Measuring Oxygen Saturation With Smartphone Cameras Using Convolutional Neural Networks
Measuring Oxygen Saturation With Smartphone Cameras Using Convolutional Neural Networks
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DOI：
10.1109/jbhi.2018.2887209
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发表时间：
2019-11-01
影响因子：
7.7
通讯作者：
Larson, Eric C.
中科院分区：
文献类型：
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作者：
Ding, Xinyi;Nassehi, Damoun;Larson, Eric C.
动脉血氧饱和度（SaO(2)）是血液中血红蛋白携带多少氧气的指标。有足够的氧气对人体细胞的功能至关重要。SaO(2)的测量通常是用脉搏血氧仪来估计的，但最近的研究工作已经研究了如何使用智能手机相机来推断SaO(2)。在本文中，我们提出了使用卷积神经网络和预处理步骤在智能手机上测量SaO(2)的方法，以更好地防止运动伪影。为了评估这种方法，我们进行了一项涉及39名参与者的屏气研究。我们用两种不同的手机比较了结果。我们将我们的模型与广泛应用于脉搏血氧仪的比率模型进行了比较，结果表明，我们的系统的平均绝对误差（2.02%）明显低于医用脉搏血氧仪。
Arterial oxygen saturation (SaO(2)) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO(2) is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO(2). In this paper, we propose methods for the measurement of SaO(2) with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) than a medical pulse oximeter.