Key result
Ensembled deep neural networks achieve ~94% AF classification accuracy using Stockwell transform patterns.
Why the study?
Detection of AF from ECG recordings is challenging due to complex characteristics, and manual observation is tedious, time-consuming, and error-prone.
An ensembled deep neural network using Stockwell transform time-frequency patterns of ECG signals achieved high accuracy (93.70%) in identifying atrial fibrillation.
Atrial fibrillation (AF) is a life threatening cardiac abnormality having high prevalence and risk with increased rate of stroke and systemic embolism, if oral anticoagulation is not recommended. Later, this leads to morbidity and mortality. Detection of AF is challenging from the electrocardiogram (ECG) recordings, due to its complex characteristics. Manual observation of ECG is tedious, time consuming, and error prone. This manuscript proposed a novel approach for identifying AF in the presence of noise and other beats by using deep neural networks (DNN) on the 2-D patterns obtained by various time–frequency analysis methods, such as short time Fourier transform (STFT), Chirplet-transform, Stockwell-transform, and Poincare plot from 1-D preprocessed ECG recordings. The above discussed methods identify the variations due to AF in ECG. Initially, the patterns are used by the pretrained DNN models for classification. ResNet18 attained the highest accuracy of 90.56<tex-math notation="LaTeX">$%$</tex-math> for the patterns of ECG obtained by Chirplet-transform on PAF prediction challenge database, whereas Chirplet patterns used by ResNet50 achieved an accuracy of 89.72<tex-math notation="LaTeX">$%$</tex-math>. Based on accuracy and the number of parameters in DNN, an ensembled network is designed for improving AF classification. Ensembling of ShuffleNet and AlexNet applied over the patterns obtained by Stockwell transform achieved the highest accuracy of 93.70<tex-math notation="LaTeX">$%$</tex-math>. This approach is further experimented on PhysioNet CinC 2017 challenge database, consisting of four classes (such as AF, normal, other rhythms, and noise).
No takes yet. Share an insight, caveat, or question.
Anbalagan et al. (2024) studied Atrial fibrillation. Deep neural networks on 2-D patterns from time-frequency analysis was evaluated on Classification accuracy. An ensembled deep neural network of ShuffleNet and AlexNet applied to Stockwell transform patterns achieved 93.70% accuracy for atrial fibrillation classification.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: