A Convolutional Neural Network using raw ECG signals automatically learned representative features for paroxysmal atrial fibrillation, improving patient screening performance compared to an end-to-end CNN.
In this paper, a novel electrocardiogram (ECG) signal classification and patient screening method is developed. The focus is on identifying patients with paroxysmal atrial fibrillation (PAF) which is a life threatening cardiac arrhythmia. The proposed approach uses the raw ECG signal as the input and automatically learns the representative features for PAF to be used by a classification mechanism. The features are learned directly from the time domain ECG signals by using a Convolutional Neural Network (CNN) with one fully connected layer. The learned features can replace the hand-crafted features and our experimental results indicate the effectiveness of the learned features in patient screening. The experimental results indicate that combining the learned features with other classifiers will improve the performance of the patient screening system as compared to an End-to-End convolutional neural network classifier. The major characteristics of the proposed approach are to simplify the process of feature extraction for different cardiac arrhythmias and to remove the need for using a human expert to specify the appropriate features. The effectiveness of the proposed ECG classification method is demonstrated through performing extensive simulation studies.
Pourbabaee et al. (Fri,) conducted a other in Paroxysmal atrial fibrillation. Convolutional Neural Network (CNN) for ECG signal classification vs. End-to-End convolutional neural network classifier / hand-crafted features was evaluated on Patient screening and ECG signal classification performance. A Convolutional Neural Network using raw ECG signals automatically learned representative features for paroxysmal atrial fibrillation, improving patient screening performance compared to an end-to-end CNN.