{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T01:23:18Z","timestamp":1774488198061,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007402"],"award-info":[{"award-number":["No.2020YFC2007402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007401"],"award-info":[{"award-number":["No.2020YFC2007401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007404"],"award-info":[{"award-number":["No.2020YFC2007404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007403"],"award-info":[{"award-number":["No.2020YFC2007403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007405"],"award-info":[{"award-number":["No.2020YFC2007405"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No.2020YFC2007400"],"award-info":[{"award-number":["No.2020YFC2007400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Basic Research Program of Suzhou","award":["SJC2022011"],"award-info":[{"award-number":["SJC2022011"]}]},{"name":"Special project of basic research on frontier leading technology in Jiangsu Province","award":["BK20192004C"],"award-info":[{"award-number":["BK20192004C"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13634-023-01066-3","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T04:02:06Z","timestamp":1698206526000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["An improved wavelet threshold denoising approach for surface electromyography signal"],"prefix":"10.1186","volume":"2023","author":[{"given":"Chuanyun","family":"Ouyang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2952-0109","authenticated-orcid":false,"given":"Liming","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tianxiang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"1066_CR1","first-page":"1","volume":"2023","author":"D Zeping","year":"2023","unstructured":"D. Zeping, Q. Dawei, L. Jing, Surface muscle advances in electric human lower limb action recognition and prediction. Comput. Eng. Appl. 2023, 1\u201315 (2023)","journal-title":"Comput. Eng. Appl."},{"key":"1066_CR2","doi-asserted-by":"publisher","unstructured":"H. Haiyan, Semg-based lower limb knee angle prediction method. Thesis, Shanghai Normal University (2022). https:\/\/doi.org\/10.27312\/d.cnki.gshsu.2022.001951","DOI":"10.27312\/d.cnki.gshsu.2022.001951"},{"key":"1066_CR3","doi-asserted-by":"publisher","unstructured":"F. Wenyuan, Research and design of semg gesture recognition system for artificial intelligence education. Thesis, Shanghai Normal University (2021). https:\/\/doi.org\/10.27312\/d.cnki.gshsu.2021.000827","DOI":"10.27312\/d.cnki.gshsu.2021.000827"},{"issue":"4","key":"1066_CR4","doi-asserted-by":"publisher","first-page":"711","DOI":"10.3233\/xst-17301","volume":"25","author":"W Wei","year":"2017","unstructured":"W. Wei, J. Hong, C. Wang, L. Wang, De-noising surface electromyograms using an adaptive wavelet approach. J. Xray Sci. Technol. 25(4), 711\u2013720 (2017). https:\/\/doi.org\/10.3233\/xst-17301","journal-title":"J. Xray Sci. Technol."},{"issue":"05","key":"1066_CR5","first-page":"8","volume":"11","author":"T Wang","year":"2022","unstructured":"T. Wang, F. Yang, J. Yang, Experimental analysis of the effect of window length on blind source separation algorithms in the time-frequency domain. Netw. New Media Technol. 11(05), 8\u201314 (2022)","journal-title":"Netw. New Media Technol."},{"issue":"3","key":"1066_CR6","first-page":"133","volume":"24","author":"C Liyu","year":"2000","unstructured":"C. Liyu, W. Zhizhong, Z. Haihong, A surface emg signal identification method based on short-time Fourier transform. Chin. J. Med. Instrum. 24(3), 133\u2013136 (2000)","journal-title":"Chin. J. Med. Instrum."},{"issue":"1","key":"1066_CR7","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.jmaa.2012.09.053","volume":"399","author":"H Giv","year":"2013","unstructured":"H. Giv, Directional short-time Fourier transform. J. Math. Anal. Appl. 399(1), 100\u2013107 (2013). https:\/\/doi.org\/10.1016\/j.jmaa.2012.09.053","journal-title":"J. Math. Anal. Appl."