Abstract

Early diagnosis and prevention of cardiovascular disorders depend on accurate detection of cardiac irregularities from electrocardiogram (ECG) data. In order to identify important cardiac disorders such tachycardia, sinus rhythm (SR), and ST depression, this study presents a cloud-based method for Lead-I ECG data processing that uses a One-Class Support Vector Machine (OC-SVM). Without the need for extensive labeled datasets, the suggested approach successfully distinguishes between normal and pathological ECG rhythms using unsupervised learning. By capturing QRS peaks and ST-segment deviations, feature extraction approaches improve the diagnostic accuracy of the model. Cloud MongoDB securely stores and manages processed ECG data and detection results, guaranteeing high scalability, data integrity, and effective access for distant analysis. According to experimental results, the suggested OC-SVM model greatly reduces false detection rates across a variety of ECG datasets while providing strong classification accuracy. The model reliably detects disorders including tachycardia, sinus rhythm (SR), and ST depression by successfully differentiating between normal and abnormal cardiac rhythms. The system's capacity for real-time ECG monitoring is improved by the integration of cloud computing with MongoDB, which offers safe data storage, quick query processing, and easy access from dispersed healthcare settings. Continuous data streaming from wearable ECG devices is made possible by this cloud-based architecture, which supports long-term cardiac analysis and extensive remote patient monitoring.

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