Do deep neural networks accurately classify premature ventricular contractions from ECGs?
Deep learning models, particularly ResNet-50 and Inception V3, demonstrate high accuracy and reliability in automating the detection of premature ventricular contractions from ECGs.
Electrocardiogram (ECG) analysis has been used to identify different heart problems and deep learning is emerging as a common tool to analyse ECGs. Premature ventricular contraction (PVC) is the most common cause of abnormal heartbeats; in most cases this is harmless but under specific conditions, it can lead to a life-threatening cardiac disease. Automated PVC detection in this scenario is a task of significant importance for relieving the heavy workloads of experts in the manual analysis of long-term ECGs. To identify PVCs, this research aims to use the MIT-BIH Arrhythmia Database to classify QRS complexes using five different deep neural networks: Long Short Term Memory, AlexNet, GoogleNet, Inception V3 and ResNet-50. The results showed high efficiency and reliability in the final diagnoses during two separate experiments (one with the entire dataset and the other with a balanced dataset). The ResNet-50 was the first experiment's best classifier (accuracy = 99.8%, F1-score = 99.2%), and the second experiment's best classifier was Inception V3 (accuracy = 98.8%, F1-score=98.8%). Relevant information, in this research, was extrapolated from a study of the confusion matrix to conduct a "failure analysis" to understand where and why the classifiers made incorrect classifications.
Marco et al. (Wed,) studied this question.