Comparison of three different types of wrist pulse signals by their physical meanings and diagnosis performance

Wangmeng Zuo, Peng Wang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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.
Original languageEnglish
Article number2369821
Pages (from-to)119-127
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 1 Jan 2016


  • Computational pulse diagnosis
  • photoelectric sensor
  • pressure sensor
  • pulse signal acquisition
  • ultrasonic sensor

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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