Pulse Momentum Research in Pulse Diagnosis

Quantitative Research on Pulse Momentum in Pulse Diagnosis of Traditional Chinese Medicine

Traditional Chinese medicine has a long history of disease diagnosis applications by pulse diagnosis. The pulse "position", "number", "shape", and "momentum" can be used as four guidelines for pulse classification. However, the finger feeling is difficult to be expressed in a quantitative approach for clinical teaching and illness-state recognition. The pressure sensor was applied to measure wrist pulse waveforms for analysis. In this research project, the "discrete wavelet transformation (DWT)" is used to decompose the time-domain pulse into several sets of signals, which are allocated at different frequency bands. The high-frequency signal over the range of 12-50 Hz is then acquired to calculate the spectral energy ratio (SER) for quantization of the pulse momentum to the persons under the suboptimal health status (SHS).

Study Overview

• Status: Recruiting
• Conditions: Suboptimal Health Status

Detailed Description

Traditional Chinese medicine has a long history of disease diagnosis applications by pulse diagnosis. Ancient physicians classified the pulse types on the basis of pulse manifestation attributes and finger-feeling features. The pulse "position", "number", "shape", and "momentum" can be used as four guidelines for pulse classification. However, the finger feeling is difficult to be expressed in a quantitative approach for clinical teaching and illness-state recognition. The modernization of pulse diagnosis in Taiwan began in the 1970s. The pressure sensor was applied to measure wrist pulse waveforms for analysis. Nowadays, the pulse "position", "number", and "shape" have been quantitatively analyzed and classified by using time-domain pulse signals and their corresponding frequency spectrums. However, since it is lack of effective high-frequency pulse acquisition method and quantitative approach, the quantitative research on "pulse momentum" for judgement of pathological status is still being investigated.

In this research project, the "discrete wavelet transformation (DWT)" is used to decompose the time-domain pulse into several sets of signals, which are allocated at different frequency bands. The high-frequency signal over the range of 12-50 Hz is then acquired to calculate the spectral energy ratio (SER) for quantization of the pulse momentum. In addition, the approximate entropy (ApEn) of the high-frequency signal is computed and defined as a new quantitative factor of pulse momentum. It will be further tried to relate the scores of clinical questionnaires. The analysis method proposed in this project has been preliminarily applied to analyze the pulse waveforms of the persons under the suboptimal health status (SHS) to demonstrate the effectiveness. In the future, more measured pulses of the subject under test will be collected and analyzed to examine the robustness of the proposed method. It is also planned to figure out the relationship between the quantitative factors, such as SER and ApEn, and the high- and low-frequency parameters of the heart rate variability (HRV). It can be further linked to the activation of sympathetic and parasympathetic nerves, and potentially build up an objective bridge of clinical diagnosis to connect the traditional Chinese medicine and modern western medicine.

• Study Type: Observational
• Enrollment (Anticipated): 100

Contacts and Locations

• Study Contact: Name: Yen-Ying KUNG, doctor; Phone Number: 333 886-2-28757453; Email: yykung@vghtpe.gov.tw
• Study Contact Backup: Name: Chao-Hsiung Tseng, doctor; Phone Number: 886-2-27376416; Email: chtseng@mail.ntust.edu.tw

Study Locations

• Taiwan
  • Taipei, Taiwan, 112
    • Recruiting
    • Taipei Veterans General Hospital
    • Contact: Yen-Ying KUNG, doctor

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years to 70 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Participants have no chronic disease or those who have been diagnosed as Sub-optimal health status by a doctor.

Description

Inclusion Criteria:

• A+B+C or A+B+D that meet the following description, and those who have no clear diagnosis of chronic diseases by western medicine, can be included:

(A) Sub-Health Questionnaire (SHSQ-25) ≧35 points (B) Resting blood pressure 120-139/80-89 mmHg measured more than 3 times a week (C) The PSQI score of the sleep questionnaire on the first test is greater than 5 points (D) Body mass index (BMI): 24~29 Kg/m2

Exclusion Criteria:

• Considerations for selection/exclusion criteria include:

  1. Those with a clear diagnosis of chronic diseases in Western medicine, such as hypertension, diabetes, chronic hepatitis, chronic kidney disease, chronic hyperlipidemia, coronary heart disease, etc., which fall within the scope of chronic diseases under the National Health Insurance
  2. Have a definite diagnosis of mental illness by Western medicine
  3. Cancer patients
  4. Pregnancy
  5. Those with obvious inflammatory infection at the time of receipt of the case

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Cross-Sectional

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Health Status; normal examination in recent six months and not fit inclusion criteria of suboptimal health status.
Suboptimal health Status; (A) Sub-Health Questionnaire (SHSQ-25) ≧35 points (B) Resting blood pressure 120-139/80-89 mmHg measured more than 3 times a week (C) The PSQI score of the sleep questionnaire on the first test is greater than 5 points (D) Body mass index (BMI): 24~29 Kg/m2

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Pulse diagnosis data analysis	Palpation of the 6 pulse positions (right cun, right guan, right chi, left cun, left guan, left chi) measured by the pulse diagnostic instrument are read into the processing program, and then the time domain signal of each pulse position is analyzed in sequence .	1 day

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Suboptimal health status questionnaires	25 items of Suboptimal symptoms	1 day

Collaborators and Investigators

• Sponsor: Taipei Veterans General Hospital, Taiwan
• Collaborators: National Yang Ming University
• Investigators: Study Director: Yen-Ying KUNG, doctor, Taipei Veterans General Hospital, Taiwan

Study record dates

Study Major Dates

• Study Start (Actual): August 10, 2022
• Primary Completion (Anticipated): July 10, 2023
• Study Completion (Anticipated): July 10, 2023

Study Registration Dates

• First Submitted: August 28, 2022
• First Submitted That Met QC Criteria: August 29, 2022
• First Posted (Actual): August 31, 2022

Study Record Updates

• Last Update Posted (Actual): August 31, 2022
• Last Update Submitted That Met QC Criteria: August 29, 2022
• Last Verified: August 1, 2022

More Information

Terms related to this study

• Keywords: discrete wavelet transformation, Pulse diagnosis in TCM
• Additional Relevant MeSH Terms: Pathologic Processes; Disease
• Other Study ID Numbers: 2022-06-014AC

Drug and device information, study documents

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