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| Name | Class |
|---|---|
| National Yang Ming Chiao Tung University | OTHER |
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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).
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Suboptimal health status questionnaires | 25 items of Suboptimal symptoms | 1 day |
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Inclusion Criteria:
(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:
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Participants have no chronic disease or those who have been diagnosed as Sub-optimal health status by a doctor.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yen-Ying KUNG, doctor | Contact | 886-2-28757453 | 333 | yykung@vghtpe.gov.tw |
| Chao-Hsiung Tseng, doctor | Contact | 886-2-27376416 | chtseng@mail.ntust.edu.tw |
| Name | Affiliation | Role |
|---|---|---|
| Yen-Ying KUNG, doctor | Taipei Veterans General Hospital, Taiwan | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Taipei Veterans General Hospital | Recruiting | Taipei | 112 | Taiwan |
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