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Open AccessArticle

Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement

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
Division of Cardiology, Chiayi Chang Gung Memorial Hospital, Chiayi City 61363, Taiwan
5
Department of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan City 33305, Taiwan
6
Institute of Medicine, School of Medicine, Chung-Shan Medical University, Taichung 402, Taiwan
7
Department of Internal Medicine, Chung-Shan Medical University Hospital, Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1476; https://doi.org/10.3390/app10041476
Received: 23 January 2020 / Revised: 14 February 2020 / Accepted: 19 February 2020 / Published: 21 February 2020
Photoplethysmography (PPG) has been extensively employed to acquire some physiological parameters such as heart rate, oxygen saturation, and blood pressure. However, PPG signals are frequently corrupted by motion artifacts and baseline wandering, especially for the reflective PPG sensor. Several different algorithms have been studied for determining the signal quality of PPG by the characteristic parameters of its waveform and the rule-based methods. The levels of signal quality usually were defined by the manual operations. Thus, whether the good PPG waveforms are enough to increase the accuracy of the measurement is still a subjective issue. The aim of this study is to use a fuzzy neural network to determine the signal quality indexes (SQI) of PPG pulses measured by the impedance cardiography. To test the algorithm performance, the beat-to-beat stroke volumes (SV) were measured with our device and the medis® CS 2000, synchronously. A total of 1466 pulses from 10 subjects were used to validate our algorithm in which the SQIs of 1007 pulses were high, those of 71 pulses were in the middle, and those of 388 pulses were low. The total error of SV measurement was −18 ± 22.0 mL. The performances of the classification were that the sensitivity and specificity for the 1007 pulses with the high SQIs were 0.81 and 0.90, and the error of SV measurement was 6.4 ± 12.8 mL. The sensitivity and specificity for the 388 pulses with the low SQIs were 0.84 and 0.93, while the error of SV measurement was 30.4 ± 3.6 mL. The results show that the proposed algorithm could be helpful in choosing good-quality PPG pulses to increase the accuracy of SV measurement in the impedance plethysmography. View Full-Text
Keywords: photoplethysmography; signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV); self-constructing neural fuzzy inference network (SoNFIN) photoplethysmography; signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV); self-constructing neural fuzzy inference network (SoNFIN)
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MDPI and ACS Style

Liu, S.-H.; Wang, J.-J.; Chen, W.; Pan, K.-L.; Su, C.-H. Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement. Appl. Sci. 2020, 10, 1476. https://doi.org/10.3390/app10041476

AMA Style

Liu S-H, Wang J-J, Chen W, Pan K-L, Su C-H. Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement. Applied Sciences. 2020; 10(4):1476. https://doi.org/10.3390/app10041476

Chicago/Turabian Style

Liu, Shing-Hong; Wang, Jia-Jung; Chen, Wenxi; Pan, Kuo-Li; Su, Chun-Hung. 2020. "Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement" Appl. Sci. 10, no. 4: 1476. https://doi.org/10.3390/app10041476

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