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In this study, we propose a P-wave absence (PWA) based method for atrial fibrillation (AF) identification over a short duration of electrocardiogram (ECG). The algorithm constructs a statistical model of normal sinus rhythm (SR) P-waves using a training set. Features extracted from P-waves are taken as an input to the Expectation–Maximization algorithm to create a Gaussian mixture model (GMM) of the...
In this study we propose a novel atrial activity-based method for atrial fibrillation (AF) identification that detects the absence of normal sinus rhythm (SR) P-waves from the surface ECG. The proposed algorithm extracts nine features from P-waves during SR and develops a statistical model to describe the distribution of the features. The Expectation-Maximization algorithm is applied to a training...
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