ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

Pedro A. Moreno-Sánchez, Guadalupe García-Isla, Valentina D.A. Corino, Antti Vehkaoja, Kirsten Brukamp, Mark van Gils, Luca Mainardi

Research output: Contribution to journalReview Articlepeer-review

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Abstract

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians’ ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

Original languageEnglish
Article number108235
Number of pages20
JournalComputers in Biology and Medicine
Volume172
DOIs
Publication statusPublished - Apr 2024
Publication typeA2 Review article in a scientific journal

Keywords

  • Bias
  • Cardiovascular diseases (CVD)
  • Deep learning
  • ECG
  • Ethical, Legal and societal implications (ELSI)
  • Explainable Artificial Intelligence (XAI)
  • Machine learning
  • Trustworthy Artificial Intelligence

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications

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