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Improved neural networks based on genetic algorithm for pulse recognition.

Zhihua Chen1, An Huang2, Xiaoli Qiang1

  • 1Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, Guangdong, China.

Computational Biology and Chemistry
|July 5, 2020
PubMed
Summary
This summary is machine-generated.

Objective pulse identification in Chinese medicine was improved using artificial intelligence. A genetic algorithm enhanced a backpropagation neural network, achieving 95% accuracy in classifying four pulse types, enhancing diagnostic reliability.

Keywords:
BP neural networkFeature extractionGenetic algorithmPulse recognition

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Area of Science:

  • Integrative and Complementary Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Traditional Chinese Medicine (TCM) pulse diagnosis lacks objective standards, impacting accuracy.
  • Existing subjective methods limit the feasibility and reliability of pulse identification.
  • Developing objective methods is crucial for advancing TCM diagnostic science.

Purpose of the Study:

  • To develop an objective method for pulse diagnosis using signal processing and machine learning.
  • To enhance the accuracy and feasibility of identifying specific pulse types (normal, wiry, smooth, thready).
  • To compare the performance of different algorithms in pulse classification.

Main Methods:

  • Extraction of four frequency-domain characteristics from pulse waves.
  • Extraction of six wavelet scale energy characteristics.
  • Development of a three-layer backpropagation (BP) neural network.
  • Improvement of the BP neural network using the Levenberg-Marquard (LM) algorithm and a genetic algorithm (GA).

Main Results:

  • Classification accuracy of 90% was achieved with the LM-improved BP network.
  • Classification accuracy of 95% was achieved with the GA-improved BP network.
  • The GA-improved BP network demonstrated superior convergence speed and a lower error rate.

Conclusions:

  • An objective, AI-driven approach significantly improves pulse diagnosis accuracy in TCM.
  • Genetic algorithm-enhanced neural networks offer a promising tool for reliable pulse identification.
  • This research paves the way for more standardized and accurate TCM diagnostics.