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[Study on several methods of character analysis and classification for pulse-condition based on statistics theory ]
1Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China. zhouyue@sjtu.edu.cn
Summary
This study explores statistical methods for pulse condition analysis, using wavelet analysis, AR modeling, and template matching for feature extraction. It evaluates five classification techniques for improved diagnostic accuracy in pulse diagnosis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Medical Informatics
Context:
- Pulse condition analysis is crucial for traditional medicine diagnostics.
- Current methods may lack robust statistical underpinnings for feature extraction and classification.
- Integrating advanced signal processing offers potential for objective pulse assessment.
Purpose:
- To investigate and compare statistical methods for pulse condition character analysis.
- To apply diverse abstraction techniques (wavelet, AR, template matching) for feature extraction.
- To evaluate the efficacy of multiple classification algorithms (BP, RBF, CGA, FMMNN) for pulse condition categorization.
Summary:
- This research applies statistical theory to analyze pulse conditions across time, frequency, and wavelet domains.
- Feature extraction methods include wavelet analysis, Autoregressive (AR) modeling, and template matching.
- Classification performance is assessed using Backpropagation (BP), Radial Basis Function (RBF), Complex Genetic Algorithm (CGA), and Fuzzy Mạnh-Mờ Neural Network (FMMNN).
Impact:
- Provides a quantitative framework for pulse condition analysis, enhancing diagnostic objectivity.
- Offers insights into the comparative performance of different signal processing and machine learning techniques for pulse data.
- Lays the groundwork for developing more accurate and reliable automated pulse diagnostic systems.
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