},{"issue":"7","key":"1066_CR8","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1080\/02664763.2014.1001728","volume":"42","author":"K Veer","year":"2015","unstructured":"K. Veer, R. Agarwal, Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals. J. Appl. Stat. 42(7), 1591\u20131601 (2015)","journal-title":"J. Appl. Stat."},{"key":"1066_CR9","first-page":"123","volume-title":"Wavelet Analysis of Biomedical Signals","author":"HD Wu Shuicai","year":"2014","unstructured":"H.D. Wu Shuicai, W. Yijie, Medical signal processing and application, in Wavelet Analysis of Biomedical Signals, vol. 1, ed. by W. Shuicai (Beijing University of Technology Press, Beijing, 2014), pp.123\u2013129"},{"key":"1066_CR10","first-page":"494","volume-title":"Discrete Wavelet Transform","author":"KL Jun","year":"2014","unstructured":"K.L. Jun, Matlab wavelet analysis super learning handbook, in Discrete Wavelet Transform, vol. 1, ed. by E. Zaimis (People\u2019s Posts and Telecommunications Press, Beijing, 2014), pp.494\u2013523"},{"issue":"6","key":"1066_CR11","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3109\/03091902.2012.684830","volume":"36","author":"F Meziani","year":"2012","unstructured":"F. Meziani, S.M. Debbal, A. Atbi, Analysis of phonocardiogram signals using wavelet transform. J. Med. Eng. Technol. 36(6), 126\u2013133 (2012)","journal-title":"J. Med. Eng. Technol."},{"issue":"06","key":"1066_CR12","first-page":"348","volume":"38","author":"L Weisong","year":"2021","unstructured":"L. Weisong, X. Weijie, Z. Tao, Improvement of threshold denoising algorithm based on wavelet transform. Comput. Simul. 38(06), 348\u2013351356 (2021)","journal-title":"Comput. Simul."},{"issue":"1743","key":"1066_CR13","first-page":"459","volume":"370","author":"MV Berry","year":"1980","unstructured":"M.V. Berry, Z.V. Lewis, On the Weierstrass-Mandelbrot fractal function. Proc. R. Soc. A 370(1743), 459\u2013484 (1980)","journal-title":"Proc. R. Soc. A"},{"key":"1066_CR14","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/978-3-319-42105-6_16","volume-title":"Engineering Mathematics II","author":"E Guariglia","year":"2016","unstructured":"E. Guariglia, S. Silvestrov, Fractional-wavelet analysis of positive definite distributions and wavelets on d\u2032(c), in Engineering Mathematics II. ed. by S. Silvestrov, M. Rani (Springer, Heidelberg, 2016), pp.337\u2013353"},{"key":"1066_CR15","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5542054","author":"E Guariglia","year":"2022","unstructured":"E. Guariglia, R.C. Guido, Chebyshev wavelet analysis. J. Funct. Spaces (2022). https:\/\/doi.org\/10.1155\/2022\/5542054","journal-title":"J. Funct. Spaces"},{"key":"1066_CR16","doi-asserted-by":"publisher","DOI":"10.1142\/s0219691319500504","author":"L Yang","year":"2019","unstructured":"L. Yang, H.L. Su, C. Zhong, Z.Q. Meng, H.W. Luo, X.C. Li, Y.Y. Tang, Y. Lu, Hyperspectral image classification using wavelet transform-based smooth ordering. Int. J. Wavelets Multiresolut. Inf. Process. (2019). https:\/\/doi.org\/10.1142\/s0219691319500504","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"issue":"7","key":"1066_CR17","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/tsp.2019.2896246","volume":"67","author":"XW Zheng","year":"2019","unstructured":"X.W. Zheng, Y.Y. Tang, J.T. Zhou, A framework of adaptive multiscale wavelet decomposition for signals on undirected graphs. IEEE Trans. Signal Process. 67(7), 1696\u20131711 (2019). https:\/\/doi.org\/10.1109\/tsp.2019.2896246","journal-title":"IEEE Trans. Signal Process."