Epilepsy is the most common neurological disorder characterized by unprovoked and unexpected electrical bursts in the brain, which results in seizures. Initially, most epilepsy treatments lie on antiepileptic drugs (AEDs). However, among the 0.5-1% of the pediatric population suffering from epilepsy, about 25-30% of patients have drug-resistant epilepsy, which is defined as the failure of adequate trials of two tolerated, appropriately chosen and administered AEDs (whether as monotherapy or in combination) to achieve seizure freedom. The severity of the seizure can lead to SUDEP (sudden unexpected death in epilepsy). Additionally, the quality of life (QoL) of these patients highly depends on other comorbidities such as cognitive impairment, depression, medication side effects, sleep quality, and privacy, and importantly, the unpredictability of seizure occurrence. A closed-loop warning system is indispensable to improve the QoL of these patients, which includes continuous monitoring of patient's data, seizure prediction (before seizure onset) and detection (during seizure onset), maintaining electronic seizure diary, and providing right dosages of AEDs.
So far, video-Electroencephalography (EEG) is the gold standard for monitoring seizures, which is obtrusive and restricts the patient's daily life. Therefore, wearable technology is evolving in this field to provide an unobtrusive measurement. Within the framework of this project, pre-ictal and inter-ictal heart rate data will be collected from a wearable sensor to give a non-obtrusive and non-EEG based seizure prediction method in real-time. This system will provide an alarm to the patients or caregivers before seizure onset, which will eventually replace the patient-reported seizure diary with an automatic one. The AED dosages will be provided based on this diary, and the effects of AEDs on the seizure frequency will be analyzed.