Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis

Visualization Engineering Platform for TCM Pulse Diagnosis - Pulse Diagnosis Based on Federated Learning to Diagnose Slippery and Choppy and Other Pulses Waveform Image Features to Assist in the Study of TCM Pathological Logic Analysis

The diagnoses processes of Traditional Chinese Medicine (TCM) focus on the following four main types of diagnoses methods consisting of inspection, olfaction, inquiry, and palpation. The most important one is palpation also called pulse diagnosis which is to measure wrist artery pulse by TCM doctor's fingers to detect patient's health state. The pulse diagnosis has three parts, namely 'Chun', 'Guan' and 'Chy', with the location. Wrist measurements correspond to different parts of the body's organs. In this project, it is to classify pulse types by using specialized pulse measuring instruments. The measured pulse wave (Measured Pulse Wave, MPW) was segmented into arterial pulse wave curves (APWC) by the image suggestion method. The research object of this project is to collect and group patients diagnosed by traditional Chinese medicine practitioners, namely slippery pulse, choppy pulse group and normal pulse control group, with at least 80 cases for each group. The research purpose of this project is mainly to carry out the visualization engineering platform of TCM pulse diagnosis - based on the pulse diagnosis of federated learning to diagnose the pulse waveform image features such as slippery pulse and choppy pulse to provide auxiliary TCM pathological logic analysis research and back-end cross-federal learning of TCM pulse diagnosis Implementation of the node system. In other words, it is expected that the pulse wave characteristics measured by TCM physicians who cooperate with experts in the field can be collected from many TCM pulse diagnosis federated learning nodes, and analyzed by the Multiple-Expert Repertory Grid Elicitation (MERGE) method. Finally, the artificial intelligence model based on FL is trained to carry out TCM pathological logic analysis and related research. The results will be provided to TCM physicians as an important reference to assist clinical diagnosis.

Inclusion Criteria:

1. Already sign test consent permit.
2. More than 20-year-old.

Exclusion Criteria:

1. There is a wound or inflammation at the wrist skin measurement.