Key result
Automatic detection of atrial fibrillation using heart rate variability and spectral features achieved 92.0% to 96.6% accuracy for two-class classification and 82.0% for three-class classification.
Why the study?
Does an automatic detection method based on HRV and spectral features accurately detect atrial fibrillation from short single-lead ECG recordings?
Does an automatic detection method based on HRV and spectral features accurately detect atrial fibrillation from short single-lead ECG recordings?
Nonlinear descriptors of HRV and spectral features can efficiently and robustly detect atrial fibrillation from short single-lead ECG recordings with high accuracy.
May aid AF screening algorithm development; leaves open prospective clinical validation.
Atrial fibrillation (AF) is one of the most common sustained arrhythmias, affecting about 1% of the population around the world. Rapid popularization of portable and wearable devices in recent years makes widespread personalized and mobile healthcare get closer to reality than ever before. This paper presents a method aiming for automatic detection of AF from short single lead electrocardiogram (ECG) recordings. Since AF is a kind of arrhythmia being likely to alter the dynamics of heart rhythms and/or the morphological characteristics in ECG tracings, heart rate variability (HRV)-based metrics and frequency analysis are adopted as feature extractors. We validate our method on a public available data set comprised of short ECG recordings of normal rhythm (N), AF (A), and other arrhythmias (O) by support vector machine and bagging trees. For two-class classification problems (N versus A), accuracy varies from 92.0% to 96.6% under different additional noise levels. For three-class classification problem (N versus A versus O), accuracy as high as 82.0% is obtained. Experimental results suggest than even for a relatively short ECG recording, nonlinear descriptors of HRV are still efficient and robust for AF detection.
No takes yet. Share an insight, caveat, or question.
Mei et al. (2018) studied Atrial fibrillation. Automatic detection of AF based on heart rate variability and spectral features was evaluated on Classification accuracy for two-class (normal vs AF) and three-class (normal vs AF vs other) problems. Automatic detection of atrial fibrillation using heart rate variability and spectral features achieved 92.0% to 96.6% accuracy for two-class classification and 82.0% for three-class classification.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: