Ignite Creation Date:
2024-05-06 @ 3:53 PM
Last Modification Date:
2024-10-26 @ 1:59 PM
Study NCT ID:
NCT04799756
Status:
UNKNOWN
Last Update Posted:
2021-04-30
First Post:
2021-03-14
Brief Title:
Pulse Diagnosis of Traditional Chinese Medicine
Sponsor:
Taipei Veterans General Hospital Taiwan
Organization:
Taipei Veterans General Hospital Taiwan
Study Overview
Official Title:
To Develop Pulse Diagnosis of Traditional Chinese Medicine by Deep Learning
Status:
UNKNOWN
Status Verified Date:
2021-04
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Taking pulse as a disease diagnosis process has a long history in traditional Chinese medicine TCM Ancient physicians used the common attributes of pulse conditions and finger-feeling characteristics as a basis for pulse classification which position rate shape and tendency is the principle for pulse differentiation However it is not easy to express feelings of hands in a scientific way and not easy for clinical teaching and practice
To develope a new direction of pulse diagnosis in TCM by deep learning and integrative time-frequency domain analysis maybe can be solved the problem
To develope a new direction of pulse diagnosis in TCM by deep learning and integrative time-frequency domain analysis maybe can be solved the problem
Detailed Description:
Taking pulse as a disease diagnosis process has a long history in traditional Chinese medicine TCM Ancient physicians used the common attributes of pulse conditions and finger-feeling characteristics as a basis for pulse classification which position rate shape and tendency is the principle for pulse differentiation However it is not easy to express feelings of hands in a scientific way and not easy for clinical teaching and practice The modernization of pulse diagnosis in Taiwan originated in the 1970s By using pressure waves of the radial artery two methods were developed time-domain analysis and frequency domain analysis Dr Huang used time-domain analysis combined with frequency-domain analysis of 6-sec pulse waves to quantify 28 pulse patterns in TCM Professor Wang measured a single pulse wave and performed Fourier transformation to obtain the corresponding 12 meridian frequency spectrum but it is very different from the clinical practice of pulse diagnosis Our team found that the frequency-domain and the tim-domain analysis can be integrated if Fourier transformation integral formula is applied Because the extracted data is big the characteristic values of time and frequency domain analysis are calculated and judged by deep learning method
The purpose of this study is to use the Integration analysis of time-domain method to extract the characteristic values of the radial pulse and then use deep learning for model training That is after measuring the pulse waves at different positions and depths of the bilateral radial arteries by using the pulse diagnostic instrument to initial signal processing and to get a single pulse Then Fourier transformation is performed to obtain the magnitude and phase parameters of the 12 harmonics 24 variables in total and then extract 7 time-domain characteristic parameters of a single pulse The next step to perform Fourier transformation again using the 6-second pulse waves to obtain high and low frequency spectrum by using above parameters The feature parameters obtained by the above two analysis methods are simultaneously sent to the deep learning-convolution neuron network CNN training Since the pulse wave changes of the radial artery are related to time CNN combined with long-short-term memory work LSTM is also used to do the above-mentioned model training It is set to compare the differences between the pulse waves of healthy subjects and subjects with the suboptimal health status It is also proved whether the frequency-domain analysis analysis method by Professor Wang and the time-domain analysis method by Dr Huang is the same through the deep learning training process It is possible to develope a new direction of pulse diagnosis in TCM by deep learning and integrative time-frequency domain analysis
The purpose of this study is to use the Integration analysis of time-domain method to extract the characteristic values of the radial pulse and then use deep learning for model training That is after measuring the pulse waves at different positions and depths of the bilateral radial arteries by using the pulse diagnostic instrument to initial signal processing and to get a single pulse Then Fourier transformation is performed to obtain the magnitude and phase parameters of the 12 harmonics 24 variables in total and then extract 7 time-domain characteristic parameters of a single pulse The next step to perform Fourier transformation again using the 6-second pulse waves to obtain high and low frequency spectrum by using above parameters The feature parameters obtained by the above two analysis methods are simultaneously sent to the deep learning-convolution neuron network CNN training Since the pulse wave changes of the radial artery are related to time CNN combined with long-short-term memory work LSTM is also used to do the above-mentioned model training It is set to compare the differences between the pulse waves of healthy subjects and subjects with the suboptimal health status It is also proved whether the frequency-domain analysis analysis method by Professor Wang and the time-domain analysis method by Dr Huang is the same through the deep learning training process It is possible to develope a new direction of pulse diagnosis in TCM by deep learning and integrative time-frequency domain analysis
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None