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Zu Soh

Showing results (21-30 of 49) with videos related to

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ASAIO Journal (American Society for Artificial Internal Organs : 1992)|April 18, 2024
Correlation Analysis Between Echinocytosis Stages and Blood Viscosity During Oxygenator Perfusion: An In Vitro StudyShigeyuki Okahara, Satoshi Miyamoto, Zu Soh, et al.
Scientific Reports|May 25, 2023
Cough sound-based estimation of vital capacity via cough peak flow using artificial neural network analysisYasutaka Umayahara, Zu Soh, Akira Furui, et al.
IEEE Transactions on Bio-Medical Engineering|September 24, 2016
Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass ApplicationsShigeyuki Okahara, Zu Soh, Satoshi Miyamoto, et al.
IEEE Journal of Translational Engineering in Health and Medicine|August 3, 2023
Simultaneous Control of Venous Reservoir Level and Arterial Flow Rate in Cardiopulmonary Bypass With a Centrifugal PumpHidenobu Takahashi, Takuya Kinoshita, Zu Soh, et al.
Scientific Reports|January 31, 2020
Markerless Measurement and Evaluation of General Movements in InfantsToshio Tsuji, Shota Nakashima, Hideaki Hayashi, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application StudySatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Artificial Organs|November 4, 2016
A Novel Blood Viscosity Estimation Method Based on Pressure-Flow Characteristics of an Oxygenator During Cardiopulmonary BypassShigeyuki Okahara, Zu Soh, Satoshi Miyamoto, et al.
IEEE Open Journal of Engineering in Medicine and Biology|March 15, 2024
The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood TemperatureSatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Scientific Reports|January 13, 2021
Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypassSatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Scientific Reports|February 9, 2024
Neuroimaging-based evidence for sympathetic correlation between brain activity and peripheral vasomotion during pain anticipationZiqiang Xu, Zu Soh, Yuta Kurota, et al.
Pageof 5

Showing results (21-30 of 49) with videos related to

Sort By:
Pageof 5
ASAIO Journal (American Society for Artificial Internal Organs : 1992)|April 18, 2024
Correlation Analysis Between Echinocytosis Stages and Blood Viscosity During Oxygenator Perfusion: An In Vitro StudyShigeyuki Okahara, Satoshi Miyamoto, Zu Soh, et al.
Scientific Reports|May 25, 2023
Cough sound-based estimation of vital capacity via cough peak flow using artificial neural network analysisYasutaka Umayahara, Zu Soh, Akira Furui, et al.
IEEE Transactions on Bio-Medical Engineering|September 24, 2016
Continuous Blood Viscosity Monitoring System for Cardiopulmonary Bypass ApplicationsShigeyuki Okahara, Zu Soh, Satoshi Miyamoto, et al.
IEEE Journal of Translational Engineering in Health and Medicine|August 3, 2023
Simultaneous Control of Venous Reservoir Level and Arterial Flow Rate in Cardiopulmonary Bypass With a Centrifugal PumpHidenobu Takahashi, Takuya Kinoshita, Zu Soh, et al.
Scientific Reports|January 31, 2020
Markerless Measurement and Evaluation of General Movements in InfantsToshio Tsuji, Shota Nakashima, Hideaki Hayashi, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application StudySatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Artificial Organs|November 4, 2016
A Novel Blood Viscosity Estimation Method Based on Pressure-Flow Characteristics of an Oxygenator During Cardiopulmonary BypassShigeyuki Okahara, Zu Soh, Satoshi Miyamoto, et al.
IEEE Open Journal of Engineering in Medicine and Biology|March 15, 2024
The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood TemperatureSatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Scientific Reports|January 13, 2021
Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypassSatoshi Miyamoto, Zu Soh, Shigeyuki Okahara, et al.
Scientific Reports|February 9, 2024
Neuroimaging-based evidence for sympathetic correlation between brain activity and peripheral vasomotion during pain anticipationZiqiang Xu, Zu Soh, Yuta Kurota, et al.
Pageof 5