Abstract
This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sensors, where IoT-based sensor data is collected and encrypted in a central database for subsequent analysis. The novelty of the work lies in the proposed AI-based software component rather than the hardware assembly, which utilizes accessible components. The developed system is designed to function as a decision support system for healthcare personnel, providing necessary information and alerts through mobile and desktop interfaces. Data obtained from the patient is classified using the proposed deep learning method, and a detailed summary is presented. The customized DNN-based model demonstrated a test accuracy of 99.94%, with a recall of 99.92% and a precision of 99.57%, results which indicate a strong potential for clinical application due to very low false positive and false negative rates. Based on this high accuracy, the model's outputs have been integrated into user-friendly interfaces to assist healthcare personnel. It is therefore suggested that the patient monitoring system, featuring this high-performance classification model, has the potential to contribute to the early and more reliable detection of significant diseases such as heart abnormalities and arrhythmia.
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