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Photoplethysmography signal analysis to assess obesity, age group and hypertension

机译:光体积描记器信号分析可评估肥胖,年龄组和高血压

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Photoplethysmography (PPG) provides a simple, convenient and noninvasive method to assess pulse oximetry. Several attempts have been made to use PPG also to estimate blood pressure and arterial stiffness. This paper attempts to assess obesity classes, age group, and hypertension classes using PPG measured from the finger. One set of features was derived from the normalized pulse width of PPG and the other from original PPG. The features were calculated based on the pulse decomposition analysis using five lognormal functions and the up-slope of the PPG pulse. Using kNN and SVM as classifiers, the results were validated using leave-one-out validation. Performances of both features sets have no significant difference, and the kNN outperformed the SVM. The best accuracies are 93%, 88%, and 92% for obesity (5 classes), age group (7 classes), and hypertension (4 classes) respectively. These three assessment targets have a strong relationship with arterial stiffness, therefore it also leads to a study about arterial stiffness using PPG. Width normalization to 1 second might affect some features points based on pulse decomposition analysis. This study also found that the up-slope analysis might give good indices when width normalization was employed. However, these findings still require more experiments to gain conclusions that are more comprehensive.
机译:光体积描记法(PPG)提供了一种简单,方便且无创的方法来评估脉搏血氧饱和度。为了使用PPG来估计血压和动脉僵硬度,已经进行了几次尝试。本文尝试使用手指测量的PPG评估肥胖症类别,年龄组和高血压类别。一组特征来自PPG的标准化脉冲宽度,另一组特征来自原始PPG。这些特征是根据使用五个对数正态函数的脉冲分解分析和PPG脉冲的上升斜率计算得出的。使用kNN和SVM作为分类器,使用留一法验证对结果进行验证。两种功能集的性能都没有显着差异,并且kNN的性能优于SVM。肥胖症(5个等级),年龄组(7个等级)和高血压(4个等级)的最佳准确率分别为93%,88%和92%。这三个评估目标与动脉僵硬度有很强的关系,因此,这也导致了使用PPG进行动脉僵硬度的研究。基于脉冲分解分析,宽度归一化到1秒可能会影响某些特征点。这项研究还发现,当采用宽度归一化时,上坡分析可能会给出良好的指标。但是,这些发现仍然需要更多的实验才能得出更全面的结论。

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