对抗性时空对抗性学习用于心电图信号
概括
本研究引入了一个对抗的时空对比学习 (ASTCL) 框架,从未标记的心电图 (ECG) 信号中提取强大的,不变的表示,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 使用深度神经网络 (DNN) 从未标记的心电图 (ECG) 信号中提取不变表示是一个重大挑战.
- 虽然对比式学习为无监督学习提供了一个有希望的方法,但它需要提高对噪声的强度和学习类似于心脏病学家的时空和语义表示的能力.
研究的目的:
- 为无监督的心电图表现学习提出一种新的患者级对抗性时空对比学习 (ASTCL) 框架.
- 提高DNN对噪声的稳定性,并使全面的时空和语义ECG表示能够被学习.
主要方法:
- ASTCL框架包含了ECG增强 (噪声增强和消声) 以加强噪声的稳定性.
- 一个对抗模块,采用区分器和编码器之间的游戏,通过抛弃干扰来学习不变表示.
- 一个时空对比模块结合了时空预测和患者歧视,利用患者层面的正对来学习类别表示,避免模型崩.
主要成果:
- 拟议的ASTCL框架证明了在ECG信号中对噪声的强化稳定性.
- 该方法有效地从未标记的心电图数据中学习了时空和语义表示.
- 在多个ECG数据集上的实验表明,ASTCL的性能优于当前最先进的方法.
结论:
- ASTCL框架为不变心电图表示的无监督学习提供了有效的解决方案.
- 拟议的方法提高了DNN在分析复杂的心电图数据方面的能力,为改进的诊断工具提供了潜力.
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