Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic...

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Science

Background:

• Electrocardiogram imaging (ECGI) reconstruction is crucial for non-invasive cardiac assessment.
• Understanding the computational techniques employed is vital for advancing ECGI accuracy.
• A systematic review of recent literature is needed to identify current trends and gaps.

Purpose of the Study:

• To analyze computational techniques used in ECGI reconstruction.
• To identify trends in dataset utilization, problem-solving approaches, and feature extraction.
• To highlight advancements and future research directions in ECGI.

Main Methods:

• Systematic literature review using PRISMA guidelines.
• Searches conducted on Scopus and Web of Science databases.
• Cochrane principles applied for risk of bias assessment.

Main Results:

• Analysis of 99 peer-reviewed English papers published between 2010 and 2023.
• Dominance of traditional methods (boundary element, Tikhonov).
• Emerging trend towards hybrid techniques and deep learning in ECGI.

Conclusions:

• Advancements in computational ECGI enhance cardiac diagnosis and treatment precision.
• Need for robust data utilization and innovative computational integration in ECGI.
• Significant progress in diagnostic and treatment methods towards tailored cardiac care.