A machine learning and blockchain-enabled framework for remote ECG monitoring achieved classification accuracies ranging from 90.00% to 95.71% across five patient urgency categories.
A novel machine learning and blockchain-based framework for remote ECG monitoring demonstrates high classification accuracy for urgent patient categories in simulation.
Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems.
Samaraweera et al. (Sat,) conducted a other in Cardiovascular diseases. Machine Learning-Enabled Secure Unified Framework was evaluated on Classification accuracy for patient urgent categories U1 to U5. A machine learning and blockchain-enabled framework for remote ECG monitoring achieved classification accuracies ranging from 90.00% to 95.71% across five patient urgency categories.