伪异常增强深度支向量数据描述用于心电图质量评估.
Xunhua Huang1, Fengbin Zhang1, Haoyi Fan2
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
Computers in biology and medicine
|January 16, 2024
概括
本研究引入了一种无监督的方法来评估心电图 (ECG) 信号质量,将其视为异常检测. 该方法有效地识别噪音,并改进可穿戴设备的ECG分析.
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:
- 可穿戴式心电图 (ECG) 记录面临噪声干扰,降低信号质量.
- 传统的监督信号质量评估 (SQA) 方法与各种噪音和未知的异常作斗争.
- 电脑心电图信号和噪声的高变异性挑战了传统的SQA方法概括.
研究的目的:
- 为ECG信号开发一种简单,有效,无监督的SQA方法.
- 提高异常的检测,并提高SQA方法的通用化.
- 为了提供可靠的ECG信号质量测量可穿戴设备.
主要方法:
- 使用伪异常增强的深度支持向量数据描述,将SQA建模为异常检测问题.
- 引入了新的心电图噪声生成方法来模拟现实世界的噪声场景.
- 通过使用生成的伪异常,以自我监督的方式学习了高质量的心电图数据的泛化超层.
主要成果:
- 提出的无监督方法有效地根据距离到已知的超球中心的距离来衡量ECG质量.
- 在多个公共数据集和现实世界12个导电心电图数据集中证明了有效性.
- 与传统方法相比,在检测信号异常方面取得了更高的性能.
结论:
- 无监督异常检测方法为可穿戴设备中的ECG SQA提供了强大的解决方案.
- 伪异常增强了深度支持向量数据描述,改善了模型概括.
- 这种方法提供了一种可靠和有效的方法来评估ECG信号质量,即使是未知的噪音类型.
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