利用下降比例斜率（FSS）鉴别子痫前期——一种新的光体积描记形态参数。
Distinguishing preeclampsia using the falling scaled slope (FSS) --- a novel photoplethysmographic morphological parameter.
机构信息
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
出版信息
Hypertens Pregnancy. 2023 Dec;42(1):2225617. doi: 10.1080/10641955.2023.2225617.
BACKGROUND
Preeclampsia (PE) presence could lead to hemodynamic changes. Previous research suggested that morphological parameters based on photoplethysmographic pulse waves (PPGW) could help diagnose PE.
AIM
To investigate the performance of a novel PPGPW-based parameter, falling scaled slope (FSS), in distinguishing PE. To investigate the advantages of the machine learning algorithm over the conventional statistical methods in the analysis.
METHODS
Eighty-one pieces of PPGPW data were acquired for the study (PE, = 44; normotensive, = 37). The FSS values were calculated and used to construct a PE classifier using the K-nearest neighbors (KNN) algorithm. A predicted PE state varying from 0 to 1 was also calculated. The classifier's performance in distinguishing PE was evaluated using the ROC and AUC. A comparison was conducted with previously published PPGPW-based models.
RESULT
Compared to the previous PPGPW-based parameters, FSS showed a better performance in distinguishing PE with an AUC value of 0.924, the best threshold of 0.498 could predict PE with a sensitivity of 84.1% and a specificity of 89.2%. As for the analysis method, training a classifier using the KNN algorithm had an advantage over the conventional statistical methods with the AUC values of 0.878 and 0.749, respectively.
CONCLUSION
The result indicated that FSS might be an effective tool for identifying PE. Moreover, the machine learning algorithm could further help the data analysis and improve performance. [Figure: see text].
背景
子痫前期（PE）的存在可能导致血液动力学变化。先前的研究表明，基于光电容积脉搏波（PPGW）的形态参数有助于诊断 PE。
目的
研究一种新的基于 PPGPW 的参数——下降比例斜率（FSS）在区分 PE 方面的性能。研究机器学习算法在分析中的优势优于传统统计方法。
方法
本研究共采集了 81 组 PPGPW 数据（PE，n=44；正常血压，n=37）。计算 FSS 值，并使用 K-最近邻（KNN）算法构建 PE 分类器。还计算了一个从 0 到 1 变化的预测 PE 状态。使用 ROC 和 AUC 评估分类器区分 PE 的性能。并与之前发表的基于 PPGPW 的模型进行了比较。
结果
与之前的基于 PPGPW 的参数相比，FSS 在区分 PE 方面表现出更好的性能，AUC 值为 0.924，最佳阈值为 0.498，预测 PE 的敏感度为 84.1%，特异性为 89.2%。就分析方法而言，使用 KNN 算法训练分类器优于传统统计方法，AUC 值分别为 0.878 和 0.749。
结论
结果表明，FSS 可能是识别 PE 的有效工具。此外，机器学习算法可以进一步帮助数据分析并提高性能。[图：见正文]。
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