Detection of atrial fibrillation using discrete-state Markov models and Random Forests

In this paper, we present a fully automated technique for robust detection of Atrial Fibrillation (AF) episodes in single-lead electrocardiogram (ECG) signals using discrete-state Markov models and Random Forests. Methods: The ECG signal is first preprocessed using Stationary Wavelet Transforms (SWT...

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Bibliographic Details
Published in:Computers in biology and medicine 2019-10, Vol.113, p.103386-103386, Article 103386
Main Authors: Kalidas, Vignesh, Tamil, Lakshman S.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arrhythmia
Atrial fibrillation
Autocorrelation
Automation
Cardiac arrhythmia
Cardiology
Datasets
Discrete-state markov models
EKG
Electrocardiograms
Electrocardiography
Fibrillation
Heart rate
Markov chains
Noise
Post-production processing
Quality assessment
Quality control
Random forests
RR-interval analysis
Sensitivity
Sensors
Signal quality
Stationary wavelet transforms
Wavelet transforms
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Summary:In this paper, we present a fully automated technique for robust detection of Atrial Fibrillation (AF) episodes in single-lead electrocardiogram (ECG) signals using discrete-state Markov models and Random Forests. Methods: The ECG signal is first preprocessed using Stationary Wavelet Transforms (SWT) for noise suppression, signal quality assessment and subsequent R-peak detection. Discrete-state Markov probabilities modelling transitions between successive RR intervals along with other statistical quantities derived from the RR-interval series constitute the feature set to perform AF classification using Random Forests. Further enhancement in AF detection is achieved by using a post-processing false positive suppression algorithm based on autocorrelation analysis of the RR-interval series. Datasets: The AF classifier was trained using the Physionet/Computing in Cardiology 2017 AF Challenge dataset and the Atrial Fibrillation Termination Database (AFTDB). The test datasets consist of the MIT-BIH Atrial Fibrillation Database (AFDB) and the MIT-BIH Arrhythmia Database (MITDB). Results: Our algorithms achieved sensitivity, specificity and F-score values of 97.4%, 98.6% and 97.7% respectively on the AFDB dataset and 96.3%, 97.0% and 85.6% respectively on the MITDB dataset. It was also observed that inclusion of the false positive suppression step resulted in a 1.1% increase in specificity and a 4.0% increase in F-score for the MITDB dataset without any decrease in sensitivity. Conclusion: The proposed method of AF detection, combining Markov models and Random Forests, achieves high accuracy across multiple databases and demonstrates comparable or superior performance to several other state-of-the-art algorithms. •An automated Atrial Fibrillation (AF) detection technique based on RR-interval analysis using single-lead ECGs is proposed.•Features from a nine-state Markov model, trained using Random Forests, resulted in robust noise estimation.•Features from an eight-state Markov model, trained using Random Forests, resulted in accurate AF detection.•An additional false positive suppression step based on autocorrelation analysis further improved AF classification accuracy.•Proposed methodology achieved F-scores of 97.7% on the AFDB database and 85.6% on the MITDB database respectively.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2019.103386