Key result
Continual learning enables dynamic physiological signal analysis without catastrophic forgetting, though clinical hurdles remain.
Why the study?
Traditional deep-learning models struggle to adapt to evolving long-term healthcare monitoring, and consolidated literature reviewing continual learning techniques, applications, and challenges specific to physiological signal analysis was lacking.
Transitioning to continual learning paradigms is necessary for developing adaptive, long-term deep learning models capable of handling evolving physiological signal data in smart healthcare systems.
Promising for adaptive physiological signal analysis but unproven clinically; leaves open standardized benchmarks and real-world validation before adoption.
Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.
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Li et al. (2024) conducted a review in Physiological signal analysis. Continual learning algorithms vs. Traditional deep learning models was evaluated. Continual learning algorithms offer adaptive capabilities for analyzing dynamic physiological signal data over time without catastrophic forgetting, though challenges remain in real-world application.
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