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在生理信号数据中使用深度神经网络进行持续学习:一项调查

Ao Li^1,2, Huayu Li¹, Geng Yuan³

¹Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA.

Healthcare (Basel, Switzerland)

|January 23, 2024

概括

持续学习增强了对心电图和脑电图等生理信号的深度学习,解决了长期医疗监测的局限性. 本综述探讨了适应性智能医疗保健系统的技术,应用和挑战.

科学领域:

生物医学工程生物医学工程
医疗保健中的人工智能
信号处理信号处理

背景情况:

深度学习优于生理信号 (ECG,EEG),但与长期监测的动态性质作斗争.
训练过的传统模型缺乏适应不断变化的医疗保健数据的能力.
持续学习为动态生理信号分析提供了至关重要的适应能力.

研究的目的:

审查用于生理信号分析的持续学习技术.
探索持续学习在智能医疗保健中的应用和挑战.
弥合长期生理监测适应性AI的文献差距.

主要方法:

关于传统和持续学习方法的文献综述.
对ECG和EEG数据应用的持续学习技术的分析.
讨论对智能医疗保健系统的影响.

主要成果:

确定持续学习是适应性生理信号处理的关键解决方案.
概述了从静态到自适应深度学习模型的演变.
突出挑战包括基准,适应性,效率和以用户为中心的设计.

结论:

持续学习对于通过自适应生理信号分析推进智能医疗保健至关重要.
未来的系统需要关注基准,效率和用户需求.
需要进一步的研究,以建立医疗保健的强大持续学习框架.

关键词:

持续的学习,持续的学习.深度学习是一种深度学习.机器学习是机器学习.生理学数据生理学数据智能医疗保健是一个智能医疗保健.

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