[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120239-en":3,"doc-seo-120239-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120239,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Optimized Machine Learning Algorithms for Real-Time ECG Signal Analysis in IoT Networks - Paper Overview","Electrocardiogram (ECG) signal analysis is essential for diagnosing cardiovascular diseases including arrhythmias and heart attacks, and real-time monitoring has become practical with Internet of Things (IoT) wearable devices and sensors. Real-time ECG analytics in IoT environments face data latency, limited computational resources, and strict energy and communication constraints. This work proposes a framework using lightweight machine learning models such as support vector machines and convolutional neural networks, supported by edge computing, data compression, and feature extraction to reduce transmission size. Performance is assessed in accuracy, energy efficiency, and transmission speed.","Optimized Machine Learning Algorithms for Real-Time ECG Signal Analysis in IoT Networks  \n1Mr.P. Selvaprasanth  \n1Assistant Professor, Electronics and Communication Engineering, Sethu Institute ofTechnology, Virudhunagar,  \nIndia.  \n1Corresponding Author’s email: [selvaprasanthapece@sethu.ac.in](selvaprasanthapece@sethu.ac.in)  \nAbstract. Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks.  \nKeywords. Real-Time ECG Signal Analysis, Machine Learning, IoT Networks, Edge Computing, Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), Data Compression, Feature Extraction, Energy Efficiency, Healthcare Monitoring.  \n1. INTRODUCTION  \nThe global burden of cardiovascular diseases (CVDs) has prompted a surge in demand for real-time heart monitoring technologies. Electrocardiograms (ECGs), which measure the electrical activity of the heart, are widely used to detect abnormal heart rhythms, cardiac arrhythmias, and other heart-related disorders. Traditionally, ECG signals are recorded in clinical settings under the supervision of healthcare professionals. However, advances in Internet of Things (IoT) technologies have revolutionized healthcare by enabling continuous monitoring of patients through wearable devices and wireless sensors.  \nThe integration of IoT with healthcare has led to the development of remote monitoring systems, where real-time ECG data is collected by wearable devices and transmitted to healthcare providers for immediate analysis. These systems are especially beneficial for elderly patients and individuals at risk of heart disease, as they provide constant monitoring and allow early detection of potential health issues. However, the real-time analysis of ECG signals in IoT networks presents several challenges, including data latency, limited computational resources ofIoT devices, energy consumption, and communication delays.  \nMachine learning (ML) has emerged as a powerful tool for analyzing ECG signals, enabling the classification of heart conditions based on patterns in the data. However, traditional machine learning models are often computationally intensive and may not be well-suited for resource-constrained IoT environments. Therefore, it is necessary to optimize these algorithms to ensure efficient and accurate real-time ECG signal analysis in","cbCaibq18fYqfJGP","https://ap.wps.com/l/cbCaibq18fYqfJGP","pdf",808475,1,7,"English","en",105,"# Introduction\n# Literature Survey\n# Proposed Framework\n# Edge Computing Strategy\n# Data Compression and Feature Extraction\n# System Evaluation and Results","[{\"question\":\"What challenges does real-time ECG signal analysis face in IoT networks?\",\"answer\":\"The document highlights data latency, limited computational power on IoT devices, energy constraints, and communication delays that hinder real-time processing.\"},{\"question\":\"Which machine learning models are proposed for optimized ECG analysis?\",\"answer\":\"The framework uses lightweight models including support vector machines (SVM) and convolutional neural networks (CNNs), selected to preserve accuracy while fitting low-power device constraints.\"},{\"question\":\"How does edge computing help with ECG processing in IoT?\",\"answer\":\"Edge computing distributes the processing load between IoT devices and nearby edge servers, enabling real-time data processing without sacrificing energy efficiency.\"}]","Optimized Machine Learning Algorithms for Real-Time ECG Signal Analysis in IoT Networks - Paper Overview | PDF",1785728938,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimized-machine-learning-algorithms-for-real-time-ecg-signal-analysis-in-iot-networks-paper-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimized-machine-learning-algorithms-for-real-time-ecg-signal-analysis-in-iot-networks-paper-overview/120239/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What challenges does real-time ECG signal analysis face in IoT networks?","Question",{"text":75,"@type":76},"The document highlights data latency, limited computational power on IoT devices, energy constraints, and communication delays that hinder real-time processing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are proposed for optimized ECG analysis?",{"text":80,"@type":76},"The framework uses lightweight models including support vector machines (SVM) and convolutional neural networks (CNNs), selected to preserve accuracy while fitting low-power device constraints.",{"name":82,"@type":73,"acceptedAnswer":83},"How does edge computing help with ECG processing in IoT?",{"text":84,"@type":76},"Edge computing distributes the processing load between IoT devices and nearby edge servers, enabling real-time data processing without sacrificing energy efficiency.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]