Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy

Bei Liu; Wenbin Tan; Xian Zhang; Ziqi Peng; Jing Cao; Bei Liu; Wenbin Tan; Xian Zhang; Ziqi Peng; Jing Cao

doi:10.3934/mbe.2022005

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 1: 102-114. doi: 10.3934/mbe.2022005

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Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy

1.
College of Mathematics and Physics, Hunan University of Arts and Science, Changde 415000, China
2.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Academic Editor: Xiangtao Li

Received: 31 July 2021 Accepted: 27 October 2021 Published: 08 November 2021

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will affect the recognition effect of denatured tissues. In order to solve the above problems, the method of multi-scale rescaled range permutation entropy (MRRPE) is proposed in this paper. The simulation results show that the MRRPE not only includes the amplitude information of the signal when calculating the signal complexity, but also extracts the extreme volatility characteristics of the signal effectively. The proposed method is applied to the HIFU echo signals during HIFU treatment, and the support vector machine (SVM) is used for recognition. The results show that compared with MPE and the multi-scale weighted permutation entropy (MWPE), the recognition rate of denatured biological tissue based on the MRRPE is higher, up to 96.57%, which can better recognize the non-denatured biological tissues and the denatured biological tissues.
- HIFU,
- multi-scale rescaled range permutation entropy,
- biological tissue,
- denatured recognition
Citation: Bei Liu, Wenbin Tan, Xian Zhang, Ziqi Peng, Jing Cao. Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 102-114. doi: 10.3934/mbe.2022005

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Abstract

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will affect the recognition effect of denatured tissues. In order to solve the above problems, the method of multi-scale rescaled range permutation entropy (MRRPE) is proposed in this paper. The simulation results show that the MRRPE not only includes the amplitude information of the signal when calculating the signal complexity, but also extracts the extreme volatility characteristics of the signal effectively. The proposed method is applied to the HIFU echo signals during HIFU treatment, and the support vector machine (SVM) is used for recognition. The results show that compared with MPE and the multi-scale weighted permutation entropy (MWPE), the recognition rate of denatured biological tissue based on the MRRPE is higher, up to 96.57%, which can better recognize the non-denatured biological tissues and the denatured biological tissues.

References

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