Evaluating Quality of Photoplethymographic Signal on Wearable Forehead Pulse Oximeter With Supervised Classification Approaches

• Published in: IEEE access Vol. 8; pp. 185121 - 185135
• Main Authors: Liu, Shing-Hong, Liu, Han-Chi, Chen, Wenxi, Tan, Tan-Hsu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; CNN; Forehead; Motion artifacts; Oximetry; Oxygen content; oxygen saturation ratio; photoplethysmography; Probes; Pulse measurements; Pulse oximeter; Pulse oximetry; Segments; Sensors; Signal quality; Support vector machines; SVM; Treadmills; Wearable technology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3029842

Pulse oximeter is a common and important instrument in medical clinic, which uses the photoplethysmography to measure oxygen saturation ratio (SpO 2 ). However, the photoplethysmographic (PPG) signal is easily corrupted by the motion artifact when SpO 2 is measured in a dynamic scenario. Moreover, the probe of most pulse oximeters available in the market is finger-type clip, which is only suitable in the static scenario. This study aims to develop a wearable forehead pulse oximeter which could be used in a dynamic scenario, and uses two classification approaches, support vector machine (SVM) and convolutional neural network (CNN), for evaluating the qualities of its PPG signals. The higher the quality of PPG signals, the higher the accuracy of SpO 2 values. The SVM classified the SQI of PPG signal by twelve statistic features calculated from 7-second PPG segment. In the CNN approach, the PPG signals converted to an image was used as the input. The VGG-19 model, was then used to evaluate the SQI of PPG segments. Twenty subjects were recruited to perform the static and dynamic experiments. For the dynamic experiment, subjects ran on a treadmill with three different speeds, 3 km/hour, 5 km/hour and 7 km/hour. Experimental results indicated that the accuracies of SVM and CNN for SQI classifications were 89.9 ± 1.18 % (mean ± deviation %) and 88.7 ± 1.54%, respectively. Then, the dynamic data were used to test the two classification models which were trained by the static data. The accuracies of SVM and CNN for SQI classifications were 89.5 ± 3.87% and 86.2 ± 4.28%, respectively. The error ratios of SpO 2 in the case of static condition before and after the SQI classification with the SVM were respectively 5.6 ± 6.6% and 1.9 ± 1.1%. The results suggested that the performance of SVM was better than CNN.