The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases

Mhamed-Amine Soumiaa; Sara Elhabbari; Mohamed Mansouri

doi:10.13164/mendel.2022.2.062

Mhamed-Amine Soumiaa ENSA Berrechid https://orcid.org/0000-0003-2152-2185
Sara Elhabbari ENSA Berrechid
Mohamed Mansouri ENSA Berrechid

DOI: https://doi.org/10.13164/mendel.2022.2.062

Keywords: Electrocardiogram, ECG, Cardiology, Deep learning, Artificial Neural Networks, Classification, Diagnosis, Automation, Discrete Wavelet Transform, Signal Processing

Abstract

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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