Enhancing Atrial Fibrillation detection accuracy: A wavelet transform filtered single lead ECG signal analysis with artificial neural networks and novel feature extraction

Atrial Fibrillation (AF) is a common cardiac arrhythmia that can lead to fatal outcomes. Detecting AF early is crucial for improving patient outcomes and reducing complications. However, AF is difficult to detect due to asymptomatic cases, false-positive test results, and patient non-adherence. Therefore, it is essential to develop effective methods for early AF detection that can prolong patient life. This study proposes a novel approach for AF detection using a wavelet transform-based filtered ECG signal and a neural network with a novel feature extractor. Firstly, the wavelet transform is applied to filter the ECG signal; secondly, the novel feature extractor is designed to extract important features from the filtered ECG signal. The extracted features are then used as input to a neural network for AF classification. The proposed method is evaluated on two publicly available ECG datasets, and the results show a prominent accuracy of 96% with a ROC is 0.95 and 86% with a ROC is 0.84 for the two datasets, respectively. In addition, to enhance the model’s performance, we have utilized 5-fold cross-validation which achieved an accuracy of 0.99 for Dataset 1 and 0.90 for Dataset 2, respectively. Compared to existing AF detection methods, our approach provides a promising solution that can improve the early diagnosis and treatment of AF. This study presents a new avenue for AF detection and has the potential to advance the field of cardiac arrhythmia detection.