Machine learning models to help classification of cardiovascular disease

DOI

10.2991/978-94-6463-496-9_17How to use a DOI?

Keywords

Electrocardiography (ECG); Deep Learning; Classification Algorithms; PTB-XL Dataset; Convolutional Neural Networks (CNNs); Reinforcement Learning

Abstract

Electrocardiography (ECG) has become a widely used noninvasive diagnostic tool, increasingly supported by algorithmic analysis. However, progress in automated ECG interpretation faces challenges due to the lack of adequate training datasets and standardized evaluation procedures, which are crucial to ensure comparability of algorithms. In this study, ECG classification models are proposed using the recently published PTB-XL dataset of 12 clinical leads. This research aims to overcome existing limitations by thoroughly investigating the performance of different deep learning-based classification algorithms. Specifically, we investigate the effectiveness of convolutional neural networks (CNNs), deep neural networks (DNNs), long short-term memory (LSTM) and U-net architectures in accurately classifying ECG signals. In addition, we explore the potential of reinforcement learning techniques using classifiers pre-trained on PTB-XL to further improve classification accuracy and robustness. This comprehensive analysis not only underscores the significant potential of deep learning algorithms in ECG analysis, but also highlights the importance of standardized datasets such as PTB-XL in advancing the field. By establishing PTB-XL as a key resource, this study aims to foster collaboration among researchers and encourage further contributions aimed at refining and extending the dataset to better serve the ECG analysis community.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Khaoula Oueldji
AU  - Nadir Farah
PY  - 2024
DA  - 2024/08/31
TI  - Machine learning models to help classification of cardiovascular disease
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 215
EP  - 230
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_17
DO  - 10.2991/978-94-6463-496-9_17
ID  - Oueldji2024
ER  -