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Open AccessArticle

Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume

1
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, Taiwan
2
Department of Biomedical Engineering, I-Shou University, Kaohsiung City 82445, Taiwan
3
Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan
4
Institute of Medicine, School of Medicine, Chung-Shan Medical University, Taichung 402, Taiwan
5
Department of Internal Medicine, Chung-Shan Medical University Hospital, Taichung 402, Taiwan
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4612; https://doi.org/10.3390/app10134612
Received: 14 May 2020 / Revised: 22 June 2020 / Accepted: 30 June 2020 / Published: 3 July 2020
As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment. Unfortunately, PPG signals are easily corrupted by motion artifacts and baseline drift during recording. Although several rule-based algorithms have been developed for evaluating the quality of PPG signals, few artificial intelligence-based algorithms have been presented. Thus, this study aims to classify the quality of PPG signals by using two two-dimensional deep convolution neural networks (DCNN) when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. An image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. To quantify the quality of individual PPG pulses, the error percentage of the beat-to-beat SV measured by our device and medis® CS 2000 synchronously is used to determine whether the pulse quality is high, middle, or low. Fourteen subjects were recruited, and a total of 3135 PPG pulses (1342 high quality, 73 middle quality, and 1720 low quality) were obtained. We used a traditional DCNN, VGG-19, and a residual DCNN, ResNet-50, to determine the quality levels of the PPG pulses. Their results were all better than the previous rule-based methods. The accuracies of VGG-19 and ResNet-50 were 0.895 and 0.925, respectively. Thus, the proposed DCNN may be applied for the classification of PPG quality and be helpful for improving the SV measurement in impedance cardiography. View Full-Text
Keywords: photoplethysmography (PPG); deep convolution neural network (DCNN); signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV) photoplethysmography (PPG); deep convolution neural network (DCNN); signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV)
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MDPI and ACS Style

Liu, S.-H.; Li, R.-X.; Wang, J.-J.; Chen, W.; Su, C.-H. Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume. Appl. Sci. 2020, 10, 4612. https://doi.org/10.3390/app10134612

AMA Style

Liu S-H, Li R-X, Wang J-J, Chen W, Su C-H. Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume. Applied Sciences. 2020; 10(13):4612. https://doi.org/10.3390/app10134612

Chicago/Turabian Style

Liu, Shing-Hong; Li, Ren-Xuan; Wang, Jia-Jung; Chen, Wenxi; Su, Chun-Hung. 2020. "Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume" Appl. Sci. 10, no. 13: 4612. https://doi.org/10.3390/app10134612

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