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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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 14; S. 3829
Hauptverfasser:	Sadrawi, Muammar, Lin, Yin-Tsong, Lin, Chien-Hung, Mathunjwa, Bhekumuzi, Fan, Shou-Zen, Abbod, Maysam F., Shieh, Jiann-Shing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 09.07.2020 MDPI
Schlagworte:	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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Keywords	genetic algorithm deep convolutional autoencoder diastolic blood pressure photoplethysmography continuous arterial blood pressure systolic blood pressure
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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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