Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation

Abstract

Background and Objectives: Atrial fibrillation (AF) is the most common sustained cardiac ar- rhythmia 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 au- tomatic 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 electrocardio- gram (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 meth- od 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 pres- ence 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 advan- tages. 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 imple- mentation easier than previous methods requiring combined indices under complex classifiers.