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The aim of our work is to design an algorithm to detect premature atrial contraction (PAC), premature ventricular contraction (PVC), and atrial fibrillation (AF) among normal sinus rhythm (NSR) using smartwatch photoplethysmographic (PPG) data. Novel image processing features and two machine learning methods are used to enhance the PAC/PVC detection results of the Poincaré plot method. Compared with support vector machine (SVM) methods, the Random Forests (RF) method performs better. It yields a 10-fold cross validation (CV) averaged sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy for PAC/PVC labels of 63%, 98%, 83%, 94%, and 93%, respectively, and a 10-fold CV averaged sensitivity, specificity, PPV, NPV, and accuracy for AF subjects of 92%, 96%, 85%, 98%, and 95%, respectively. This is one of the first studies to derive image processing features from Poincaré plots to further enhance the accuracy of PAC/PVC detection using PPG recordings from a smartwatch.

Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is challenging as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a preliminary study of using density Poincare plot based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. First, we propose creation of this new density Poincare plot which is derived from the difference of the heart rate. Next, from this density Poincare plot, template correlation and discrete wavelet transform are used to extract suitable image-based features, which is followed by infinite latent feature selection algorithm to rank the features. Finally, classification of AF vs PAC/PVC is performed using K-Nearest Neighbor, discriminant analysis and support vector machine (SVM) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 8 AF and 8 PAC/PVC subjects. Both 10-fold and leave-one-subject-out cross validations are performed to show the robustness of our proposed method. During the 10-fold cross-validation, SVM achieved the best performance with 99.49% sensitivity, 94.51% specificity and 97.29% accuracy with the extracted features while for the leave-one-subject-out, the highest overall accuracy is 90.91%. Moreover, when compared with two state-of-the-art methods, the proposed algorithm achieves superior AF vs. PAC/PVC discrimination performance.Clinical Relevance-This preliminary study shows that with the help of density Poincare plot, AF can be separated from PAC/PVC with better accuracy.