Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photopl...

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Published in:	Sensors (Basel, Switzerland) Vol. 20; no. 14; p. 3829
Main Authors:	Sadrawi, Muammar, Lin, Yin-Tsong, Lin, Chien-Hung, Mathunjwa, Bhekumuzi, Fan, Shou-Zen, Abbod, Maysam F., Shieh, Jiann-Shing
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 09.07.2020 MDPI
Subjects:	Algorithms Arterial Pressure Blood Pressure Blood Pressure Determination Cardiac arrhythmia continuous arterial blood pressure Datasets deep convolutional autoencoder Deep learning diastolic blood pressure Electrocardiography Gas turbine engines genetic algorithm Humans Hypertension Hypertension - diagnosis Neural networks Patients Photoplethysmography systolic blood pressure Vital signs United States > US genetic algorithm deep convolutional autoencoder diastolic blood pressure photoplethysmography continuous arterial blood pressure systolic blood pressure
ISSN:	1424-8220, 1424-8220
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Abstract	Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.
AbstractList	Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal. Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.
Author	Abbod, Maysam F. Lin, Chien-Hung Fan, Shou-Zen Shieh, Jiann-Shing Mathunjwa, Bhekumuzi Sadrawi, Muammar Lin, Yin-Tsong
AuthorAffiliation	2 AI R&D Department, New Era AI Robotic Inc., Taipei 105, Taiwan; lotusytlin@neweraai.com (Y.-T.L.); lance_lin@neweraai.com (C.-H.L.) 4 Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK; Maysam.Abbod@brunel.ac.uk 3 Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan; shouzen@gmail.com 1 Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan; muammarsadrawi@yahoo.com (M.S); mathunjwabhekie@gmail.com (B.M.)
AuthorAffiliation_xml	– name: 3 Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan; shouzen@gmail.com – name: 1 Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan; muammarsadrawi@yahoo.com (M.S); mathunjwabhekie@gmail.com (B.M.) – name: 4 Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK; Maysam.Abbod@brunel.ac.uk – name: 2 AI R&D Department, New Era AI Robotic Inc., Taipei 105, Taiwan; lotusytlin@neweraai.com (Y.-T.L.); lance_lin@neweraai.com (C.-H.L.)
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Cites_doi	10.1016/j.compbiomed.2018.09.013 10.1007/s10916-018-0999-1 10.1109/SMARTCOMP.2016.7501681 10.3390/s120201816 10.3390/sym10030071 10.3390/s17112445 10.1038/srep45644 10.1007/978-3-319-24574-4_28 10.3390/jcm8010012 10.3390/e21121229 10.3390/app10062137 10.1109/ACCESS.2019.2912273 10.1093/bmb/ldh050 10.3390/s19153420 10.1155/2015/536863 10.1016/j.bspc.2019.02.028 10.1016/j.eswa.2014.08.007 10.1016/S0140-6736(08)60655-8 10.1016/S0002-8703(99)70312-1 10.1016/S0004-3702(02)00190-X 10.1109/IECBES.2016.7843473 10.3390/s20082338 10.1038/s41591-018-0268-3 10.1016/j.renene.2019.07.065 10.1109/CYBERNETICSCOM.2019.8875637 10.3390/en13071621 10.1109/5.726791 10.1016/j.compbiomed.2017.09.017 10.1038/nature14539 10.1109/IECBES.2016.7843455 10.1007/s00542-020-04782-0 10.3390/s19081866
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References	Lawes (ref_3) 2008; 371 Acharya (ref_29) 2018; 100 Phillips (ref_8) 2012; 12 ref_35 ref_12 He (ref_2) 1999; 138 ref_11 ref_33 ref_10 Zaidan (ref_20) 2015; 42 ref_32 Mlakar (ref_16) 2019; 19 Silitonga (ref_18) 2020; 146 ref_19 ref_17 ref_15 Liu (ref_30) 2019; 7 Hannun (ref_28) 2019; 25 Sadrawi (ref_21) 2015; 2015 Liu (ref_31) 2018; 42 ref_24 ref_23 ref_22 Wong (ref_4) 2005; 73 Tang (ref_6) 2017; 7 LeCun (ref_27) 2015; 521 LeCun (ref_34) 1998; 86 ref_1 Zhou (ref_25) 2002; 137 Tanveer (ref_13) 2019; 51 Zadi (ref_14) 2018; 102 ref_26 ref_9 ref_5 ref_7
