Learning to detect the onset of slow activity after a generalized tonic–clonic seizure

Abstract

Abstract Background Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8–17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https://sbmi.uth.edu/hackathon/archive/sept19.htm . Methods In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic–clonic seizure, as well as other EEG signal detection problems exhibiting data paucity. Results We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence. Conclusions Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.

Keywords

• Electroencephalogram
• Sudden death in epilepsy
• Generalized tonic–clonic seizure
• Onset of slow activity
• Signal detection
• Machine learning