<![CDATA[Cardiovascular diseases (CVDs) remain the fore- most 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 profes- sionals, 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 MIT- BIH Arrhythmia, PTB Diagnostic ECG, and Kaggle databases. Empirical evaluation results demonstrate high model perfor- mance, 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, user- friendly technology. Index Terms—Electrocardiogram, MLP, deep learning, ar- rhythmia, bradycardia, tachycardia, HRV, desktop health appli- cation, cloud computing, RLS, Supabase, Flutter]]>
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