Automated atrial fibrillation classification based on denoising stacked autoencoder and optimized deep network

Abstract

This study proposed an efficient ECG preprocessing technique, and the preprocessed ECG data was subsequently used to classify atrial fibrillation (AFib) using end-to-end deep neural networks. This is significant since early identification of AFib can help to prevent mortality. With this in mind, the two-fold study is proposed, in which three denoising autoencoders for ECG signal pre-processing were evaluated and compared. Denoising autoencoders (DAEs) utilising convolutional neural networks (CNNs) as backbones outperformed the other two DAE models. As a result, the preprocessed signal is then used to classify atrial fibrillation using the three classification models. The combination of CNN-based DAE and CNN-based classification model yielded the best results, with 99.20 percent accuracy, 99.50 percent specificity, 99.50 percent sensitivity, and 99.00 percent true positive rate. The average accuracy of the algorithms we investigated was 96.26 percent, and our technique was 3.2 percent more accurate than the other algorithms we compared. Furthermore, with a 24-h ECG signal processing time of only 1.3 s, the proposed model is computationally inexpensive for real-time applications. To determine its robustness, the proposed framework is tested on previously unseen datasets with varied proportions of arrhythmias, producing a 99.10 percent recall rate and a 98.50 percent accuracy.