{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:37:25Z","timestamp":1781840245690,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017668","name":"Anhui Provincial Key Research and Development Plan","doi-asserted-by":"publisher","award":["202004a07020037"],"award-info":[{"award-number":["202004a07020037"]}],"id":[{"id":"10.13039\/501100017668","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017668","name":"Anhui Provincial Key Research and Development Plan","doi-asserted-by":"publisher","award":["202204295107020047"],"award-info":[{"award-number":["202204295107020047"]}],"id":[{"id":"10.13039\/501100017668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53\u201324.57 and 18.57\u201326.92. Both the quantitative index and the time\u2013frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters.<\/jats:p>","DOI":"10.3390\/s23115182","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T02:33:27Z","timestamp":1685414007000},"page":"5182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform"],"prefix":"10.3390","volume":"23","author":[{"given":"Jingcheng","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoming","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"},{"name":"Chinese Academy of Sciences (Hefei) Institute of Technology Innovation, Hefei 230088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"},{"name":"School of Mathematics and Computer, Tongling University, Tongling 244061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/TIE.2015.2497212","article-title":"sEMG-Based Identification of Hand Motion Commands Using Wavelet Neural Network Combined with Discrete Wavelet Transform","volume":"63","author":"Duan","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"61378","DOI":"10.1109\/ACCESS.2019.2914728","article-title":"Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition","volume":"7","author":"Qi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s41378-019-0127-5","article-title":"An epidermal sEMG tattoo-like patch as a new human\u2013machine interface for patients with loss of voice","volume":"6","author":"Liu","year":"2020","journal-title":"Microsyst. Nanoeng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, J.C., Sun, Y.N., and Sun, S.M. (2021). Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering. Sensors, 21.","DOI":"10.3390\/s21030692"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Luo, X.Y., Wu, X.Y., Chen, L., Zhao, Y., Zhang, L., Li, G.L., and Hou, W.S. (2019). Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures. Sensors, 19.","DOI":"10.3390\/s19030610"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TNSRE.2018.2796070","article-title":"An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals with and Without Knee Pathology","volume":"26","author":"Naik","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3389\/fnhum.2017.00130","article-title":"Assessment of Upper Limb Motor Dysfunction for Children with Cerebral Palsy Based on Muscle Synergy Analysis","volume":"11","author":"Tang","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_8","first-page":"1044","article-title":"Evaluation of Non-invasive Neuromuscular Measurements and Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation","volume":"68","author":"Qiang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nardo, F.D., Strazza, A., Mengarelli, A., Cardarelli, S., Tigrini, A., Verdini, F., Nascimbeni, A., Agostini, V., Knaflitz, M., and Fioretti, S. (2019). EMG-Based Characterization of Walking Asymmetry in Children with Mild Hemiplegic Cerebral Palsy. Biosensors, 9.","DOI":"10.3390\/bios9030082"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TITB.2012.2226905","article-title":"A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals","volume":"17","author":"Cheng","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xi, X., Tang, M., Miran, S.M., and Luo, Z. (2017). Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors. Sensors, 17.","DOI":"10.3390\/s17061229"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2019.02.020","article-title":"An automatic and non-invasive physical fatigue assessment method for construction workers","volume":"103","author":"Yu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4356","DOI":"10.1038\/s41467-020-18210-4","article-title":"Plasticity of muscle synergies through fractionation and merging during development and training of human runners","volume":"11","author":"Cheung","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_14","unstructured":"Sperlich, B., Baumgart, C., Boccia, G., Vigotsky, A.D., Halperin, I., Lehman, G.J., Trajano, G.S., and Vieira, T.M. (Politecnico di Torino Repository Istituzionale Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences, 2018). Politecnico di Torino Repository Istituzionale Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences."},{"key":"ref_15","first-page":"54","article-title":"Muscle Coordination during Archery Shooting: A Comparison of Archers with Different Skill Levels","volume":"23","author":"Zhang","year":"2021","journal-title":"Eur. J. Sport Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Toledo-Perez, D.C., Martinez-Prado, M.A., Gomez-Loenzo, R.A., Paredes-Garcia, W.J., and Rodriguez-Resendiz, J. (2019). A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels. Electronics, 8.","DOI":"10.3390\/electronics8030259"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Toledo-Perez, D.C., Rodriguez-Resendiz, J., Gomez-Loenzo, R.A., and Jauregui-Correa, J.C. (2019). Support Vector Machine-Based EMG Signal Classification Techniques: A Review. Appl. Sci., 9.","DOI":"10.3390\/app9204402"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8783","DOI":"10.1109\/ACCESS.2020.2964678","article-title":"A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aviles, M., Sanchez-Reyes, L.M., Fuentes-Aguilar, R.Q., Toledo-Perez, D.C., and Rodriguez-Resendiz, J. (2022). A Novel Methodology for Classifying EMG Movements Based on SVM and Genetic Algorithms. Micromachines, 13.","DOI":"10.3390\/mi13122108"},{"key":"ref_20","unstructured":"Zhang, Q., and Luo, Z. (2006, January 25\u201328). Wavelet De-Noising of Electromyography. Proceedings of the IEEE International Conference on Mechatronics & Automation, Luoyang, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Limem, M., and Hamdi, M.A. (2015, January 16\u201318). Uterine electromyography signals denoising using discrete wavelet transform. Proceedings of the 2015 International Conference on Advances in Biomedical Engineering (ICABME), Beirut, Lebanon.","DOI":"10.1109\/ICABME.2015.7323261"},{"key":"ref_22","first-page":"857","article-title":"Hermite interpolation-based wavelet transform modulus maxima reconstruction algorithm\u2019s application to EMG de-noising","volume":"31","author":"Luo","year":"2009","journal-title":"Dianzi Yu Xinxi Xuebao\/J. Electron. Inf. Technol."