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

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 dif...

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Main Authors: Shing-Hong Liu, Jia-Jung Wang, Wenxi Chen, Kuo-Li Pan, Chun-Hung Su
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/10/4/1476
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spelling doaj-bad9d80f304b4d909d2e24b1c63e364b2020-11-25T00:31:11ZengMDPI AGApplied Sciences2076-34172020-02-01104147610.3390/app10041476app10041476Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume MeasurementShing-Hong Liu0Jia-Jung Wang1Wenxi Chen2Kuo-Li Pan3Chun-Hung Su4Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City 41349, TaiwanDepartment of Biomedical Engineering, I-Shou University, Kaohsiung City 82445, TaiwanBiomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, JapanDivision of Cardiology, Chiayi Chang Gung Memorial Hospital, Chiayi City 61363, TaiwanInstitute of Medicine, School of Medicine, Chung-Shan Medical University, Taichung 402, TaiwanPhotoplethysmography (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<sup>&#174;</sup> 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 &#8722;18 &#177; 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 &#177; 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 &#177; 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.https://www.mdpi.com/2076-3417/10/4/1476photoplethysmographysignal quality index (sqi)impedance cardiography (icg)stroke volume (sv)self-constructing neural fuzzy inference network (sonfin)
collection DOAJ
language English
format Article
sources DOAJ
author Shing-Hong Liu
Jia-Jung Wang
Wenxi Chen
Kuo-Li Pan
Chun-Hung Su
spellingShingle Shing-Hong Liu
Jia-Jung Wang
Wenxi Chen
Kuo-Li Pan
Chun-Hung Su
Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
Applied Sciences
photoplethysmography
signal quality index (sqi)
impedance cardiography (icg)
stroke volume (sv)
self-constructing neural fuzzy inference network (sonfin)
author_facet Shing-Hong Liu
Jia-Jung Wang
Wenxi Chen
Kuo-Li Pan
Chun-Hung Su
author_sort Shing-Hong Liu
title Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
title_short Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
title_full Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
title_fullStr Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
title_full_unstemmed Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
title_sort classification of photoplethysmographic signal quality with fuzzy neural network for improvement of stroke volume measurement
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2020-02-01
description 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<sup>&#174;</sup> 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 &#8722;18 &#177; 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 &#177; 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 &#177; 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.
topic photoplethysmography
signal quality index (sqi)
impedance cardiography (icg)
stroke volume (sv)
self-constructing neural fuzzy inference network (sonfin)
url https://www.mdpi.com/2076-3417/10/4/1476
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