Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry
Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry
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使用形态特征测量脉动生理信号的信号质量：在脉搏血氧饱和度可靠性测量中的应用
DOI：
10.1016/j.imu.2019.100222
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发表时间：
2019
影响因子：
--
通讯作者：
Najarian, Kayvan
中科院分区：
文献类型：
--
作者：
Sabeti, Elyas;Reamaroon, Narathip;Mathis, Michael;Gryak, Jonathan;Sjoding, Michael;Najarian, Kayvan
脉搏血氧仪是一种无创、低成本的生理监测仪，可测量血氧水平。虽然脉搏血氧测定法的非侵入性性质是有利的，但是由这些设备生成的氧饱和度的估计易于产生运动伪影和环境噪声，从而降低了这种估计的可靠性。临床医生通过视觉检查光电体积描记器（PPG）来评估氧饱和度估计的质量来对抗这种情况，光电体积描记器（PPG）代表脉动血容量的变化并且也由脉搏血氧计生成。在本文中，我们提出了六个形态特征，可用于确定PPG信号的质量，并生成信号质量指数。与许多类似的研究不同，这种方法使用机器学习，不需要单独的信号（如ECG）作为参考。针对患有心血管和呼吸疾病的患者的46个30分钟PPG片段测试了多种算法，包括房颤、缺氧、急性心力衰竭、肺炎、ARDS和肺栓塞。由两名临床医生独立注释这些信号的信号质量，并将其注释的联合用作地面实况。类似于在临床环境中记录的任何生理信号，所利用的数据集也是不平衡的，有利于高质量的片段。实验表明，成本敏感的支持向量机（SVM）优于其他测试方法，是强大的不平衡的数据的性质。虽然所提出的算法在PPG信号上进行了测试，但该方法对所使用的数据集仍然是不可知的，并且可以应用于任何类型的脉动生理信号。
Pulse oximetry is a noninvasive and low-cost physiological monitor that measures blood oxygen levels. While the noninvasive nature of pulse oximetry is advantageous, the estimates of oxygen saturation generated by these devices are prone to motion artifacts and ambient noise, reducing the reliability of such estimations. Clinicians combat this by assessing the quality of oxygen saturation estimation by visual inspection of the photoplethysmograph (PPG), which represents changes in pulsatile blood volume and is also generated by the pulse oximeter. In this paper, we propose six morphological features that can be used to determine the quality of the PPG signal and generate a signal quality index. Unlike many similar studies, this approach uses machine learning and does not require a separate signal, such as ECG, for reference. Multiple algorithms were tested against 46 30-min PPG segments of patients with cardiovascular and respiratory conditions, including atrial fibrillation, hypoxia, acute heart failure, pneumonia, ARDS, and pulmonary embolism. These signals were independently annotated for signal quality by two clinicians, with the union of their annotations used as the ground-truth. Similar to any physiological signal recorded in a clinical setting, the utilized dataset is also unbalanced in favor of good quality segments. The experiments showed that a cost-sensitive Support Vector Machine (SVM) outperformed other tested methods and was robust to the unbalanced nature of the data. Though the proposed algorithm was tested on PPG signals, the methodology remains agnostic to the dataset used, and may be applied to any type of pulsatile physiological signal.
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DOI：
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期刊：
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DOI：
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期刊：
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