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.
|Title of host publication||2020 Computing in Cardiology, CinC 2020|
|Place of Publication||Rimini, Italy|
|Publication status||Published - 10 Feb 2021|
|Event||Computing in Cardiology 2020 - Palacongressi, Rimini, Italy|
Duration: 13 Sep 2020 → 16 Sep 2020
|Name||Computing in Cardiology|
|Conference||Computing in Cardiology 2020|
|Period||13/09/20 → 16/09/20|
Bibliographical noteFunding Information:
This work was supported by the Eastern Corridor Medical Engineering Centre that is funded by the European Union’s INTERREG VA Programme and managed by the Special EU Programmes Body (SEUPB)
© 2020 Creative Commons; the authors hold their copyright.
Copyright 2021 Elsevier B.V., All rights reserved.
- deep learning
- Premature ventricular contraction
- machine learning
- ECG analysis