Design and implementation of a photoplethysmography acquisition system with an optimized artificial neural network for accurate blood pressure measurement
A new neural network (NN) is orchestrated by this study to achieve high-accuracy in blood pressure (BP) estimation by a real-time photoplethysmography (PPG). The PPG system consists of an OLED/OPD module to detect the pulsation of blood vessels, followed by a readout circuitry. The circuit is compri...
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| Published in | Microsystem technologies : sensors, actuators, systems integration Vol. 27; no. 6; pp. 2345 - 2367 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0946-7076 1432-1858 |
| DOI | 10.1007/s00542-020-05109-9 |
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| Abstract | A new neural network (NN) is orchestrated by this study to achieve high-accuracy in blood pressure (BP) estimation by a real-time photoplethysmography (PPG). The PPG system consists of an OLED/OPD module to detect the pulsation of blood vessels, followed by a readout circuitry. The circuit is comprised of transimpedance amplifier, a digital tune high order band pass filter, programmable gain amplifier (PGA), time interleave OLED driver, micro-controller unit, and the Bluetooth transceiver. The obtained PPG signals are subsequently processed with quality checking, feature extraction, and into an NN for estimating BP. The feature extraction is assisted, by principal component analysis (PCA) to reduce the total number of input features to five with accuracy assured. 96 subjects participated in data collection for calibrating the designed NN. The resulted correlation is 0.81, while the errors for SBP and DBP are 2.00 ± 6.08 and 1.87 ± 4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS), a BP device in Grade A needs to control its accuracy error less than ± 8 mmHg, based on which the BP sensor developed herein are in Grade A, since the resulted errors of ± 6.08 and ± 4.09 mmHg are both less than ± 8 mmHg, showing the satisfactory performance of the BP monitor developed by this study. |
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| AbstractList | A new neural network (NN) is orchestrated by this study to achieve high-accuracy in blood pressure (BP) estimation by a real-time photoplethysmography (PPG). The PPG system consists of an OLED/OPD module to detect the pulsation of blood vessels, followed by a readout circuitry. The circuit is comprised of transimpedance amplifier, a digital tune high order band pass filter, programmable gain amplifier (PGA), time interleave OLED driver, micro-controller unit, and the Bluetooth transceiver. The obtained PPG signals are subsequently processed with quality checking, feature extraction, and into an NN for estimating BP. The feature extraction is assisted, by principal component analysis (PCA) to reduce the total number of input features to five with accuracy assured. 96 subjects participated in data collection for calibrating the designed NN. The resulted correlation is 0.81, while the errors for SBP and DBP are 2.00 ± 6.08 and 1.87 ± 4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS), a BP device in Grade A needs to control its accuracy error less than ± 8 mmHg, based on which the BP sensor developed herein are in Grade A, since the resulted errors of ± 6.08 and ± 4.09 mmHg are both less than ± 8 mmHg, showing the satisfactory performance of the BP monitor developed by this study. |
| Author | Lin, Tse-Yu Pandey, Rajeev Kumar Chao, Paul C.-P. |
| Author_xml | – sequence: 1 givenname: Rajeev Kumar surname: Pandey fullname: Pandey, Rajeev Kumar organization: EECS International Graduate Program, National Chiao Tung University – sequence: 2 givenname: Tse-Yu surname: Lin fullname: Lin, Tse-Yu organization: Department of Electrical Engineering, National Chiao Tung University – sequence: 3 givenname: Paul C.-P. surname: Chao fullname: Chao, Paul C.-P. email: pchao@mail.nctu.edu.tw organization: Department of Electrical Engineering, National Chiao Tung University |
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| Cites_doi | 10.1007/s00421-011-1983-3 10.1007/s00542-020-04946-y 10.1007/s00542-018-3877-3 10.1109/TBME.2013.2243148 10.1109/LSSC.2019.2957261 10.1109/TNN.2006.875973 10.1007/s11517-015-1410-8 10.1109/TBME.2011.2180019 10.1038/s41598-017-11507-3 10.1109/JBHI.2016.2614962 10.1109/JSSC.2016.2642205 10.1109/JSEN.2017.2704098 10.1038/sdata.2018.76 10.1186/s12938-016-0302-y 10.1109/ACCESS.2019.2939798 10.1007/s00542-020-04895-6 10.1109/ACCESS.2020.2981903 10.1097/ALN.0b013e31824f94ed 10.1109/JSEN.2014.2329676 10.1109/JSTQE.2018.2871604 10.1109/TIM.2019.2947103 10.1109/TBME.2014.2318779 10.1109/IEMBS.2009.5332505 10.1109/EMBC.2019.8857108 10.1109/SENSORS43011.2019.8956825 10.1109/I2MTC.2013.6555424 10.23919/VLSIC.2019.8778004 10.1109/ICSEM.2010.14 10.1109/EMBC.2016.7592189 10.22489/CinC.2016.081-339 10.1145/3055635.3056634 10.1109/ICSENS.2018.8589796 |
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| Title | Design and implementation of a photoplethysmography acquisition system with an optimized artificial neural network for accurate blood pressure measurement |
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