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

Abstract

ObjectiveTo explore the feasibility of remotely obtaining complex information on traditional Chinese medicine (TCM) pulse conditions through voice signals. MethodsWe 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. ResultsThe 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%. ConclusionVoice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.