[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-292430-105":59,"doc-detail-292430-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","electrocardiogram-signal-denoising-and-heart-disease-classification-article","Electrocardiogram signal denoising and heart disease classification - Article","","Electrocardiogram recordings often suffer baseline wander, power-line interference, and motion or muscular noise, which weakens the reliability of manual and automated diagnosis. The work introduces a light, reproducible MATLAB pipeline using windowed FIR filters designed with Kaiser and Hamming windows for ECG denoising, followed by R-peak detection and RR-interval analysis to classify heart rate into tachycardia, bradycardia, or normal. Experiments on 15 MIT-BIH records with added Gaussian noise benchmark performance across multiple SNR levels.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/electrocardiogram-signal-denoising-and-heart-disease-classification-article/292430/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/electrocardiogram-signal-denoising-and-heart-disease-classification-article/292430.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What noise sources affect ECG recordings in this study?","Question",{"text":113,"@type":114},"The paper describes baseline wander, power-line interference, and motion or muscular noise as key contaminants that reduce diagnosis reliability.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed pipeline classify heart conditions?",{"text":118,"@type":114},"After applying windowed FIR denoising, it performs R-peak detection and then analyzes RR intervals to classify heart rate as tachycardia, bradycardia, or normal.",{"name":120,"@type":111,"acceptedAnswer":121},"What dataset and noise setting are used for benchmarking?",{"text":122,"@type":114},"The experiments use 15 MIT-BIH records with added Gaussian noise at multiple SNR levels to evaluate denoising performance.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},292430,1789651240,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Electrocardiogram signal denoising and heart disease  \nclassification  \nK Venkata Siva Reddy, M Balaji  \nDepartment of Electronics and Communication Engineering, School of Engineering, Mohan Babu University, Tirupati, India  \nArticle history:  \nReceived Aug 28, 2025 Revised Mar 31, 2026 Accepted Apr 19, 2026  \nKeywords:  \nArrhythmia  \nDigital filtering techniques Electrocardiogram  \nFinite-impulse-response filter MATLAB  \nCorresponding Author:  \nElectrocardiogram (ECG) recordings are often contaminated by baseline wander (BLW), power-line interference, and motion or muscular noise, reducing the reliability of both manual and automated diagnosis. The paper presents a light and reproducible MATLAB pipeline applying finite-impulseresponse (FIR) filters designed using Kaiser and Hamming windows for ECG denoising, which after R-peak detection follows an RR-interval analysis for classification of heart rate as tachycardia, bradycardia, or normal. In the experiments, 15 MIT-BIH records with added Gaussian noise at several SNR levels were used for benchmarking the performance of denoising. FIR bandpass and low-pass windowed filters improved the clarity of the waveform and supported robust R-peak detection; RR-interval-based classification reached a mean accuracy of ≈98.7% on the study set. The approach is computationally lightweight and quite suitable for embedded real-time deployment but is restricted to the small sample of records and synthetic noise modeling. Future work will compare the efficacy of windowed FIR filtering against modern deep-learning denoisers (CNN/RNN/GAN architectures) and assess the pipeline in larger clinical datasets.  \nThis is an open access article under the CC BY-SA license.  \nK Venkata Siva Reddy  \nDepartment of Electronics and Communication Engineering, School of Engineering Mohan Babu University  \nTirupati, India  \n[Email: kvenkatsivareddy@gmail.com](Email: kvenkatsivareddy@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nElectrocardiogram (ECG) is a standard, noninvasive method to record the electrical activity of the heart for diagnosing arrhythmias and other cardiovascular conditions. As low-amplitude ECG signals are susceptible to various types of noise-baseline wander (BLW), power-line interference, electromyographic (EMG) contamination, and electrode motion artifacts-visual interpretation and automated processing remain difficult tasks. Preprocessing, and especially denoising, remain important steps in any ECG analysis pipeline. Recent reviews indicate strong interest in wavelet-based denoising, adaptive filtering, and especially deep learning (DL)-based denoisers that exhibit state-of-the-art performance, often at the cost of larger models and heavier computational requirements.  \nCurrent studies for ECG analysis continue to employ advanced processing techniques which are comprised of a mixture of wavelet transform and neural denoising. Nevertheless, linear phase filters with low complexity such as finite impulse response (FIR) Windowed (Hamming or Kaiser) filters still provide an effective real-time/embedded alternative, due to their predictable performance and ease of integrating them into hardware. As of late, only a handful of publications exist that thoroughly evaluate the design of FIR Windowed filters for use on ECG data while none has addressed how these types of filters can be incorporated  \ninto a validated and repeatable MATLAB denoising – R-peak detection – RR interval classification workflow. In addition to fill the existing gaps, this publication provides: i) an explanation of the design, and the comparative performance of Hamming and Kaiser Windowed FIR ECG data denoising filters; ii) an example of both an R-peak detection as well as RR Interval classification of ECG records from the MIT-BIH database (N=15), and iii) a suggested implementation method for use in real-time hardware applications. The goal of the authors is to provide a baseline for the evaluation of newer denoisi","cbCaihfafYc2YuJC","https://ap.wps.com/l/cbCaihfafYc2YuJC","pdf",784009,"English","# ABSTRACT\n# 1. INTRODUCTION\n# 2. LITERATURE REVIEW","[{\"question\":\"What noise sources affect ECG recordings in this study?\",\"answer\":\"The paper describes baseline wander, power-line interference, and motion or muscular noise as key contaminants that reduce diagnosis reliability.\"},{\"question\":\"How does the proposed pipeline classify heart conditions?\",\"answer\":\"After applying windowed FIR denoising, it performs R-peak detection and then analyzes RR intervals to classify heart rate as tachycardia, bradycardia, or normal.\"},{\"question\":\"What dataset and noise setting are used for benchmarking?\",\"answer\":\"The experiments use 15 MIT-BIH records with added Gaussian noise at multiple SNR levels to evaluate denoising performance.\"}]","Electrocardiogram signal denoising and heart disease classification - Article | PDF",25]