eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism

Abstract

Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment.