{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:42:18Z","timestamp":1760240538203,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11774088,11474090,61502164"],"award-info":[{"award-number":["11774088,11474090,61502164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e21070666","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment"],"prefix":"10.3390","volume":"21","author":[{"given":"Bei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Physics and Electronics, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyou","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weipeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Physics and Electronics, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ultsonch.2015.05.035","article-title":"A review of high intensity focused ultrasound in relation to the treatment of renal tumours and other malignancies","volume":"27","author":"Cranston","year":"2015","journal-title":"Ultrason. Son."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4896","DOI":"10.1118\/1.4927060","article-title":"Frequency considerations for deep ablation with high-intensity focused ultrasound: A simulation study","volume":"42","author":"Ellens","year":"2015","journal-title":"Med. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1134\/1.1591291","article-title":"Physical mechanisms of the therapeutic effect of ultrasound (a review)","volume":"49","author":"Bailey","year":"2003","journal-title":"Acoust. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.7863\/jum.2010.29.12.1787","article-title":"Correlations between B-mode ultrasonic image texture features and tissue temperature in microwave ablation","volume":"29","author":"Yang","year":"2010","journal-title":"J. Ultrasound Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1080\/02656730600661149","article-title":"Thermal monitoring: Invasive minimal-invasive and non-invasive approaches","volume":"22","author":"Wust","year":"2006","journal-title":"Int. J. Hyperth."},{"key":"ref_6","unstructured":"Goldhaber, D.M., Deli, M., Mineyev, M.I., and Gronemeyer, D.H.W. (November, January 31). Measurement of tissue temperature by MRI. Proceedings of the 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, USA."},{"key":"ref_7","first-page":"35","article-title":"Nonalcoholic fatty liver disease: Diagnosis and management","volume":"88","author":"Wilkins","year":"2013","journal-title":"Am. Fam. Phys."},{"key":"ref_8","unstructured":"Seip, R., Tavakkoli, J., Carlson, R.F., and Wunderlich, A. (2002, January 8\u201311). High-intensity focused ultrasound(HIFU) multiple lesion imagine: Comparison of detection algorithms for real-time treatment control. Proceedings of the 2002 IEEE Ultrasonics Symposium, Munich, Germany."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1121\/1.419737","article-title":"Dependence of ultrasonic attenuation and absorption in dog soft tissue on temperature and thermal dose","volume":"102","author":"Damianou","year":"1997","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2050-5736-1-14","article-title":"Estimating dynamic changes of tissue attenuation coefficient during high- intensity focused ultrasound treatment","volume":"1","author":"Rahimian","year":"2013","journal-title":"J. Ther. Ultrasound"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ghoshal, G., and Oelze, M.L. (2009, January 20\u201323). Use of quantitative ultrasound to detect temperature variations in biological phantoms due to heating. Proceedings of the 2009 IEEE International Ultrasonics Symposium (IUS), Rome, Italy.","DOI":"10.1109\/ULTSYM.2009.5441535"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/10.398644","article-title":"Noninvasive estimation of tissue temperature response to heating fields using diagnostic ultrasound","volume":"42","author":"Seip","year":"1995","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/0301-5629(94)90051-5","article-title":"Theoretical estimation of the temperature dependence of backscattered ultrasonic power for noninvasive thermometry","volume":"20","author":"Straube","year":"1994","journal-title":"Ultrasound Med. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, N., Chen, H., Zhang, S., Cai, G., Li, W., Xu, D., and Fang, L. (2016). Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine. Entropy, 18.","DOI":"10.3390\/e18090322"},{"key":"ref_15","first-page":"66","article-title":"Reservoir Distribution Detection based on Time-Frequency Entropy","volume":"32","author":"Cai","year":"2010","journal-title":"J. Oil Gas Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TASSP.1977.1162950","article-title":"Short term spectral analysis, synthesis, and modification by discrete Fourier transform","volume":"25","author":"Allen","year":"2003","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Daubechies, I. (1992). Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9781611970104"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/78.492555","article-title":"Localization of the Complex Spectrum: The S Transform","volume":"44","author":"Stockwell","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","first-page":"2478","article-title":"Examples of wavelet transform time-frequency analysis in direct hydrocarbon detection","volume":"21","author":"Sun","year":"2002","journal-title":"Seg. Tech. Program Expand. Abstr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1109\/TSP.2013.2265222","article-title":"Empirical Wavelet Transform","volume":"61","author":"Gilles","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MSP.2013.2267931","article-title":"Empirical Mode Decomposition-Based Time- Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis. Signal Process","volume":"30","author":"Mandic","year":"2013","journal-title":"Mag. IEEE"},{"key":"ref_22","first-page":"759","article-title":"Generalized S Transform and Seismic Response Analysis of Thin Interbedss Surrounding Regions by Gps. Chin","volume":"46","author":"Gao","year":"2003","journal-title":"J. Geophys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"91","DOI":"10.4103\/2228-7477.181032","article-title":"Radio Frequency Ultrasound Time Series Signal Analysis to Evaluate High-intensity Focused Ultrasound Lesion Formation Status in Tissue","volume":"6","author":"Mobasheri","year":"2016","journal-title":"J. Med. Signals Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tsui, P.-H., and Wan, Y.-L. (2016). Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals. Entropy, 18.","DOI":"10.3390\/e18090341"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.infbeh.2010.04.007","article-title":"Infant\u2019s emotional variability associated to interactive stressful situation: A novel analysis approach with Sample Entropy and Lempel\u2013Ziv Complexity","volume":"33","author":"Montirosso","year":"2010","journal-title":"Infant Behav. Dev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tao, M., Poskuviene, K., Alkayem, N., Cao, M., and Ragulskis, M. (2018). Permutation entropy Based on Non-Uniform Embedding. Entropy, 20.","DOI":"10.3390\/e20080612"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.3390\/e14081343","article-title":"Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine","volume":"14","author":"Wu","year":"2012","journal-title":"Entropy"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gao, Y., Villecco, F., Li, M., and Song, W. (2017). Multi-Scale permutation entropy based on improved LMD and HMM for rolling bearing diagnosis. Entropy, 19.","DOI":"10.3390\/e19040176"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"028702","DOI":"10.7498\/aps.68.20181772","article-title":"Recognition of denatured biological tissue based on variational mode decomposition and multi-scale permutation entropy","volume":"68","author":"Liu","year":"2019","journal-title":"Acta. Phys. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"022911","DOI":"10.1103\/PhysRevE.87.022911","article-title":"Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information","volume":"87","author":"Fadlallah","year":"2013","journal-title":"Phys. Rev. E"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.3390\/e15031069","article-title":"Time Series Analysis Using Composite Multiscale","volume":"15","author":"Wu","year":"2013","journal-title":"Entropy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.ymssp.2016.09.010","article-title":"Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines","volume":"85","author":"Zheng","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.physleta.2014.03.034","article-title":"Analysis of complex time series using refined composite multiscale entropy","volume":"378","author":"Wu","year":"2014","journal-title":"Phys. Lett. A"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/7\/666\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:38Z","timestamp":1760187818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/7\/666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["e21070666"],"URL":"https:\/\/doi.org\/10.3390\/e21070666","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2019,7,8]]}}}