},{"key":"ref_23","first-page":"1488","article-title":"De-noising method of the sEMG based On EEMD and second generation wavelet transform","volume":"25","author":"Xi","year":"2012","journal-title":"Chin. J. Sens. Actuators"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.medengphy.2012.10.009","article-title":"Filtering of surface EMG using ensemble empirical mode decomposition","volume":"35","author":"Zhang","year":"2013","journal-title":"Med. Eng. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.aeue.2016.12.008","article-title":"An Efficient Method for Analysis of EMG Signals using Improved Empirical Mode Decomposition","volume":"72","author":"Mishra","year":"2016","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_26","first-page":"64","article-title":"Methods of Power Line Interference Elimination in EMG Signal","volume":"40","author":"Ladrova","year":"2019","journal-title":"J. Biomim. Biomater. Biomed. Eng."},{"key":"ref_27","unstructured":"Mewett, D.T., Nazeran, H., and Reynolds, K.J. (2001, January 25\u201328). Removing power line noise from recorded EMG. Proceedings of the 2001 Conference Proceedings of the 23rd annual international conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Piskorowski, J. (2012, January 13\u201316). Powerline interference rejection from sEMG signal using notch filter with transient suppression. Proceedings of the 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Graz, Austria.","DOI":"10.1109\/I2MTC.2012.6229332"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/JSEN.2018.2874724","article-title":"An Adaptive Algorithm for Online Interference Cancellation in EMG Sensors","volume":"19","author":"Gokcesu","year":"2019","journal-title":"Sens. J. IEEE"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1007\/BF02350994","article-title":"Reducing power line interference in digitised electromyogram recordings by spectrum interpolation","volume":"42","author":"Mewett","year":"2004","journal-title":"Med. Biol. Eng. Comput. J. Int. Fed. Med. Biol. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Akbary, P., and Rabbani, H. (2010, January 24\u201328). Removing Power Line Interference and ECG signal from EMG signal using Matching Pursuit. Proceedings of the IEEE 10th International Conference on Signal Processing Proceedings, Beijing, China.","DOI":"10.1109\/ICOSP.2010.5656716"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"87769","DOI":"10.1109\/ACCESS.2019.2924962","article-title":"Enhanced Singular Spectrum Decomposition and Its Application to Rolling Bearing Fault Diagnosis","volume":"7","author":"Pang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112170","DOI":"10.1109\/ACCESS.2020.3002999","article-title":"Research on a Signal Separation Method Based on Vold-Kalman Filter of Improved Adaptive Instantaneous Frequency Estimation","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","first-page":"1","article-title":"High-Order Synchroextracting Time-Frequency Analysis and Its Application in Seismic Hydrocarbon Reservoir Identification","volume":"18","author":"Chen","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1190\/geo2016-0433.1","article-title":"Automatic noise-removal\/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data","volume":"82","author":"Mousavi","year":"2017","journal-title":"Geophysics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TPWRS.2020.3015145","article-title":"Non-Stationary Power System Forced Oscillation Analysis Using Synchrosqueezing Transform","volume":"36","author":"Estevez","year":"2021","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.acha.2020.12.003","article-title":"Signal separation based on adaptive continuous wavelet transform and analysis","volume":"53","author":"Chui","year":"2021","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sharma, T., and Sharma, K.K. (2016, January 21\u201324). Power line interference removal from ECG signals using wavelet transform based component-retrieval. Proceedings of the International Conference on Advances in Computing, Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732031"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5787","DOI":"10.1109\/TSP.2012.2212891","article-title":"A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising","volume":"60","author":"Meignen","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.cmpb.2017.01.008","article-title":"A low-rank matrix factorization approach for joint harmonic and baseline noise suppression in biopotential signals","volume":"141","author":"Zivanovic","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106443","DOI":"10.1016\/j.ymssp.2019.106443","article-title":"Fault diagnosis of rotating machines based on the EMD manifold","volume":"135","author":"Wang","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.jhydrol.2018.11.015","article-title":"A robust method for non-stationary streamflow prediction based on improved EMD-SVM model","volume":"568","author":"Meng","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ji, N., Ma, L., Dong, H., and Zhang, X.J. (2019). EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy. Brain Sci., 9.","DOI":"10.3390\/brainsci9080201"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Karpinski, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., and Nogalski, A. (2022). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint. Sensors, 22.","DOI":"10.3390\/s22103765"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Karpinski, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., and Nogalski, A. (2022). Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part I: Femoral-Tibial Joint. Sensors, 22.","DOI":"10.3390\/s22062176"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"056017","DOI":"10.1088\/1741-2552\/ab33e4","article-title":"VMD-based denoising methods for surface electromyography signals","volume":"16","author":"Xiao","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.sigpro.2012.11.029","article-title":"The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications","volume":"93","author":"Thakur","year":"2013","journal-title":"Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1007\/BF02345747","article-title":"Estimation of surface electromyogram spectral alteration using reduced-order autoregressive model","volume":"38","author":"Karlsson","year":"2000","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_49","first-page":"8","article-title":"Standards for surface electromyography: The European Project Surface EMG for Non-invasive Assessment of Muscles (SENIAM)","volume":"10","author":"Hermens","year":"1999","journal-title":"Enschede Roessingh Res. Dev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:44:46Z","timestamp":1760125486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":49,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115182"],"URL":"https:\/\/doi.org\/10.3390\/s23115182","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,29]]}}}