ABSTRACT

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, n = 44; normotensive, n = 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.

中文翻译：

---

使用下降比例斜率（FSS）区分先兆子痫——一种新颖的光电体积描记形态学参数

摘要

背景

先兆子痫（PE）的存在可能导致血流动力学变化。先前的研究表明，基于光电容积脉搏波（PPGW）的形态参数可以帮助诊断PE。

目的

研究基于 PPGPW 的新型参数下降比例斜率 (FSS) 在区分 PE 方面的性能。研究机器学习算法在分析中相对于传统统计方法的优势。

方法

本研究获取了 81 条 PPGPW 数据（PE，n  = 44；血压正常，n  = 37）。使用 K 最近邻 (KNN) 算法计算 FSS 值并用于构建 PE 分类器。还计算了从 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。与之前发布的基于 PPGPW 的模型进行了比较。

结果

与之前基于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0.498可以预测PE，敏感性为84.1%，特异性为89.2%。在分析方法上，使用KNN算法训练分类器比传统的统计方法有优势，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。