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

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.

References

Cardiovascular diseases (cvds) - key facts. world health organisation (who), 2021. Available on: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) (Accessed on 15 june 2021).

Bunrit, S., Kerdprasop, N., and Kerdprasop, K. Multiresolution analysis based on wavelet transform for commodity prices time series forecasting. International Journal of Machine Learning and Computing 8, 2 (2018), 175–180.

Hannibal, G. B. Interpretation of the lowvoltage ecg. AACN Advanced Critical Care 25, 1 (2014), 64–68.

He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (2016), pp. 770–778.

Kandaswamy, A., Kumar, C. S., Ramanathan, R. P., Jayaraman, S., and Malmurugan, N. Neural classification of lung sounds using wavelet coefficients. Computers in biology and medicine 34, 6 (2004), 523–537.

Li, H., Yuan, D., Wang, Y., Cui, D., and Cao, L. Arrhythmia classification based on multidomain feature extraction for an ecg recognition system. Sensors 16, 10 (2016), 1744.

Liu, F., Liu, C., Zhao, L., Zhang, X., Wu, X., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., et al. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. Journal of Medical Imaging and Health Informatics 8, 7 (2018), 1368–1373.

Oh, S. L., Ng, E. Y., San Tan, R., and Acharya, U. R. Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats. Computers in biology and medicine 102 (2018), 278–287.

Roth, G. A., Johnson, C., Abajobir, A., Abd-Allah, F., Abera, S. F., Abyu, G., Ahmed, M., Aksut, B., Alam, T., Alam, K., et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. Journal of the American college of cardiology 70, 1 (2017), 1–25.

Xiong, Z., Nash, M. P., Cheng, E., Fedorov, V. V., Stiles, M. K., and Zhao, J. Ecg signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiological measurement 39, 9 (2018), 094006.

Published
2022-12-20
How to Cite
[1]
Soumiaa, M.-A., Elhabbari, S. and Mansouri, M. 2022. The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases. MENDEL. 28, 2 (Dec. 2022), 62-66. DOI:https://doi.org/10.13164/mendel.2022.2.062.
Section
Research articles