Abstract
Atrial fibrillation, which is the most common cardiac arrhythmia, is typically classified into four clinical subtypes: paroxysmal, persistent, long-standing persistent and permanent. The ability to distinguish between them is of crucial significance in choosing the most suitable therapy for each patient. Nevertheless, classification is currently established once the natural history of the arrhythmia has been disclosed as it is not possible to make an early differentiation. This paper presents a novel method to discriminate persistent and long-standing atrial fibrillation patients by means of a time-frequency analysis of the surface electrocardiogram. Classification results provide approximately 75% accuracy when evaluating ECGs of consecutive unselected patients from a tertiary center and higher than 80% when patients are not under antiarrhythmic treatment or do not have structural heart disease (76% sensitivity and 88% specificity). Moreover, to our knowledge, this is the first study that discriminates between persistent and long-standing persistent subtypes in a heterogeneous population sample and without discontinuing antiarrhythmic therapy to patients. Thus, it can help clinicians to address the most suitable therapeutic approach for each patient.
Acknowledgments
This work was supported by Generalitat Valenciana under grant PrometeoII/2013/013 and by MINECO under grants MTM2010-15200, MTM2013-43540-P.
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©2016 by De Gruyter
Articles in the same Issue
- Frontmatter
- Editorial
- Biosignal processing
- Review
- A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals
- Research articles
- Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms
- Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation
- P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference
- Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients
- A portable device for recording evoked potentials, optimized for pattern ERG
- Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier
- Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal
- Nonlinear analysis of pupillary dynamics
- A multichannel bioimpedance monitor for full-body blood flow monitoring
- Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model
- Short communication
- Quantifying the complexity of human colonic pressure signals using an entropy measure
Articles in the same Issue
- Frontmatter
- Editorial
- Biosignal processing
- Review
- A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals
- Research articles
- Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms
- Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation
- P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference
- Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients
- A portable device for recording evoked potentials, optimized for pattern ERG
- Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier
- Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal
- Nonlinear analysis of pupillary dynamics
- A multichannel bioimpedance monitor for full-body blood flow monitoring
- Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model
- Short communication
- Quantifying the complexity of human colonic pressure signals using an entropy measure