{"id":14263,"date":"2026-02-11T16:51:39","date_gmt":"2026-02-11T14:51:39","guid":{"rendered":"https:\/\/dx-systems.skillunit.pl\/?p=14263"},"modified":"2026-02-11T16:51:40","modified_gmt":"2026-02-11T14:51:40","slug":"artifact-correction-method-for-m-response-usingunet1d-network","status":"publish","type":"post","link":"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/","title":{"rendered":"Artifact Correction Method for M-response usingUNet1D Network"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"<p>Abstract\u2014this article explores the application of convolutional neural networks (CNNs), specifically the<br \/>\nUNet1D architecture, for filtering M-response signals \u2212 an essential component of electromyographic (EMG) diagnostics used to assess the functional state of the<br \/>\nperipheral neuromuscular system. The goal of the study is to improve signal quality by effectively removing artifacts while<br \/>\npreserving the amplitude-frequency characteristics critical for clinical interpretation. The model was trained on real biomedical data containing various types of noise and artifacts. Results show that the proposed model achieves high<br \/>\nfiltering accuracy with minimal deviation from the original signals (a mean amplitude deviation of only \u22120.04%, and a 20.75% increase in signal-to-noise ratio, SNR). The CNN-based approach demonstrated strong potential for preserving diagnostically significant features and can be effectively<br \/>\nintegrated into clinical EMG workflows to enhance diagnostic precision and reduce the impact of external<br \/>\ninterference.<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[58],"tags":[],"class_list":["post-14263","post","type-post","status-publish","format-standard","hentry","category-publications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Artifact Correction Method for M-response usingUNet1D Network - DX-SYSTEMS<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artifact Correction Method for M-response usingUNet1D Network - DX-SYSTEMS\" \/>\n<meta property=\"og:description\" content=\"Abstract\u2014this article explores the application of convolutional neural networks (CNNs), specifically the UNet1D architecture, for filtering M-response signals \u2212 an essential component of electromyographic (EMG) diagnostics used to assess the functional state of the peripheral neuromuscular system. The goal of the study is to improve signal quality by effectively removing artifacts while preserving the amplitude-frequency characteristics critical for clinical interpretation. The model was trained on real biomedical data containing various types of noise and artifacts. Results show that the proposed model achieves high filtering accuracy with minimal deviation from the original signals (a mean amplitude deviation of only \u22120.04%, and a 20.75% increase in signal-to-noise ratio, SNR). The CNN-based approach demonstrated strong potential for preserving diagnostically significant features and can be effectively integrated into clinical EMG workflows to enhance diagnostic precision and reduce the impact of external interference.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\" \/>\n<meta property=\"og:site_name\" content=\"DX-SYSTEMS\" \/>\n<meta property=\"article:published_time\" content=\"2026-02-11T14:51:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-11T14:51:40+00:00\" \/>\n<meta name=\"author\" content=\"dx\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"dx\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\"},\"author\":{\"name\":\"dx\",\"@id\":\"https:\/\/dx-systems.com\/#\/schema\/person\/01c23512fcd5d05934a91a0d8f06b6b8\"},\"headline\":\"Artifact Correction Method for M-response usingUNet1D Network\",\"datePublished\":\"2026-02-11T14:51:39+00:00\",\"dateModified\":\"2026-02-11T14:51:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\"},\"wordCount\":8,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/dx-systems.com\/#organization\"},\"articleSection\":[\"Publications\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\",\"url\":\"https:\/\/dx-systems.com\/en\/artifact-correction-method-for-m-response-usingunet1d-network\/\",\"name\":\"Artifact Correction Method for M-response usingUNet1D Network - 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