Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.

OBJECTIVE: Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a novel density Poincaré plot-based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. METHODS: First, we propose the generation of this new density Poincaré plot which is derived from the difference of the heart rate (DHR) and provides the overlapping phase-space trajectory information of the DHR. Next, from this density Poincaré plot, several image processing domain-based approaches including statistical central moments, template correlation, Zernike moment, discrete wavelet transform and Hough transform features are used to extract suitable features. Subsequently, the infinite latent feature selection algorithm is implemented to rank the features. Finally, classification of AF vs. PAC/PVC is performed using K-Nearest Neighbor, Support vector machine (SVM) and Random Forest (RF) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 10 AF and 10 PAC/PVC subjects. Results- During the segment-wise 10-fold cross-validation, SVM achieved the best performance with 98.99% sensitivity, 95.18% specificity and 97.45% accuracy with the extracted features. In subject-wise scenario, RF achieved the highest accuracy of 91.93%. Moreover, we further validated the proposed method using two other databases: wearable armband ECG data and the Physionet AFPDB. 100% PAC detection accuracy was obtained for both databases without any further training. CONCLUSION: Our proposed density Poincaré plot-based method showed superior performance when compared with four existing algorithms; thus showing the efficacy of the extracted image domain-based features. SIGNIFICANCE: From intensive care unit's ECG to wearable armband ECGs, the proposed method is shown to discriminate PAC/PVCs from AF with high accuracy.

Preliminary Results on Density Poincare Plot Based Atrial Fibrillation Detection from Premature Atrial/Ventricular Contractions.

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.

Digital Image Processing Features of Smartwatch Photoplethysmography for Cardiac Arrhythmia Detection.

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.