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

doi:10.3390/app10134612

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24640

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24640

Title:	Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume
Authors:	Liu, SH;Li, RX;Wang, JJ;Chen, WX;Su, CH
Keywords:	photoplethysmography (PPG);deep convolution neural network (DCNN);signal quality index (SQI);impedance cardiography (ICG);stroke volume (SV)
Date:	2020
Issue Date:	2022-08-09T08:05:37Z (UTC)
Publisher:	MDPI
Abstract:	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(R)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.
URI:	http://dx.doi.org/10.3390/app10134612 https://www.webofscience.com/wos/woscc/full-record/WOS:000555520000001 https://ir.csmu.edu.tw:8080/handle/310902500/24640
Relation:	APPLIED SCIENCES-BASEL ,2020 ,v10 ,issue 13
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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