},{"key":"1066_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/e21030304","author":"E Guariglia","year":"2019","unstructured":"E. Guariglia, Primality, fractality, and image analysis. Entropy (2019). https:\/\/doi.org\/10.3390\/e21030304","journal-title":"Entropy"},{"key":"1066_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/e20090714","author":"E Guariglia","year":"2018","unstructured":"E. Guariglia, Harmonic sierpinski gasket and applications. Entropy (2018). https:\/\/doi.org\/10.3390\/e20090714","journal-title":"Entropy"},{"key":"1066_CR20","doi-asserted-by":"publisher","first-page":"3862","DOI":"10.1109\/access.2016.2587581","volume":"4","author":"M Srivastava","year":"2016","unstructured":"M. Srivastava, C. Anderson, J. Freed, A new wavelet denoising method for selecting decomposition levels and noise thresholds. IEEE Access 4, 3862\u20133877 (2016). https:\/\/doi.org\/10.1109\/access.2016.2587581","journal-title":"IEEE Access"},{"key":"1066_CR21","doi-asserted-by":"publisher","DOI":"10.1142\/s0219691320300017","author":"RC Guido","year":"2020","unstructured":"R.C. Guido, F. Pedroso, A. Furlan, R.C. Contreras, L.G. Caobianco, J.S. Neto, Cwt x dwt x dtwt x sdtwt: Clarifying terminologies and roles of different types of wavelet transforms. Int. J. Wavelets Multiresolut. Inf. Process. (2020). https:\/\/doi.org\/10.1142\/s0219691320300017","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"1066_CR22","unstructured":"D. Yujuan, Based on wavelet transform speech threshold denoising algorithm research. Thesis, Chongqing University (2009)"},{"issue":"7","key":"1066_CR23","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1016\/j.aml.2011.02.018","volume":"24","author":"RC Guido","year":"2011","unstructured":"R.C. Guido, A note on a practical relationship between filter coefficients and scaling and wavelet functions of discrete wavelet transforms. Appl. Math. Lett. 24(7), 1257\u20131259 (2011). https:\/\/doi.org\/10.1016\/j.aml.2011.02.018","journal-title":"Appl. Math. Lett."},{"key":"1066_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2022.08.001","volume":"985","author":"RC Guido","year":"2022","unstructured":"R.C. Guido, Wavelets behind the scenes: practical aspects, insights, and perspectives. Phys. Rep. Rev. Sect. Phys. Lett. 985, 1\u201323 (2022). https:\/\/doi.org\/10.1016\/j.physrep.2022.08.001","journal-title":"Phys. Rep. Rev. Sect. Phys. Lett."},{"issue":"3","key":"1066_CR25","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/msp.2017.2672759","volume":"34","author":"RC Guido","year":"2017","unstructured":"R.C. Guido, Effectively interpreting discrete wavelet transformed signals. IEEE Signal Process. Mag. 34(3), 89 (2017). https:\/\/doi.org\/10.1109\/msp.2017.2672759","journal-title":"IEEE Signal Process. Mag."},{"key":"1066_CR26","first-page":"1","volume":"2023","author":"J Tianyong","year":"2023","unstructured":"J. Tianyong, Y. Chenyu, H. Ke, Z. Jie, W. Lei, Optimization of vmd parameter joint based on ao algorithm bridge signal denoising method based on wavelet thresholding. J. China Highway Soc. 2023, 1\u201319 (2023)","journal-title":"J. China Highway Soc."},{"issue":"02","key":"1066_CR27","first-page":"212","volume":"40","author":"G Xuan","year":"2023","unstructured":"G. Xuan, Z. Wei, L. Shanshan, L. Fu\u2019e, L. Donghua, Research on ecg emg signal denoising based on improved wavelet thresholding algorithm. Chin. J. Med. Phys. 40(02), 212\u2013219 (2023)","journal-title":"Chin. J. Med. Phys."},{"key":"1066_CR28","doi-asserted-by":"publisher","unstructured":"H. Wenwen, Research on physiological signal analysis and processing algorithms for wearable devices. Thesis, University of Electronic Science and Technology of China (2021). https:\/\/doi.org\/10.27005\/d.cnki.gdzku.2021.005082","DOI":"10.27005\/d.cnki.gdzku.2021.005082"},{"key":"1066_CR29","doi-asserted-by":"publisher","unstructured":"D. Valencia, D. Orejuela, J. Salazar, J. Valencia, Comparison analysis between rigrsure, sqtwolog, heursure and minimaxi techniques using hard and soft thresholding methods, in 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), pp. 5\u20135 (2016). https:\/\/doi.org\/10.1109\/stsiva.2016.7743309","DOI":"10.1109\/stsiva.2016.7743309"},{"issue":"6","key":"1066_CR30","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1007\/s00034-017-0665-8","volume":"37","author":"MA Hassanein","year":"2018","unstructured":"M.A. Hassanein, M.T. Hanna, N.P.A. Seif, M.T.M.M. Elbarawy, Signal denoising using optimized trimmed thresholding. Circuits Syst. Signal Process. 