Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump

This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotati...
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Autor*in:	Dang Caixin [verfasserIn] Wang Shuai [verfasserIn] Yu Zheqin [verfasserIn] Wu Weiqiang [verfasserIn] Wu Kun [verfasserIn] Tan Jianping [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2020)
Übergeordnetes Werk:	year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2020.0045

Katalog-ID:	DOAJ061919667

Internformat


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245	1	0	\|a Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
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520			\|a This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions.
650		4	\|a blood vessels
650		4	\|a blood pressure measurement
650		4	\|a pulsatile flow
650		4	\|a haemodynamics
650		4	\|a multilayer perceptrons
650		4	\|a blood
650		4	\|a pumps
650		4	\|a differential pressure
650		4	\|a multilayer perceptron
650		4	\|a axial flow blood pump
650		4	\|a noninvasive method
650		4	\|a pulsating flow
650		4	\|a estimation model
650		4	\|a pulsating conditions
650		4	\|a pulsation waveform
650		4	\|a flow error
650		4	\|a pressure difference error
650		4	\|a traditional flow
650		4	\|a pressure prediction method
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Wang Shuai \|e verfasserin \|4 aut
700	0		\|a Yu Zheqin \|e verfasserin \|4 aut
700	0		\|a Wu Weiqiang \|e verfasserin \|4 aut
700	0		\|a Wu Kun \|e verfasserin \|4 aut
700	0		\|a Tan Jianping \|e verfasserin \|4 aut
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publishDate	2020
allfields	10.1049/joe.2020.0045 doi (DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2 DE-627 ger DE-627 rakwb eng TA1-2040 Dang Caixin verfasserin aut Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions. blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General) Wang Shuai verfasserin aut Yu Zheqin verfasserin aut Wu Weiqiang verfasserin aut Wu Kun verfasserin aut Tan Jianping verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2020.0045 kostenfrei https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
spelling	10.1049/joe.2020.0045 doi (DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2 DE-627 ger DE-627 rakwb eng TA1-2040 Dang Caixin verfasserin aut Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions. blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General) Wang Shuai verfasserin aut Yu Zheqin verfasserin aut Wu Weiqiang verfasserin aut Wu Kun verfasserin aut Tan Jianping verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2020.0045 kostenfrei https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfields_unstemmed	10.1049/joe.2020.0045 doi (DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2 DE-627 ger DE-627 rakwb eng TA1-2040 Dang Caixin verfasserin aut Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions. blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General) Wang Shuai verfasserin aut Yu Zheqin verfasserin aut Wu Weiqiang verfasserin aut Wu Kun verfasserin aut Tan Jianping verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2020.0045 kostenfrei https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfieldsGer	10.1049/joe.2020.0045 doi (DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2 DE-627 ger DE-627 rakwb eng TA1-2040 Dang Caixin verfasserin aut Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions. blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General) Wang Shuai verfasserin aut Yu Zheqin verfasserin aut Wu Weiqiang verfasserin aut Wu Kun verfasserin aut Tan Jianping verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2020.0045 kostenfrei https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
allfieldsSound	10.1049/joe.2020.0045 doi (DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2 DE-627 ger DE-627 rakwb eng TA1-2040 Dang Caixin verfasserin aut Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions. blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General) Wang Shuai verfasserin aut Yu Zheqin verfasserin aut Wu Weiqiang verfasserin aut Wu Kun verfasserin aut Tan Jianping verfasserin aut In The Journal of Engineering Wiley, 2013 (2020) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2020 https://doi.org/10.1049/joe.2020.0045 kostenfrei https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 kostenfrei https://doaj.org/toc/2051-3305 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2020
language	English
source	In The Journal of Engineering (2020) year:2020
sourceStr	In The Journal of Engineering (2020) year:2020
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Dang Caixin @@aut@@ Wang Shuai @@aut@@ Yu Zheqin @@aut@@ Wu Weiqiang @@aut@@ Wu Kun @@aut@@ Tan Jianping @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ061919667
language_de	englisch
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callnumber-first	T - Technology
author	Dang Caixin
