Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey.
Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey.
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DOI：
10.3390/healthcare12020155
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发表时间：
2024-01-09
期刊：
影响因子：
2.8
通讯作者：
Yuan, Geng
中科院分区：
文献类型：
--
作者：
Li, Ao;Li, Huayu;Yuan, Geng
深度学习算法在处理包括心电(ECGs)和脑电(EEGs)在内的生理信号数据方面有着广阔的前景。然而，医疗保健往往需要长期监测，这对传统的深度学习模式构成了挑战。这些模型通常只培训一次，然后部署，这限制了它们适应动态和不断变化的医疗场景的能力。持续学习--以其随时间推移的自适应学习能力而闻名--为这些挑战提供了一个有希望的解决方案。然而，仍然缺乏统一的文献，这些文献回顾了针对生理信号分析的持续学习的技术、应用和挑战，以及它的未来方向。为了弥合这一差距，我们的审查试图提供流行技术及其对智能医疗的影响的概述。我们描绘了从传统方法到持续学习范式的演变。我们的目标是提供对面临的挑战的见解，并勾勒出潜在的前进道路。我们的讨论强调了在未来医疗保健系统的开发中需要基准、适应性、计算效率和以用户为中心的设计。
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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