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-bas...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2021-02, Vol.68 (2), p.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: Article
Language:English
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
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Summary: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.
ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2020.3004310