recorded and were sent for processing. ECG signal was extracted from EEG data. Signal processing and HRV extraction was performed using EDF browser based on MATLAB. Time domain measurements like mean HR, RRI, RMSSD, SDNN and pNN50 were analyzed at different times – 20 min before the seizure, 1 minute before the seizure, during the seizure and 20 min after the seizure for each individual event. Results. Mean age was 27.14 ± 12.15 years with a minimum age of 5 years and maximum age of 58 years. Out of a total of 142 events recorded, 23 events were focal seizures with preserved consciousness, 73 events were focal seizures with impaired consciousness, 45 events were generalized seizures and 1 was a psychogenic seizure. There was significant rise in the mean HR and a drop in RRI, RMSSD, SDNN and pNN50 in the immediate preictal and interictal period. A comparison of various time intervals compared with respect to type of event showed that generalized event had a more autonomic dysregulation compared to focal seizures. Conclusion. HRV analysis is a valid method for quantifying central influences on autonomic nervous system and its cardiac control, and can give important information when used as an adjunct during clinical seizures.