{"title":"Effect of Different Thresholding Techniques for Denoising of EMG Signals by using Different Wavelets","authors":"R. Thukral, Ashwani Kumar, A. Arora, Gulshan","doi":"10.1109/ICCT46177.2019.8969036","DOIUrl":null,"url":null,"abstract":"The Electromyogram (EMG) was at first produced for diagnosing the neuro-muscular disorders and abnormalities. Clinical applications before long wound up obvious, most eminently in epilepsy, lastly it ended up well known because of the introduction of prosthetics, explicitly body-powered prosthesis. The EMG recorded amid different movements to know the electrical action and functional state of the muscles which recognizes the medicinal variations from the normal conditions. The aim of this paper is to remove noise using the thresholding values at each and every level of the decomposition using wavelet can be a better technique, if the thresholding values are appropriate so that the loss of the EMG signal chances will be less.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT46177.2019.8969036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
The Electromyogram (EMG) was at first produced for diagnosing the neuro-muscular disorders and abnormalities. Clinical applications before long wound up obvious, most eminently in epilepsy, lastly it ended up well known because of the introduction of prosthetics, explicitly body-powered prosthesis. The EMG recorded amid different movements to know the electrical action and functional state of the muscles which recognizes the medicinal variations from the normal conditions. The aim of this paper is to remove noise using the thresholding values at each and every level of the decomposition using wavelet can be a better technique, if the thresholding values are appropriate so that the loss of the EMG signal chances will be less.
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