Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. Issue 131 (July 2016)

• Record Type: Journal Article
• Authors: García, Manuel; Ródenas, Juan; Alcaraz, Raúl; Rieta, José J.
• Is Part Of: Computer methods and programs in biomedicine. Issue 131(2016)
• Page Start: 157
• Page End: 168
• Publication Date: 2016-07
• Subjects: Atrial fibrillation -- Automatic detection -- Electrocardiogram -- Relative wavelet energy -- Stationary wavelet transform; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Automatic detection of atrial fibrillation regardless of the heart rate is pursued. Relative stationary wavelet energy is analyzed from the surface TQ interval. The method is validated both by synthesized and real ECG recordings. Results shows a detection accuracy similar to previous works but with a shorter transition delay. Abstract: Background and Objectives: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing healthcare burden worldwide. It is often asymptomatic and may appear as episodes of very short duration; hence, the development of methods for its automatic detection is a challenging requirement to achieve early diagnosis and treatment strategies. The present work introduces a novel method exploiting the relative wavelet energy (RWE) to automatically detect AF episodes of a wide variety in length. Methods: The proposed method analyzes the atrial activity of the surface electrocardiogram (ECG), i.e., the TQ interval, thus being independent on the ventricular activity. To improve its performance under noisy recordings, signal averaging techniques were applied. The method's performance has been tested with synthesized recordings under different AF variable conditions, such as the heart rate, its variability, the atrial activity amplitude or the presence of noise. Next, the method was tested with real ECG recordings. Results: Results proved that the RWE provided a robust automatic detection of AF under wide ranges of heart rates, atrial activity amplitudes as well as noisy recordings. Moreover, the method's detection delay proved to be shorter than most of previous works. A trade-off between detection delay and noise robustness was reached by averaging 15 TQ intervals. Under these conditions, AF was detected in less than 7 beats, with an accuracy higher than 90%, which is comparable to previous works. Conclusions: Unlike most of previous works, which were mainly based on quantifying the irregular ventricular response during AF, the proposed metric presents two major advantages. First, it can perform successfully even under heart rates with no variability. Second, it consists of a single metric, thus turning its clinical interpretation and real-time implementation easier than previous methods requiring combined indices under complex classifiers.