37(6), 2413\u20132432 (2018)","journal-title":"Circuits Syst. Signal Process."},{"key":"1066_CR31","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3233\/THC-236049","volume":"31","author":"Z Jun","year":"2023","unstructured":"Z. Jun, G. Xingguang, Z. Yitao, Y. Fei, W. Yunfeng, Z. Haiying, Application of translation wavelet transform with new threshold function in pulse wave signal denoising. Technol. Health Care 31, 551\u2013563 (2023)","journal-title":"Technol. Health Care"},{"key":"1066_CR32","first-page":"494","volume-title":"Wavelet Time-Frequency Characteristics and Applications","author":"R Qiwen","year":"2016","unstructured":"R. Qiwen, Theory and application of wavelet transform and fractional Fourier transform, in Wavelet Time-Frequency Characteristics and Applications. ed. by R. Qiwen (Harbin Institute of Technology Press, Harbin, 2016), pp.494\u2013523"},{"issue":"4","key":"1066_CR33","doi-asserted-by":"publisher","first-page":"373","DOI":"10.2307\/1269730","volume":"37","author":"L Breiman","year":"1995","unstructured":"L. Breiman, Better subset regression using the nonnegative garrote. Technometrics 37(4), 373\u2013384 (1995). https:\/\/doi.org\/10.2307\/1269730","journal-title":"Technometrics"},{"issue":"04","key":"1066_CR34","first-page":"1","volume":"2","author":"L Chun","year":"2018","unstructured":"L. Chun, A. Yuan, L. Xin, Research on the improvement of denoising based on garrote threshold method. Mod. Inf. Technol. 2(04), 1\u20135 (2018)","journal-title":"Mod. Inf. Technol."},{"key":"1066_CR35","doi-asserted-by":"crossref","unstructured":"B. Zou, H. Liu, Z. Shang, R. Li, Proceedings of 2015 IEEE 6th international conference on software engineering and service science. Image Denoising Based On Wavelet Transform, pp. 366\u2013368 (2015)","DOI":"10.1109\/ICSESS.2015.7339070"},{"issue":"14","key":"1066_CR36","doi-asserted-by":"publisher","first-page":"219","DOI":"10.19595\/j.cnki.1000-6753.tces.2016.14.025","volume":"31","author":"F Xiaolong","year":"2016","unstructured":"F. Xiaolong, X. Weicheng, J. Wenbo, L. Yi, H. Xiaoli, A kind of stationary wavelet transform power quality disturbance signal denoising method with improved threshold function. Acta Electrotech. 31(14), 219\u2013226 (2016). https:\/\/doi.org\/10.19595\/j.cnki.1000-6753.tces.2016.14.025","journal-title":"Acta Electrotech."},{"issue":"3","key":"1066_CR37","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3233\/xst-17324","volume":"26","author":"XL Cui","year":"2018","unstructured":"X.L. Cui, L. Mili, G. Wang, H.Y. Yu, Wavelet-based joint ct-mri reconstruction. J. Xray Sci. Technol. 26(3), 379\u2013393 (2018). https:\/\/doi.org\/10.3233\/xst-17324","journal-title":"J. Xray Sci. Technol."},{"issue":"1","key":"1066_CR38","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/msp.2018.2874549","volume":"36","author":"RC Guido","year":"2019","unstructured":"R.C. Guido, Paraconsistent feature engineering. IEEE Signal Process. Mag. 36(1), 154\u2013158 (2019). https:\/\/doi.org\/10.1109\/msp.2018.2874549","journal-title":"IEEE Signal Process. Mag."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-023-01066-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T20:25:10Z","timestamp":1700598310000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-023-01066-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,25]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1066"],"URL":"https:\/\/doi.org\/10.1186\/s13634-023-01066-3","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,25]]},"assertion":[{"value":"21 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing financial interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"108"}}