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Pulse Momentum Research in Pulse Diagnosis

NCT ID: NCT05522413

Last Updated: 2022-08-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-08-10

Study Completion Date

2023-07-10

Brief Summary

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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).

Detailed Description

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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.

Conditions

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Suboptimal Health Status

Keywords

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discrete wavelet transformation, Pulse diagnosis in TCM

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Health Status

normal examination in recent six months and not fit inclusion criteria of suboptimal health status.

No interventions assigned to this group

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

No interventions assigned to this group

Eligibility Criteria

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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

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
Minimum Eligible Age

20 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Yang Ming Chiao Tung University

OTHER

Sponsor Role collaborator

Taipei Veterans General Hospital, Taiwan

OTHER_GOV

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Yen-Ying KUNG, doctor

Role: STUDY_DIRECTOR

Taipei Veterans General Hospital, Taiwan

Locations

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Taipei Veterans General Hospital

Taipei, , Taiwan

Site Status RECRUITING

Countries

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Taiwan

Central Contacts

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Yen-Ying KUNG, doctor

Role: CONTACT

Phone: 886-2-28757453

Email: [email protected]

Chao-Hsiung Tseng, doctor

Role: CONTACT

Phone: 886-2-27376416

Email: [email protected]

Facility Contacts

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Yen-Ying KUNG, doctor

Role: primary

Other Identifiers

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2022-06-014AC

Identifier Type: -

Identifier Source: org_study_id