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...
Main Authors: | , , , , |
---|---|
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 |
id |
doaj-bad9d80f304b4d909d2e24b1c63e364b |
---|---|
record_format |
Article |
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>®</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 −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.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>®</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 −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. |
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 |
work_keys_str_mv |
AT shinghongliu classificationofphotoplethysmographicsignalqualitywithfuzzyneuralnetworkforimprovementofstrokevolumemeasurement AT jiajungwang classificationofphotoplethysmographicsignalqualitywithfuzzyneuralnetworkforimprovementofstrokevolumemeasurement AT wenxichen classificationofphotoplethysmographicsignalqualitywithfuzzyneuralnetworkforimprovementofstrokevolumemeasurement AT kuolipan classificationofphotoplethysmographicsignalqualitywithfuzzyneuralnetworkforimprovementofstrokevolumemeasurement AT chunhungsu classificationofphotoplethysmographicsignalqualitywithfuzzyneuralnetworkforimprovementofstrokevolumemeasurement |
_version_ |
1725323258775994368 |