Automatic Detection of Tonic-Clonic and Myoclonic Epileptic Seizures Using Prefrontal Electroencephalography (EEG)

Abstract

Epilepsy is a neurological disease that affects about 50 million people worldwide. It is a disorder of the central nervous system, characterized by recurrent seizures that can have a massive impact in the physical and mental health of the people who suffer from it, as well as their loved ones. Long-term monitoring of epilepsy in uncontrolled environments is key to provide accurate characterization of the disease, and to create tools that improve the patients lives. Although some wearable devices (particularly with motion and cardiac-based sensors) are quickly gaining ground as everyday use monitoring devices, in the scope of epilepsy, electroencephalography (EEG) remains the gold-standard. Therefore, it is of utmost importance to include this modality in ambulatory settings, leveraging its extensive presence in literature and available databases. Nevertheless, in long-term recordings, having information about the onset of the seizure is of utmost importance for effective analysis of the collected data. Hence, this work explores the use of a single-channel, nonintrusive, EEG configuration in automatic seizure detection, with the purpose of event annotation in long-term recordings. This is a key element to the creation of multimodal datasets that can be used in seizure detection and, eventually, prediction, as well as towards comprehensive multimodal epilepsy monitoring techniques. A seizure-specific Support Vector Machines (SVM) classifier was designed for labeling eight different types of seizure, using a limited-channel configuration (Fp1-Fp2). Our work uses the TUH EEG Seizure Corpus, for which encouraging results were achieved for tonic-clonic and myoclonic seizures, with sensitivities of 98.9% and 98.2%, as well as precisions of 100% and 99.8%, respectively.