[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117938-en":3,"doc-seo-117938-105":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},117938,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345",7,"Healthcare","Securing the Biometric through ECG using Machine Learning Techniques","Biometric security relies on extracting identity information from biomedical signals, with the electrocardiogram (ECG) valued for individual uniqueness. The study distinguishes persons by combining preprocessing and feature extraction, using discrete cosine transform to extract signal features. Classification is performed with Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). SVM achieves 87% accuracy, while K-NN reaches 96.6% accuracy with k=3, demonstrating machine learning value for ECG-based identification.","Securing the Biometric through ECG using Machine  \nLearning Techniques  \nMr. Praveen Kumar Gupta1, Dr. Subhash Singh Parihar2, Dr. Ritesh Rastogi3, Dr. Vidushi4, Ms. Komal Salgotra5  \n1Assistant Professor KIET Group of Institutions, Ghaziabad  \n2Associate Professor, Pranveer Singh Institutions of Technology, Kanpur  \n3Associate Professor, Noida Institutions of Technology, Greater Noida  \n4Assistant Professor, CHRIST (Deemed to be University) Delhi-NCR  \n5Teaching Assistant, KIET Group of Institutions, Ghaziabad  \nAbstract: In the current era, biometrics is widely used for maintaining the security. To extract the information from the biomedical signals, biomedical signal processing is needed. One of the significant tools used for the diagnostic is electrocardiogram (ECG) . The main reason behind this is the certain uniqueness in the ECG signals of the individual. In this paper, the focus will be on distinguishing the individual on the basis of ECG signals using feature extraction approaches and the machine learning algorithms. Other than preprocessing approach, the discrete cosine transform is applied to perform the extraction. The classification between the signals of the individuals is carried out using the Support Vector Machine and K-Nearest Neighbor machine learning techniques. The classification accuracy achieved through SVM is 87% and K-NN has achieved a classification accuracy of 96.6% with k=3 . The work has shown how machine learning can be used to classify the ECG signal.  \nKeywords: auto correlation, discrete cosine transform, normalization, K-NN, SVM  \nI. Introduction  \nVerification or identification biometric information systems can be either implemented on an existing system or built from scratch[1] . The verification method strives to validate an individual's claimed identity by checking their claims against several markers of individuality. In relation to this matter, it is important to note that an identification system has the capability to ascertain the identity of an individual (among the individuals stored in an expert system) without necessitating the individual to explicitly provide their identity. The verification system employs a one-to-one search methodology, wherein a single item is searched to identify a corresponding match. Conversely, the identification system utilizes a single to Multi search approach, wherein multiple items are searched to identify potential matches. The utilization of biosignature technology has had a significant influence in a wide range of applications, facilitating enhanced security measures and enabling its involvement in diverse domains such as bank check verification, author identification, operational banking, face recognition, medicinal findings, turnout tracking, authorized file authentication, and security trials [2] .  \nAlthough several biometric systems, a signature verification method remains among the most demanding and lucrative behavioural biometrics [3] . The term signature is based on signing off, which originates from the Latin root, signature, and sign. Whether it is someone's writing or someone else's, a signed specimen is used to identify a  \nperson. The signature verification system is a method of verifying the authenticity of a signer before any existing samples are used. It is one of the most desirable biometric verification systems due to its vast number of favourable characteristics such as convenience, social acceptability, and lack of legal or societal problems [4] .  \nThe recent introduction of big data and artificial intelligence technology is revolutionizing the healthcare system significantly, which could lead to significant economic change for the industry[5] . This research article is geared toward digital health security that takes advantage of machine learning for biometric data. Biometric authentication is on the rise and is becoming a more popular option for access control systems, which is why it's becoming the main control method. The ut","cbCaiv9UoNmH8Sxo","https://ap.wps.com/l/cbCaiv9UoNmH8Sxo","pdf",275548,1,6,"English","en",105,"# I. Introduction\n## Biometric verification vs identification\n## Applications of biosignatures\n## Signature verification and access control\n## Digital health security and ECG-based authentication\n## Overview of biomedical signals and biometric traits\n## Proposed biometric classification pipeline","[{\"question\":\"Why is the ECG signal useful for biometric identification?\",\"answer\":\"ECG signals contain distinctive characteristics for each individual, which enables reliable person distinction using biomedical signal analysis.\"},{\"question\":\"What methods are used for feature extraction from ECG in this work?\",\"answer\":\"Discrete cosine transform is applied as part of the feature extraction process, alongside preprocessing and related signal handling steps.\"},{\"question\":\"How do SVM and K-NN perform for ECG classification?\",\"answer\":\"SVM achieves 87% classification accuracy, while K-NN achieves 96.6% accuracy with k=3.\"}]","Securing the Biometric through ECG using Machine Learning Techniques | PDF",1785680450,15,{"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},"securing-the-biometric-through-ecg-using-machine-learning-techniques","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/securing-the-biometric-through-ecg-using-machine-learning-techniques/117938/",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","2026-08-02",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},"Why is the ECG signal useful for biometric identification?","Question",{"text":75,"@type":76},"ECG signals contain distinctive characteristics for each individual, which enables reliable person distinction using biomedical signal analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methods are used for feature extraction from ECG in this work?",{"text":80,"@type":76},"Discrete cosine transform is applied as part of the feature extraction process, alongside preprocessing and related signal handling steps.",{"name":82,"@type":73,"acceptedAnswer":83},"How do SVM and K-NN perform for ECG classification?",{"text":84,"@type":76},"SVM achieves 87% classification accuracy, while K-NN achieves 96.6% accuracy with k=3.","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},[93,97,101,105,110,114,117,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},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"]