Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability...

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Electrocardiogram (ECG) interpretation traditionally relies on expert cardiologists.
• Deep learning (DL) has advanced medical data analysis, including ECG diagnostics.
• Inter-patient variability in ECG data limits the generalizability of population-trained AI models.

Purpose of the Study:

• To systematically review recent deep learning approaches for personalized ECG diagnosis.
• To examine models specifically designed to address patient-specific variability in ECG interpretation.
• To identify limitations and future directions in personalized ECG AI.

Main Methods:

• Systematic review methodology.
• Searched four major databases (PubMed, IEEE Xplore, Web of Science, Google Scholar) for studies from 2020-2024.
• Rigorous two-step screening and analysis of 112 selected studies.

Main Results:

• Overview of advanced DL techniques for personalized ECG analysis, including transfer learning, GANs, meta-learning, and domain adaptation.
• Analysis of methods addressing patient-specific variability in ECG interpretation.
• Identification of challenges such as balancing generalization with specificity and data privacy.

Conclusions:

• Deep learning holds transformative potential for personalized ECG diagnostics in clinical practice.
• Advanced DL techniques offer a pathway toward more accurate, efficient, and patient-centered cardiac diagnostics.
• This review lays the foundation for future innovations in personalized cardiac care.