scholarly journals Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 666 ◽  
Author(s):  
Bei Liu ◽  
Shengyou Qian ◽  
Weipeng Hu
Keyword(s):  
Biological Tissues ◽  
High Intensity Focused Ultrasound ◽  
Permutation Entropy ◽  
Hifu Treatment ◽  
Echo Signal ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Normal Tissues ◽  
Multi Scale ◽  
Ultrasonic Echo

Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment.

scholarly journals Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 944
Author(s):  
Bei Liu ◽  
Runmin Wang ◽  
Ziqi Peng ◽  
Lingjie Qin
Keyword(s):  
Compressed Sensing ◽  
Biological Tissue ◽  
Focused Ultrasound ◽  
Matching Pursuit ◽  
Biological Tissues ◽  
High Intensity Focused Ultrasound ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Hifu Treatment ◽  
Normal Tissues

Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate the complexity of ultrasonic scattered echo signals during HIFU treatment. In the analysis of CS, the method of orthogonal matching pursuit (OMP) is employed to reconstruct the denoised signal. CS-OMP can denoise the ultrasonic scattered echo signal effectively. Comparing with traditional multiscale dispersion entropy (MDE), IMDE improves the coarse-grained process in the multiscale analysis, which improves the stability of MDE. In the analysis of simulated signals, the entropy value of the IMDE method has less fluctuation compared with MDE, indicating that the IMDE method has better stability. In addition, MDE and IMDE are applied to the 300 cases of ultrasonic scattered echo signals after denoising (including 150 cases of normal tissues and 150 cases of denatured tissues). The experimental results show that the MDE and IMDE values of denatured tissues are higher than normal tissues. Both the MDE and IMDE method can be used to identify whether biological tissue is denatured. However, the multiscale entropy curve of IMDE is smoother and more stable than MDE. The interclass distance of IMDE is greater than MDE, and the intraclass distance of IMDE is less than MDE at different scale factors. This indicates that IMDE can better distinguish normal tissues and denatured tissues to obtain more accurate clinical diagnosis during HIFU treatment.

scholarly journals Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 404
Author(s):  
Bei Liu ◽  
Xian Zhang ◽  
Xiao Zou ◽  
Jing Cao ◽  
Ziqi Peng
Keyword(s):  
Tissue Damage ◽  
Biological Tissue ◽  
Biological Tissues ◽  
Coarse Grained ◽  
Permutation Entropy ◽  
Hifu Treatment ◽  
Monitoring Method ◽  
Multi Scale ◽  
Damage Monitoring ◽  
The Stability

Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment.

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

2021 ◽  
Vol 19 (1) ◽  
pp. 102-114
Author(s):  
Bei Liu ◽  
◽  
Wenbin Tan ◽  
Xian Zhang ◽  
Ziqi Peng ◽  
...  
Keyword(s):  
Biological Tissue ◽  
Focused Ultrasound ◽  
Recognition Rate ◽  
Biological Tissues ◽  
High Intensity Focused Ultrasound ◽  
Permutation Entropy ◽  
Support Vector ◽  
Nonlinear Method ◽  
Multi Scale ◽  
Rescaled Range

<abstract> <p>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.</p> </abstract>

scholarly journals Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 269 ◽  
Author(s):  
Wei Zhang ◽  
Zhipeng Li ◽  
Xuyang Gao ◽  
Yanjun Li ◽  
Yibing Shi
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Arrival Time ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Information Criteria ◽  
Echo Signal ◽  
Time Frequency ◽  
Ultrasonic Echo ◽  
Ultrasonic Anemometer

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach

2013 ◽  
Vol 321-324 ◽  
pp. 1311-1316 ◽  
Author(s):  
Jian Ming Yu ◽  
Ze Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Thin Plates ◽  
Echo Signal ◽  
Stationary Characteristic ◽  
Bonding Quality ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Echo

The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate its energy torque. The HHT method highlights the time-frequency performance of echo signals effectively. The simulated signals verify that EEMD has more excellent decomposition performance than EMD, that is, EEMD diminishes the mode mixing to some extent generated from EMD decomposition.

scholarly journals An Effective Feature Extraction Method of Quantitative Recognition of Ultrasonic Echo for Bonding Quality of Composite Sheet

CONVERTER ◽  
2021 ◽  
pp. 25-40
Author(s):  
Dong Li, Bateer, Bingqing Gao
Keyword(s):  
Feature Extraction ◽  
Thin Plate ◽  
Extraction Method ◽  
Echo Signal ◽  
Bonding Quality ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Signal Parameters ◽  
Ultrasonic Echo ◽  
Quantitative Identification

In this paper, a set of effective ultrasonic echo feature extraction method is proposed for the quantitative identification of thin plate composite bonding quality, which opens up a new research idea for the research of ultrasonic echo feature extraction for quantitative identification of thin plate bonding quality, and also lays a foundation for further quantitative identification of bonding structure debonding degree of thin plate. In addition to the time and frequency domain analysis, the empirical mode time-frequency analysis method is used to highlight the time-frequency characteristics of the echo signal, improve the resolution of the signal analysis, and expand the space for the echo signal feature extraction. The feature validity evaluation theory of fault diagnosis is introduced to evaluate the feature validity of high-dimensional signal parameters, and the invalid signal parameters are eliminated to obtain an effective echo signal feature set with low dimension. Using the exhaustive search strategy of feature selection theory, geometric distance measurement and information measurement evaluation criteria, and through the classification verification of support vector machine classifier, the effective feature vector for quantitative identification of adhesive quality is obtained.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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