Classification of TCM pulse diagnoses based on pulse and periodic features from personal health data

Kiichi Tago, Haidong Wang, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Pulse diagnosis is one of the diagnostic methods in traditional Chinese medicine (TCM). Such diagnoses are made subjectively by a TCM doctor, who requires expert knowledge. If pulse diagnosis could be automated, it would be beneficial for health management. In our previous study, we showed that pulse diagnosis might be related to personal health data, such as step count and sleep score. In this study, we propose a new approach to classifying pulse diagnoses based on a combination of features from pulse and health data. Pulse characteristics are extracted from electronically recorded pulse shapes, and health data feature analysis is augmented by considering the periodicity of daily health metrics. Using these features, we perform both single- and multi-label classifications, and investigate the possibility to improve classification accuracy. We further adopt two classification methods for multi-label classification: random forests and deep learning. Our results show that our approach improves classification accuracy for pulse diagnoses.

Original languageEnglish
Title of host publication2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109626
DOIs
Publication statusPublished - 2019 Dec
Event2019 IEEE Global Communications Conference, GLOBECOM 2019 - Waikoloa, United States
Duration: 2019 Dec 92019 Dec 13

Publication series

Name2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

Conference

Conference2019 IEEE Global Communications Conference, GLOBECOM 2019
CountryUnited States
CityWaikoloa
Period19/12/919/12/13

Keywords

  • Personal health data
  • Pulse diagnoses classification
  • Traditional Chinese Medicine (TCM)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Health Informatics

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