Visualization Engineering Platform for Pulse Diagnosis of Traditional Chinese Medicine-The Research of Similar Moiré Feature Analyzing Approach Based on Recurrent Neural Network to Process the Measured Slip Pulse Wave-Images With Chun, Guan and Chy

The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by TCM doctor's fingers to detect patient's health state. The pulse diagnosis has three parts, namely 'Chun', 'Guan' and 'Chy', with the location. Wrist measurements correspond to different parts of the body's organs. However, the pulse information is analyzed by TCM doctor's pulse diagnoses process, which is picked only a single waveform from a long-term pulse measured process and often discarded the other waveforms contained in the same information, e.g. Slip Pulse waveform. The research object of this project is to divide the TCM diagnosis patients into two groups, one is the Slip pulse group and the other is a Flat Pulse group, in which the cases are collected at least 50 cases for the first gruop and 30 cases for the second group, individually. The purpose of this project will integrate the TCM doctor's experiences and standardize them in order to construct an effective Slip pulse diagnosis system based on visualization engineering platform. It could refine Slip pulse diagnoses processes and reduce their diagnoses loading by using the proposed approach during this project. Therefore, we will propose a similar Moiré feature analyzing approach based on Recurrent Neural Network to process the measured Slip Pulse wave-images with 'Chun', 'Guan' and 'Chy' in order to prepare this visualization engineering platform. We will measure the waveform images of the three parts of the wrist as 'Chun', 'Guan' and 'Chy' with the moiré feature technology proposed in this project. It mainly extracts the moiré features such as pulse image from numerous measurement 'Chun', 'Guan' and 'Chy' signals, and further analyzes and summarizes them with AI technology. We could extract the pulse characteristics of Slip pulse automatically from the many measurement signals and enhance the automatic judging ability of the current pulse instrument to the Slip pulse waveform. Provide important pulse information to the TCM doctors as an important reference for precision clinical diagnoses.

Study Overview

• Status: Unknown
• Conditions: Slippery Pulse
• Intervention / Treatment: Diagnostic test: Recurrent Neural Network

• Study Type: Observational
• Enrollment (Anticipated): 80

Contacts and Locations

Study Locations

• Taiwan
  • North District
    • Taichung, North District, Taiwan, 40447
      • Recruiting
      • China Medical University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Outpatient.

Description

Inclusion Criteria:

1. Already sign test consent permit.
2. More than 20-year-old.

Exclusion Criteria:

1. There is a wound or inflammation at the wrist skin measurement.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Slippery Pulse	The purpose of this project will integrate the TCM doctor's experiences and standardize them in order to construct an effective Slippery Pulse diagnosis system.	30 minutes in duration

Collaborators and Investigators

• Sponsor: China Medical University Hospital

Study record dates

Study Major Dates

• Study Start (Anticipated): December 1, 2020
• Primary Completion (Anticipated): December 1, 2021
• Study Completion (Anticipated): December 1, 2021

Study Registration Dates

• First Submitted: December 4, 2020
• First Submitted That Met QC Criteria: December 4, 2020
• First Posted (Actual): December 10, 2020

Study Record Updates

• Last Update Posted (Actual): December 10, 2020
• Last Update Submitted That Met QC Criteria: December 4, 2020
• Last Verified: December 1, 2020

More Information

Terms related to this study

• Keywords: Artificial intelligence; Recurrent Neural Network; Wrist artery pulse diagnosis instrument; TCM pulse diagnosis; Feature extraction
• Additional Relevant MeSH Terms: Pathologic Processes; Disease
• Other Study ID Numbers: CMUH109-REC2-159

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No