Binary Classification of Cardiac Pathologies Using Deep Learning: A PTB-XL Dataset Approach

Mohamed Khalil Chaabani (), Nathan Guerreiro (), Luiz Ribeiro (), Luiz E. Luiz (), Mohamed Aymen Slim () and João Paulo Teixeira ()
Additional contact information
Mohamed Khalil Chaabani: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
Nathan Guerreiro: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
Luiz Ribeiro: Federal Technological University of Paraná
Luiz E. Luiz: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)
Mohamed Aymen Slim: Private Higher Polytechnic Institute, Private University of Tunis
João Paulo Teixeira: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)

A chapter in Health Technologies and Demographic Challenges, 2025, pp 83-92 from Springer

Abstract: Abstract Cardiovascular diseases, such as myocardial infarction, are among the leading causes of death worldwide. Accuracy and time are crucial for diagnosing these conditions and for effective treatment, usually requiring time-consuming manual analysis of clinical-grade electrocardiogram (ECG). This paper presents a novel deep learning-based method for binary classification of cardiac pathologies using the PTB-XL dataset. The model integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies between leads. Preprocessing involves converting RGBA ECG images to RGB format and normalizing them to meet the requirements of the inputs of the layers. Initial experiments have shown promising results, achieving an AUC (Area Under the Curve) of 86.06%. Further work includes tests to optimize the model’s performance based on different key metrics, including accuracy and precision.

Keywords: ECG classification; Linformer; Optuna; EfficientNet; Adam optimizer (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-94901-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031949012

DOI: 10.1007/978-3-031-94901-2_7

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().