Key result
A ResNet34 deep learning model using Multi-level Discrete Wavelet Transform achieved an AUC of 0.98 and an accuracy of 97.1% for classifying 8 types of cardiovascular diseases from 12-lead ECGs.
Why the study?
Reading and analyzing ECGs can be time consuming, and automated decision support tools can facilitate and accelerate ECG diagnosis.
A deep learning model using ResNet34 and Multi-level Discrete Wavelet Transform can accurately classify 12-lead ECG signals into 8 cardiovascular diseases and normal rhythm.
Proposed ECG classifier needs prospective validation before use; leaves open real-world diagnostic impact.
Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.
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Soumiaa et al. (2022) studied Cardiovascular diseases (arrhythmias and ST abnormalities) (n=6,877). ResNet34 Deep Learning algorithm with Multi-level Discrete Wavelet Transform was evaluated on Classification accuracy and AUC for 8 cardiovascular diseases. A ResNet34 deep learning model using Multi-level Discrete Wavelet Transform achieved an AUC of 0.98 and an accuracy of 97.1% for classifying 8 types of cardiovascular diseases from 12-lead ECGs.
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