运行RunDAE模型:运行无噪声自动编码器模型,用于无噪声ECG信号
1FB Life Science Engineering (LSE), Institut für Biomedizinische Technik (IBMT), Technische Hochschule Mittelhessen (THM), Gießen, Germany; Department of biomedical engineering, University of Duhok, Duhok, Kurdistan Region-Iraq.
Computers in biology and medicine
|October 8, 2023
概括
一个运行无声自编码器 (RunDAE) 的新型系统有效地无声化了心电图 (ECG) 信号,使用没有R峰对齐的短段. 这种浅层学习模型的性能优于经典的DAE,提供高效的ECG信号无声化,使用最小的层次.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 无声化自编码器 (DAE) 用于生物信号无声化,如心电图 (ECG) 信号,通过尺寸缩小.
- 传统的DAE模型需要对相关输入段进行培训 (例如QRS对齐或长心电图段).
- 使用长长的心电图段导致复杂的,深层的DAE模型具有许多隐藏的层,这是一个显著的缺点.
研究的目的:
- 提出一个新的DAE模型,运行DAE (RunDAE),用于拒绝短ECG段.
- 开发一种不依赖于R峰检测用于ECG细分对齐的方法.
- 评估RunDAE与经典DAE在ECG信号消噪方面的性能.
主要方法:
- 拟议的RunDAE模型对ECG数据进行样本对样本的处理,利用连续的重叠段中的相关性.
- 评估了经典的DAE和RunDAE模型 (具有卷积和密集层) 在被物理和模拟噪声损坏的ECG段上.
- 在QRS对齐和非对齐的心电图段上进行测试,包括运动工件,电极运动,基线漫步和高斯白噪声.
主要成果:
- 与QRS对齐的细分市场相比,与非对齐的细分市场相比,QRS对齐的细分市场产生了更好的无效化结果.
- 运行DAE模型在拒绝ECG信号方面表现优于传统的DAE,特别是在密集的层和对齐的段落中.
- 训练 RunDAE 模型的正常和非节律心电图信号都提高了他们的能力.
- RunDAE作为一个多阶段的,非因果的,非线性适应性过器.
结论:
- 一个浅层学习模型,RunDAE,只使用邻近的样本相关性,实现了优异的无效化性能.
- RunDAE为ECG信号消噪提供了一个高效的替代方案,特别是在短,未对齐的段落中.
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