Neuro-Detect: A Machine Learning-Based Fast and Accurate Seizure Detection System in the IoMT

Abstract

Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably negative impact on both the quality and the expectancy of life of the patient. Approximately 3.4 million individuals in the USA and up to 1% of the world population is afflicted by epilepsy. This necessitates the real-time detection of seizures which can be done by the use of an Internet of Things (IoT) framework for smart healthcare. In this paper, we propose an electroencephalogram (EEG)-based seizure detection system in the IoT framework which uses the discrete wavelet transform (DWT), Hjorth parameters (HPs), statistical features, and a machine learning classifier. Seizure detection is done in two stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and features (activity, signal complexity, and standard deviation) were extracted from each of these sub-bands. In the second stage, a deep neural network (DNN) classifier is used to classify the EEG data. A prototype of the proposed neuro-detect was implemented using the hardware-in-the-loop approach. The results demonstrate a significant difference in HP values between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. In this approach, we report an accuracy of 100% for a classification of normal versus ictal EEG and 98.6% for normal and interictal versus ictal EEG.