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

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 2; pp. 448 - 460
• Main Authors: Bashar, Syed Khairul, Han, Dong, Zieneddin, Fearass, Ding, Eric, Fitzgibbons, Timothy P., Walkey, Allan J., McManus, David D., Javidi, Bahram, Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Premature Complexes - diagnosis; Cardiac arrhythmia; correlation; Density; density Poincaré plot; Discrete Wavelet Transform; Domains; EKG; Electrocardiography; Feature extraction; Fibrillation; Heart Atria; Heart rate; Heart rate variability; Hough transformation; Humans; Image processing; Intensive care; Learning algorithms; Machine Learning; MIMICs; Picture archiving and communication systems; Premature atrial contraction; premature ventricular contraction; random forest; Signal Processing, Computer-Assisted; Support vector machines; SVM; Ventricle; Ventricular Premature Complexes - diagnosis; wavelet transform; Wavelet transforms; Wearable technology
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.3004310

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.