A novel algorithm using continuous wavelet transform and 2D convolutional neural networks achieved an overall accuracy of 99.23% for detecting atrial fibrillation using just five heartbeats.
Does an automatic detection algorithm based on continuous wavelet transform and 2D convolutional neural networks accurately detect atrial fibrillation episodes in ECG recordings?
A novel algorithm combining continuous wavelet transform and 2D convolutional neural networks demonstrated >99% accuracy in detecting atrial fibrillation from short 5-beat ECG segments.
Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.
He et al. (Thu,) conducted a other in Atrial Fibrillation (n=21). Continuous Wavelet Transform and 2D Convolutional Neural Networks vs. Existing AF detection algorithms was evaluated on Overall accuracy (ACC) of AF detection. A novel algorithm using continuous wavelet transform and 2D convolutional neural networks achieved an overall accuracy of 99.23% for detecting atrial fibrillation using just five heartbeats.