spellingShingle	Dang Caixin misc TA1-2040 misc blood vessels misc blood pressure measurement misc pulsatile flow misc haemodynamics misc multilayer perceptrons misc blood misc pumps misc differential pressure misc multilayer perceptron misc axial flow blood pump misc noninvasive method misc pulsating flow misc estimation model misc pulsating conditions misc pulsation waveform misc flow error misc pressure difference error misc traditional flow misc pressure prediction method misc Engineering (General). Civil engineering (General) Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
authorStr	Dang Caixin
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topic_title	TA1-2040 Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump blood vessels blood pressure measurement pulsatile flow haemodynamics multilayer perceptrons blood pumps differential pressure multilayer perceptron axial flow blood pump noninvasive method pulsating flow estimation model pulsating conditions pulsation waveform flow error pressure difference error traditional flow pressure prediction method
topic	misc TA1-2040 misc blood vessels misc blood pressure measurement misc pulsatile flow misc haemodynamics misc multilayer perceptrons misc blood misc pumps misc differential pressure misc multilayer perceptron misc axial flow blood pump misc noninvasive method misc pulsating flow misc estimation model misc pulsating conditions misc pulsation waveform misc flow error misc pressure difference error misc traditional flow misc pressure prediction method misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc blood vessels misc blood pressure measurement misc pulsatile flow misc haemodynamics misc multilayer perceptrons misc blood misc pumps misc differential pressure misc multilayer perceptron misc axial flow blood pump misc noninvasive method misc pulsating flow misc estimation model misc pulsating conditions misc pulsation waveform misc flow error misc pressure difference error misc traditional flow misc pressure prediction method misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc blood vessels misc blood pressure measurement misc pulsatile flow misc haemodynamics misc multilayer perceptrons misc blood misc pumps misc differential pressure misc multilayer perceptron misc axial flow blood pump misc noninvasive method misc pulsating flow misc estimation model misc pulsating conditions misc pulsation waveform misc flow error misc pressure difference error misc traditional flow misc pressure prediction method misc Engineering (General). Civil engineering (General)
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title	Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
ctrlnum	(DE-627)DOAJ061919667 (DE-599)DOAJd3ca1aacd0a64f819329cc57bdcbe4b2
title_full	Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
author_sort	Dang Caixin
journal	The Journal of Engineering
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author_browse	Dang Caixin Wang Shuai Yu Zheqin Wu Weiqiang Wu Kun Tan Jianping
class	TA1-2040
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author-letter	Dang Caixin
doi_str_mv	10.1049/joe.2020.0045
author2-role	verfasserin
title_sort	estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
callnumber	TA1-2040
title_auth	Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
abstract	This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions.
abstractGer	This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions.
abstract_unstemmed	This study proposes a non-invasive method for estimating the pulsating flow and pressure difference, which uses the blood pump estimation model based on a multi-layer perceptron to calculate the flow and pressure difference under pulsating conditions. The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. Compared with the traditional flow and pressure prediction method, this method has higher precision, which will provide a certain technical accumulation for accurately estimating the flow and pressure difference of the blood pump in the pulsating conditions.
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title_short	Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump
url	https://doi.org/10.1049/joe.2020.0045 https://doaj.org/article/d3ca1aacd0a64f819329cc57bdcbe4b2 https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0045 https://doaj.org/toc/2051-3305
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author2	Wang Shuai Yu Zheqin Wu Weiqiang Wu Kun Tan Jianping
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doi_str	10.1049/joe.2020.0045
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up_date	2024-07-03T23:24:34.002Z
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The model takes 11 parameters such as the rotational speed, power, and pulsation waveform of the blood pump as the input and uses the pressure difference and flow as the output. The experimental results of 119,590 sample data show that the flow error of the training set of the blood pump estimation model is 0.14 l/min and the pressure difference error is 7.50 mmHg; the flow error of the test set is 0.14 l/min and the pressure difference error is 7.50 mmHg. 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Estimation of pulsatile flow and differential pressure based on multi-layer perceptron using an axial flow blood pump

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