Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

Abstract

Recognition of biological tissue denaturation is a vital work in high-intensity focused ultrasound (HIFU) therapy. Multiscale permutation entropy (MPE) is a nonlinear signal processing method for feature extraction, widely applied to the recognition of biological tissue denaturation. However, the typical MPE cannot derive a stable entropy due to intensity information loss during the coarse-graining process. For this problem, an improved multiscale permutation entropy (IMPE) is proposed in this work. IMPE is obtained through refining and reconstructing MPE. Compared with MPE, the IMPE overcomes the deficiency of amplitude information loss due to the coarse-graining process when computing signal complexity. Through the simulation of calculating MPE and IMPE from white Gaussian noise, it is found that the entropy derived by IMPE is more stable than that derived by MPE. The processing method based on IMPE feature extraction is applied to the experimental ultrasonic scattered echo signals in HIFU treatment. Support vector machine and Gustafson–Kessel fuzzy clustering based on MPE and IMPE feature extraction are also used for biological tissue denaturation classification and recognition. The results calculated from the different combination algorithms show that the recognition of biological tissue denaturation based on IMPE-GK clustering is more reliable with the accuracy of 95.5%.

Distribution Network Fault-Line Selection Method Based on MICEEMDAN–Recurrence Plot–Yolov5

“…The permutation entropy can only detect the complexity and randomness of the time series on a single scale. Moreover, the output time series of complex systems contain characteristic information on multiple scales when the permutation entropy analysis is no longer satisfied [23]. In order to study the multi-scale complexity variation of a time series, multi-scale permutation entropy is proposed.…”

“…(3) The feature map f is convolved with a convolution kernel of 1 × 1 according to the original height and width to obtain the feature maps F h and F w , with the same number of channels as the original one. The attention weights g h for the feature maps in the height and width and g w in the width direction are obtained after the Sigmoid activation function, as shown in Equation (23).…”