Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation.
OBJECTIVE
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.
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain.
Manuel
M
García
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain.
Juan
J
Ródenas
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Albacete, Spain. Electronic address: raul.alcaraz@uclm.es.
Raúl
R
Alcaraz
Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain.
José J
JJ
Rieta
D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans
Atrial fibrillation; Automatic detection; Electrocardiogram; Relative wavelet energy; Stationary wavelet transform