聚类光电脉冲图脉冲波形状及其与临床数据的关联
Serena Zanelli1, Kornelia Eveilleau2, Peter H Charlton3
1Laboratoire Analyse, Géométrie et Applications, University Sorbonne Nord, Villetaneuse, France.
Frontiers in physiology
|November 13, 2023
概括
使用无监督的机器学习和深度学习方法来分类光聚光学 (PPG) 数字体积脉冲 (DVP) 波形. 这些方法克服了传统的DVP形状分类的局限性,提高了生物标志物的可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 摄影电磁镜 (PPG) 是一种记录数字体积脉冲 (DVP) 的非侵入性技术.
- 目前用于对DVP波形形状进行分类的方法,通常是基于二突位置,在现实数据标签方面面临挑战.
- 准确的DVP形状识别对于提高提取的生物标志物的精度和可靠性至关重要.
研究的目的:
- 开发和评估无监督机器学习和深度学习方法来对DVP波形形状进行分类.
- 为了克服手动数据标签在DVP分析中的局限性.
- 为了确定代表DVP形状可变性的集群的最佳数量.
主要方法:
- 使用K-medoids集群与手工制作的DVP功能,衍生动态时间曲折 (DDTW) 相似性矩阵和CNN自动编码器提取的功能.
- 通过强加四个 medoids (代表Dowber类) 和通过自动搜索四个集群来测试集群.
- 通过使用轮得分,预测强度和惯性来评估最佳的集群数量.
主要成果:
- 无监督的方法成功地集群了DVP波形,解决了手动标签的挑战.
- 与获得的集群相关的临床数据的分析为拟议的方法提供了验证.
- 该研究确定了DPV波形分类的最佳集群参数.
结论:
- 无监督机器学习和深度学习为DVP波形分类提供了强大的替代方案.
- 这些方法提高了从PPG信号中精确提取生物标志物的潜力.
- 这些发现有助于推进PPG信号分析及其临床应用.
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