Automated arrhythmia classification for monitoring cardiac patients using machine learning techniques
Rekha Rajagopal Department of Information Technology, PSG College of Technology, Coimbatore, India
Abstract
People with chronic illnesses, such as cardiovascular disease, stroke, and diabetes, can be continuously monitored with the help of remote patient monitoring (RPM) systems. The major concern in RPM systems, which were developed to monitor cardiac patients, is identification of the correct arrhythmia class from the electrocardiogram signal. Existing arrhythmia classification techniques report low accuracy for a few arrhythmia classes due to class overlap and class imbalance problems. Arrhythmias are classified into five classes: ...
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