Classification research of TCM pulse conditions based on multi-label voice analysis

Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Use of Non-Verbal Representations to Define Concept of Pulse Conditions in Traditional Chinese Medicine Standards

The standardization of terms and definitions is fundamental to all activities in the domain of traditional Chinese medicine(TCM).For decades,definitions of TCM terminology relied on conventional verbal representations to differentiate between related concepts.However,the ancient Chinese is obscure and comprises a massive volume of information,making it difficult to convey the definition accurately in other languages.This article proposes a potential solution that the definition for pulse terminology can be supplemented by modern means of non-verbal representation,i.e.,using pulse waveform graphs and parameters to complete the definition of each pulse.A discussion of the challenges of obtaining reliable data is also included.

Design and Implementation of a Chinese Pulse Condition Acquisition System

Nowadays, with improvements in the quality of life, people are paying more attention to their health. Traditional Chinese medicine offers great advantages for daily care. In this paper, we present the development of a remote health care system, namely, Chinese Pulse Condition Acquisition System （CPCAS）, based on the principle of Chinese pulse diagnosis in Chinese medicine and a wireless sensor network. We designed a remote health care terminal with a mini-pulse collection bench to overcome the challenge of differences in pulse characters of different people. An effective measured pressure control algorithm is proposed to achieve a balance between control accuracy and control time. The special signal conditioning circuit showed good performance in analog pulse signal processing. We also performed significant research to address the challenges of symptom recognition. Other distinctive features of this system include the following： intelligent sensing, a wireless health care network, effective energy management, small size, lightweight, and the ability to be networked for remote management. In this paper, we have introduced the design and implementation of CPCAS. We also demonstrate the use of the system and give evaluations on this system by several experiments. Our results indicate that CPCAS has significant practical feasibility.