介绍心电图信号质量评估和织电极的估计精度
Neusa R Adão Martins1,2, Frederik Bauer1, Florent Baty3
1Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, 9014, St. Gallen, Switzerland.
Scientific reports
|November 21, 2025
概括
这项研究引入了用于可穿戴心电图 (ECG) 数据的新信号质量评估 (SQA) 算法. 该算法有效地识别了适合精确RR间隔提取的ECG段,提高了数据可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 可穿戴技术可穿戴技术
背景情况:
- 可穿戴式心电图 (ECG) 数据越来越多地用于建模.
- 现有的信号质量评估 (SQA) 算法通常依赖于噪声水平,这可能并不总是与数据可用性相关联.
- 需要用于可穿戴心电图系统的先进的SQA方法.
研究的目的:
- 为基于织品的可穿戴心电图系统提出一个新的SQA算法.
- 根据提取的RR间隔的准确性来分类ECG信号段.
- 将范式从基于噪声的评估转变为特定应用程序的数据准确性.
主要方法:
- 开发了一个SQA算法,利用模板匹配和生理可行性检查.
- 根据估计的RR区间精度,将心电图信号段分为四个质量级别.
- 评估了算法对来自织品可穿戴系统的心电图信号的有效性.
主要成果:
- 拟议的SQA算法成功识别了ECG信号段,产生了准确的RR间隔.
- 数据在质量级别的分布对影响信号质量的已知因素敏感.
- 证明了算法的有效性在评估可穿戴式心电图数据的可靠性.
结论:
- 开发的SQA算法提供了一种可靠的方法来评估可穿戴式心电图信号的质量,用于RR间隔提取.
- 与传统的基于噪音的方法相比,这种方法提供了更以应用为中心的ECG信号质量视图.
- 这些发现支持使用这种SQA算法来提高可穿戴式心电图数据在各种建模应用中的实用性.
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