A deep learning approach using a convolutional neural network and 10 extracted features improved the diagnosis performance for premature ventricular contraction recognition.
Does a deep learning approach using CNN improve premature ventricular contraction recognition in ECG signals?
A deep learning approach using CNN and specific morphological/statistical features can effectively recognize premature ventricular contractions from ECG signals.
Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.
Sarshar et al. (Sat,) conducted a other in Premature ventricular contraction (PVC). Convolutional neural network (CNN) using 10 heartbeat and statistical features was evaluated on PVC recognition and classification performance. A deep learning approach using a convolutional neural network and 10 extracted features improved the diagnosis performance for premature ventricular contraction recognition.
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