A Supervised Approach to Robust Photoplethysmography Quality Assessment.
A Supervised Approach to Robust Photoplethysmography Quality Assessment.
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DOI：
10.1109/jbhi.2019.2909065
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发表时间：
2020-03
影响因子：
7.7
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中科院分区：
文献类型：
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及早发现心房颤动(AFib)是预防中风复发的关键。监测心率的新工具对风险分层和中风预防很重要。由于许多长期AFib检测的新方法现在都是基于来自可穿戴设备的光体积图(PPG)记录，确保高PPG信噪比是稳健检测AFib发作的基本要求。传统上，信号质量评估通常基于脉冲之间的相似性评估来导出信号质量指数。在存在心律失常的情况下，使用这种方法准确评估PPG质量是有局限性的，主要是由于脉搏形态的实质性变化。在这篇文章中，我们首先测试了从一系列关于PPG质量评估的研究中选择的算法的性能，使用了来自AFib患者的PPG记录的数据集。然后，我们提出了机器学习方法来评估来自加州大学旧金山分校神经重症监护室的13名中风患者的30-S段的PPG质量，以及来自加州大学旧金山分校5个普通重症监护室之一的3764名患者的另一个数据集。我们使用了从两个系统获得的数据，来自床边监测系统的指尖PPG(FpPG)和使用可穿戴商业腕带测量的放射状PPG(RPPG)。我们比较了各种有监督的机器学习技术，包括k近邻、决策树和两类支持向量机。支持向量机的性能最好。在建立模型时使用了现场可编程PG信号，当使用来自测试集的专用现场可编程PG的数据进行测试时，达到了0.9477的准确率，当在rPPG数据上进行测试时，达到了0.9589的精度。
Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.