KEYWORDS: Arrhythmia, Signal detection, Feature extraction, Artificial neural networks, Pulse signals, Photoplethysmography, Atrial fibrillation, Electrocardiography, Medical research, Education and training
Photoplethysmography (PPG) is a non-invasive optical-based technique used to measure various hemodynamic parameters. State-of-the-art proposed various methods for arrhythmia (premature ventricular contraction (PVC), atrial fibrillation) detection using PPG signals. However, restricted research has been carried out for detecting other arrhythmias that could be life-threatening. In this research work, the detection of atrial flutter (AFl) from Normal, Sinus Tachycardia (ST), and PVC signals have been carried out using PPG signals. The method relies on time-domain and entropy features for characterizing the AFl PPG pulse. A sliding window approach has been applied to extract features, and an artificial neural network has been implemented for feature classification. The ground-truth generation for the PPG signals has been carried out on publically available and prospective data. The comparative analysis of the results obtained from the two datasets is useful in the effective identification of the abnormality.
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