Continual learning offers a promising solution for processing dynamic physiological signal data in long-term healthcare monitoring compared to traditional deep-learning models.
Continual learning offers a promising solution for adapting deep neural networks to the dynamic nature of long-term physiological signal monitoring in healthcare.
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.
Li et al. (Tue,) conducted a review in Physiological signal data analysis. Continual learning with deep neural networks vs. Traditional deep-learning models was evaluated. Continual learning offers a promising solution for processing dynamic physiological signal data in long-term healthcare monitoring compared to traditional deep-learning models.
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