<![CDATA[Cardiovascular diseases (CVDs) remain the foremost cause of mortality globally, necessitating the development of advanced tools for early and accurate cardiac diagnosis. This paper presents the comprehensive design, implementation, and evaluation of a desktop-based Electrocardiogram (ECG) monitoring system. The system architecture integrates a powerful Multi-Layer Perceptron (MLP) deep learning model designed to automatically identify and classify critical heart rhythm abnormalities, including bradycardia, tachycardia, and other forms of arrhythmia. A cornerstone of this system is its seamless and secure integration with a Supabase cloud backend, which facilitates centralized data storage, real-time synchronization, and secure, role-based access for various healthcare professionals, rigorously enforced through PostgreSQL’s Row Level Security (RLS). The MLP model was trained and validated on a diverse and extensive collection of data from the MITBIH Arrhythmia, PTB Diagnostic ECG, and Kaggle databases. Empirical evaluation results demonstrate high model performance, with classification accuracies reaching 92% for both bradycardia and tachycardia, and 89% for general arrhythmia detection. Functional and performance testing further validate the system’s operational reliability, showing an average cloud data synchronization time of approximately 4 seconds and robust, though partially incomplete, RLS policy enforcement. This work contributes a scalable, accurate, and secure solution for advanced cardiac monitoring in desktop environments, effectively bridging the gap between clinical-grade analysis and accessible, userfriendly technology]]>
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