Internet of Medical Things (IoMT) for Premature Estimate of Epileptic Seizures

• Published in: 2023 16th International Conference on Developments in eSystems Engineering (DeSE) pp. 350 - 355
• Main Authors: Alrawi, Ali Amer Ahmed, Mashhadany, Yousif Al, Najeeb, Mushtaq, Hadi, Sura A., Algburi, Sameer, Graovac, Stevica
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18-12-2023
• Subjects: ANNs; Epilepsy; Instruments; Internet of Medical Things; Internet of Things; Machine learning; Machine learning algorithms; mobile cloud; Mobile handsets; Sensor systems; Sensors
• DOI: 10.1109/DeSE60595.2023.10469228

This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamlessly accesses MATLAB capabilities and uses MATLAB Drive for cloud-based data storage and analysis. Time-domain and frequency-domain analysis turn acceleration impulses into meaningful feature vectors. Then, machine learning classifiers like SVMs or deep learning models are used to train the system on a seizure and non-seizure dataset. The algorithm is calibrated to distinguish s [e-mail] eizure occurrences from regular activity. The IoMT technique uses real-time streaming to send sensor data to the cloud-based MATLAB Drive for continuous monitoring. The MATLAB Mobile app allows smartphone users to remotely monitor seizure activity using the seizure detection algorithm. The proposed IoMT Seizure Detection Algorithm is tested utilizing a broad dataset of seizure episodes and physical activities. Accuracy, sensitivity, specificity, and the ROC curve are evaluated. The algorithm's robustness and effectiveness in detecting seizure occurrences highlight its potential as a noninvasive, cost-effective early seizure detection system. MATLAB Mobile and MATLAB Drive integration shows that IoMT-based healthcare solutions implemented in real-world circumstances, improving patient care and medical insights. The auto-correlation between the patient's motion and the [] data helped clinicians decide if the patient's motion was typical or not. Up to 85% consistency was found while using our proposed method for detecting epilepsy.