RT-NeuroDDSM: Real-Time EEG-Driven Diagnostic Decision Support Model for Neurological Disorders Using Deep Learning

Abstract

The internet of medical things (IoMT) has become a pivotal aspect of IoT applications, playing a crucial role in cutting down healthcare expenses, enhancing access to clinical services, and refining operational efficiency within the healthcare domain. An early detection of neurological brain disorders continues to present a formidable challenge. In response, our research endeavors are directed towards the IoMT-based system used for the real-time diagnosis of neurological disorders. In this paper, IoMT-based real-time diagnosis model is developed for neurological brain disorders using systematic deep learning and electroencephalogram (EEG) signal (RT-NeuroDDSM). We first introduce the time domain, channel and spatial attention network (TCSNet) for feature extraction which extracts the high-level time series, channel and spatial features, respectively. TCSNet aims to learn more valuable features from the input data to achieve good classification results. Furthermore, in order to maximize features, we create the modified normative fish swarm (MNFS) feature selection algorithm. Next, the detection of various neurological brain problems, including neuro-typical, epilepsy, and autism spectrum disorder (ASD), is accomplished by applying the hinging hyperplane neural network (HHNN). To verify the performance, we used the publicly accessible EEG datasets from University of Bonn-Germany, CHB-MIT EEG repository, and King Abdulaziz University. The RT-NeuroDDSM has an overall classification accuracy of 99.956%, making it 5.471% more in efficiency compared to the existing state-of-the-art model.

Keywords

• Autism
• biomedical signal processing
• deep learning
• digital healthcare
• electroencephalography
• epilepsy