Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/628326
Title: Comparison of Three Different Types of Wrist Pulse Signals by Their Physical Meanings and Diagnosis Performance
Authors: Wangmeng Zuo;Peng Wang;David Zhang
subject: pressure sensor;photoelectric sensor;computational pulse diagnosis;ultrasonic sensor;pulse signal acquisition
Year: 2016
Publisher: IEEE
Abstract: Increasing interest has been focused on computational pulse diagnosis where sensors are developed to acquire pulse signals, and machine learning techniques are exploited to analyze health conditions based on the acquired pulse signals. By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this paper, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependence and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
URI: http://localhost/handle/Hannan/163049
http://localhost/handle/Hannan/628326
ISSN: 2168-2194
2168-2208
volume: 20
issue: 1
Appears in Collections:2016

Files in This Item:
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6981906.pdf1.25 MBAdobe PDFThumbnail
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Title: Comparison of Three Different Types of Wrist Pulse Signals by Their Physical Meanings and Diagnosis Performance
Authors: Wangmeng Zuo;Peng Wang;David Zhang
subject: pressure sensor;photoelectric sensor;computational pulse diagnosis;ultrasonic sensor;pulse signal acquisition
Year: 2016
Publisher: IEEE
Abstract: Increasing interest has been focused on computational pulse diagnosis where sensors are developed to acquire pulse signals, and machine learning techniques are exploited to analyze health conditions based on the acquired pulse signals. By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this paper, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependence and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
URI: http://localhost/handle/Hannan/163049
http://localhost/handle/Hannan/628326
ISSN: 2168-2194
2168-2208
volume: 20
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
6981906.pdf1.25 MBAdobe PDFThumbnail
Preview File
Title: Comparison of Three Different Types of Wrist Pulse Signals by Their Physical Meanings and Diagnosis Performance
Authors: Wangmeng Zuo;Peng Wang;David Zhang
subject: pressure sensor;photoelectric sensor;computational pulse diagnosis;ultrasonic sensor;pulse signal acquisition
Year: 2016
Publisher: IEEE
Abstract: Increasing interest has been focused on computational pulse diagnosis where sensors are developed to acquire pulse signals, and machine learning techniques are exploited to analyze health conditions based on the acquired pulse signals. By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this paper, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependence and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
URI: http://localhost/handle/Hannan/163049
http://localhost/handle/Hannan/628326
ISSN: 2168-2194
2168-2208
volume: 20
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
6981906.pdf1.25 MBAdobe PDFThumbnail
Preview File