Nonlinear time series analysis of pulse patterns

DOI: 10.12201/bmr.202603.00079

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  Abstract: This research aims to transform pulse conditions into nonlinear time series analysis to enhance the objectivity and scientific nature of pulse condition research and promote the modernization process of traditional Chinese medicine. Methods such as recurrence plot analysis and temporal convolutional networks were used to analyze the preprocessed pulse condition data. The study found that different types of pulse conditions exhibit unique characteristic patterns under nonlinear time series analysis, such as significant differences in parameters such as recurrence rate and determinism, providing a scientific basis for the accurate identification and classification of pulse conditions. In addition, the pulse condition classification model constructed using a temporal convolutional network achieved high accuracy. The research results not only help to deepen the understanding of the formation mechanism of pulse conditions but also provide a more objective and accurate technical means for clinical diagnosis in traditional Chinese medicine, which is of great significance for the standardization and modernization of traditional Chinese medicine pulse conditions.

  Key words: Pulse condition; Nonlinear time - series analysis; Modernization of traditional Chinese medicine; Feature extraction; Model construction

  Submit time: 23 March 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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• ID	Submit time	Number	Download
  1	2026-03-18	10.12201/bmr.202603.00079V1	Download

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