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.

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.

Inclusion Criteria:

People who do not have a clear diagnosis of chronic diseases by Western medicine

Exclusion Criteria:

1. Western medicine confirms the diagnosis of chronic diseases, such as high blood pressure, diabetes, chronic hepatitis, chronic kidney disease, chronic hyperlipidemia, coronary heart disease, etc.
2. There is a clear diagnosis of mental illness by Western medicine
3. Cancer patients

"Sub-healthy state" is defined as a condition where there is no illness but unhealthy. It causes abnormal psychological and physiological changes under internal and external environmental stimulation, but it has not yet reached the level of obvious pathological response.