References_xml	– volume: 102 start-page: 104 year: 2018 ident: ref_14 article-title: Arterial blood pressure feature estimation using photoplethysmography publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2018.09.013 – volume: 42 start-page: 148 year: 2018 ident: ref_31 article-title: Design and evaluation of a real time physiological signals acquisition system implemented in multi-operating rooms for anesthesia publication-title: J. Med. Syst. doi: 10.1007/s10916-018-0999-1 – ident: ref_12 doi: 10.1109/SMARTCOMP.2016.7501681 – volume: 12 start-page: 1816 year: 2012 ident: ref_8 article-title: Evaluation of electrical and optical plethysmography sensors for noninvasive monitoring of hemoglobin concentration publication-title: Sensors doi: 10.3390/s120201816 – ident: ref_26 doi: 10.3390/sym10030071 – ident: ref_32 – ident: ref_22 doi: 10.3390/s17112445 – volume: 7 start-page: 45644 year: 2017 ident: ref_6 article-title: Identification of atrial fibrillation by quantitative analyses of fingertip photoplethysmogram publication-title: Sci. Rep. doi: 10.1038/srep45644 – ident: ref_35 doi: 10.1007/978-3-319-24574-4_28 – ident: ref_7 doi: 10.3390/jcm8010012 – ident: ref_10 doi: 10.3390/e21121229 – ident: ref_9 doi: 10.3390/app10062137 – ident: ref_1 – volume: 7 start-page: 53731 year: 2019 ident: ref_30 article-title: Spectrum analysis of eeg signals using cnn to model patient’s consciousness level based on anesthesiologists’ experience publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912273 – volume: 73 start-page: 57 year: 2005 ident: ref_4 article-title: Hypertensive retinopathy signs as risk indicators of cardiovascular morbidity and mortality publication-title: Br. Med. Bull. doi: 10.1093/bmb/ldh050 – volume: 19 start-page: 3420 year: 2019 ident: ref_16 article-title: Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network publication-title: Sensors doi: 10.3390/s19153420 – volume: 2015 start-page: 13 year: 2015 ident: ref_21 article-title: Computational depth of anesthesia via multiple vital signs based on artificial neural networks publication-title: Biomed Res. Int. doi: 10.1155/2015/536863 – volume: 51 start-page: 382 year: 2019 ident: ref_13 article-title: Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network publication-title: Biomed. Signal Process. Control. doi: 10.1016/j.bspc.2019.02.028 – volume: 42 start-page: 539 year: 2015 ident: ref_20 article-title: Bayesian hierarchical models for aerospace gas turbine engine prognostics publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.08.007 – volume: 371 start-page: 1513 year: 2008 ident: ref_3 article-title: Global burden of blood-pressure-related disease, 2001 publication-title: Lancet doi: 10.1016/S0140-6736(08)60655-8 – ident: ref_33 – volume: 138 start-page: S211 year: 1999 ident: ref_2 article-title: Elevated systolic blood pressure and risk of cardiovascular and renal disease: Overview of evidence from observational epidemiologic studies and randomized controlled trials publication-title: Am. Heart J. doi: 10.1016/S0002-8703(99)70312-1 – volume: 137 start-page: 239 year: 2002 ident: ref_25 article-title: Ensembling neural networks: Many could be better than all publication-title: Artif. Intell. doi: 10.1016/S0004-3702(02)00190-X – ident: ref_11 doi: 10.1109/IECBES.2016.7843473 – ident: ref_15 doi: 10.3390/s20082338 – volume: 25 start-page: 65 year: 2019 ident: ref_28 article-title: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network publication-title: Nat. Med. doi: 10.1038/s41591-018-0268-3 – volume: 146 start-page: 1278 year: 2020 ident: ref_18 article-title: Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization publication-title: Renew. Energy doi: 10.1016/j.renene.2019.07.065 – ident: ref_17 doi: 10.1109/CYBERNETICSCOM.2019.8875637 – ident: ref_19 doi: 10.3390/en13071621 – volume: 86 start-page: 2278 year: 1998 ident: ref_34 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 – volume: 100 start-page: 270 year: 2018 ident: ref_29 article-title: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.09.017 – volume: 521 start-page: 436 year: 2015 ident: ref_27 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref_5 doi: 10.1109/IECBES.2016.7843455 – ident: ref_23 doi: 10.1007/s00542-020-04782-0 – ident: ref_24 doi: 10.3390/s19081866
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SubjectTerms	Algorithms Arterial Pressure Blood Pressure Blood Pressure Determination Cardiac arrhythmia continuous arterial blood pressure Datasets deep convolutional autoencoder Deep learning diastolic blood pressure Electrocardiography Gas turbine engines genetic algorithm Humans Hypertension Hypertension - diagnosis Neural networks Patients Photoplethysmography systolic blood pressure Vital signs
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Title	Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography
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