Medical Remote Sensing and Internet of Things for Detection of Epileptic Seizure Using Deep Neural Networks

Medical Remote sensing and the Internet of Things (IoT) have emerged as powerful tools in the field of disease detection and monitoring. Early detection of infectious diseases is crucial in order to prevent outbreaks and minimize their impact on public health. This paper aims to explore suitable methods and algorithms that can effectively utilize medical remote sensing data and IoT devices to detect and identify infectious diseases at an early stage. In this research, the novel real-time personal health monitoring system WISE (Wearable IoT-cloud-based health monitoring system) is introduced. In the present research, the body area network (BAN) is used to collect the patient's EEG signal. These signals are sent from the patient's location to the cloud via wifi. To turn the EEG input signal into an image, the discrete wavelet transform (DWT) is initially employed. This EEG input image first undergoes data de-noising pre-processing. Wavelet decomposition of EEG data accurately captures and pinpoints transient patterns in both temporal and frequency domains. Classifiers based on multilayer perceptron neural networks (MLPNN) were created and their classification accuracy for EEG signals was tested. . As compared to the existing algorithm the accuracy rate in predicting an abnormal EEG was 98.1 %. The classifier is employed to assess whether a region is typical or anomalous, and it also gauges its effectiveness.