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Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy
Bei Liu1, Wenbin Tan1, Xian Zhang2
1College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China.
Mathematical Biosciences and Engineering : MBE
|December 14, 2021
Summary
A new method, multi-scale rescaled range permutation entropy (MRRPE), improves the recognition of denatured biological tissue during high intensity focused ultrasound (HIFU) treatment. MRRPE enhances accuracy by incorporating amplitude information, outperforming traditional methods.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Medical Physics
Background:
- Accurate recognition of denatured biological tissue is crucial for effective high intensity focused ultrasound (HIFU) treatment.
- Traditional multi-scale permutation entropy (MPE) methods for tissue recognition overlook signal amplitude information, potentially limiting accuracy.
- Existing methods like MPE and multi-scale weighted permutation entropy (MWPE) have limitations in capturing complex signal characteristics.
Purpose of the Study:
- To propose a novel method, multi-scale rescaled range permutation entropy (MRRPE), for enhanced recognition of denatured biological tissue.
- To address the limitations of traditional MPE by incorporating amplitude information and extreme volatility characteristics.
- To evaluate the performance of MRRPE in identifying denatured tissues during HIFU treatment.
Main Methods:
- Development of the multi-scale rescaled range permutation entropy (MRRPE) algorithm.
- Application of MRRPE to analyze high intensity focused ultrasound (HIFU) echo signals.
- Utilizing a support vector machine (SVM) classifier for tissue recognition based on MRRPE features.
- Comparative analysis against traditional multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE).
Main Results:
- The proposed MRRPE method effectively incorporates signal amplitude information and captures extreme volatility.
- MRRPE-based analysis demonstrated a higher recognition rate for denatured biological tissue compared to MPE and MWPE.
- The highest recognition accuracy achieved was 96.57%, indicating superior performance in distinguishing between non-denatured and denatured tissues.
Conclusions:
- MRRPE offers a more effective approach for recognizing denatured biological tissue in HIFU applications.
- The method's ability to utilize amplitude and volatility information leads to improved diagnostic accuracy.
- MRRPE shows significant potential for enhancing the precision and efficacy of HIFU treatments.
