1Associate Professor, Department of Electronics, Baselios Poulose II Catholicos College, Piravom, Ernakulam Dist, Kerala, India
Background: Artificial intelligence (AI) has entered a transformative era defined by large-scale foundation models trained through self-supervised learning (SSL). Unlike conventional supervised approaches that depend heavily on annotated datasets, SSL leverages intrinsic data structure to learn robust and transferable representations from vast quantities of unlabelled data. In electrocardiography (ECG), this paradigm represents a significant breakthrough, addressing longstanding challenges related to annotation cost, dataset bias, limited cross-population generalization, and scalability across devices and clinical environments. Cardiovascular diseases remain the leading cause of global mortality, underscoring the urgent need for reliable, scalable, and interpretable automated ECG analysis systems.
Architectural Foundations: Foundational advances in transformer architectures (Vaswani et al., 2017), contextual pretraining (Devlin et al., 2018), contrastive representation learning (Chen et al., 2020; He et al., 2020), masked modelling (He et al., 2021), and probabilistic generative modelling (Kingma & Welling, 2014) have collectively established the theoretical and architectural basis for foundation-level ECG intelligence. These innovations enable models to capture both local waveform morphology and long-range rhythm dependencies, while supporting transfer learning across diverse downstream tasks such as arrhythmia detection, risk stratification, and anomaly identification.
Review and Future Directions: This review synthesizes the evolution of SSL paradigms applied to ECG analysis up to 2022, critically examining architectural design choices, benchmarking methodologies, domain adaptation techniques, interpretability mechanisms, privacy-preserving training strategies, and requirements for clinical validation. Furthermore, I propose a forward-looking roadmap toward multimodal, federated, and continually learning biomedical foundation models capable of robust cross-institutional deployment and trustworthy clinical integration. The convergence of SSL and ECG analytics signals a paradigm shift from narrow task-specific classifiers toward universal, adaptive cardiovascular representation learning systems that can underpin next-generation precision cardiology.
How to cite this article: Paul B. Foundation Models and Self-Supervised Learning in ECG Signal Processing: Towards Scalable, Multimodal, and Trustworthy Cardiovascular Artificial Intelligence. Int J Drug Deliv Technol. 2026;16(2): 669-676. DOI: 10.25258/ijddt.16.2.71
Source of support: Nil.
Conflict of interest: None