Abstract
Objective: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. Methods: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. Results: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. Conclusion: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. Significance: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.
Highlights
Photoplethysmography (PPG) is a convenient, low-cost technology that has been applied to various aspects of cardiovascular monitoring, such as blood oxygen saturation, heart rate, respiration, blood pressure, cardiac output, microvascular blood flow, endothelial function, arterial aging, and autonomic function [1]
Prior studies on PPG-based SpO2 monitoring usually relied on inconvenient methods, such as wired solutions, or wireless methods [19,20,21,22], that did not explore key design principles, such as different finger types, fingers, and hands [23,24,25]
With respect to the model type, both model 2 and model 3 correspond to the lowest RMSE, but considering model 2 is of lower complexity and it is easier to implement in the smartphone, we choose model 2 as the final SpO2 model
Summary
Photoplethysmography (PPG) is a convenient, low-cost technology that has been applied to various aspects of cardiovascular monitoring, such as blood oxygen saturation, heart rate, respiration, blood pressure, cardiac output, microvascular blood flow, endothelial function, arterial aging, and autonomic function [1]. Prior studies on PPG-based SpO2 monitoring usually relied on inconvenient methods, such as wired solutions, or wireless methods [19,20,21,22], that did not explore key design principles, such as different finger types, fingers, and hands [23,24,25]. We have developed a highly convenient, continuous SpO2 monitor, and explored all the above mentioned key design principles. (v) the probe of different SpO2 estimation models: From linear and non-linear regressions models to the machine learning model, (vi) the comparison of left and right hands, and the comparison of different fingers, for SpO2 monitoring
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