{"title":"基于符号离散化的学习集成心电分类","authors":"Mariem Taktak, Hela Ltifi, Mounir Ben Ayed","doi":"10.1016/j.is.2023.102294","DOIUrl":null,"url":null,"abstract":"<div><p>This paper introduces a novel learning ensemble algorithm designed for the classification of Electro-Cardio Graphic (ECG) signals. In real-time monitoring of cardiovascular patients, addressing the scalability challenge requires an adapted representation that enhances dimensionality reduction before the classification process. Our approach focuses on a discretization technique that transforms Time Series (TS) data into a sequence of ordered symbols, thereby enabling simultaneous compression and classification of ECG signals. Experimental results conducted on various ECG databases from the UCR archive benchmark demonstrate a significant improvement over two types of classifiers, namely distance-based and structure-based, and competitive results when compared to shapelet-based classifiers. The proposed algorithm and technique hold promise for enhancing the efficiency and accuracy of ECG signal classification, which is vital for the timely diagnosis and treatment of cardiovascular diseases.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ECG classification with learning ensemble based on symbolic discretization\",\"authors\":\"Mariem Taktak, Hela Ltifi, Mounir Ben Ayed\",\"doi\":\"10.1016/j.is.2023.102294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper introduces a novel learning ensemble algorithm designed for the classification of Electro-Cardio Graphic (ECG) signals. In real-time monitoring of cardiovascular patients, addressing the scalability challenge requires an adapted representation that enhances dimensionality reduction before the classification process. Our approach focuses on a discretization technique that transforms Time Series (TS) data into a sequence of ordered symbols, thereby enabling simultaneous compression and classification of ECG signals. Experimental results conducted on various ECG databases from the UCR archive benchmark demonstrate a significant improvement over two types of classifiers, namely distance-based and structure-based, and competitive results when compared to shapelet-based classifiers. The proposed algorithm and technique hold promise for enhancing the efficiency and accuracy of ECG signal classification, which is vital for the timely diagnosis and treatment of cardiovascular diseases.</p></div>\",\"PeriodicalId\":50363,\"journal\":{\"name\":\"Information Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306437923001308\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923001308","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
ECG classification with learning ensemble based on symbolic discretization
This paper introduces a novel learning ensemble algorithm designed for the classification of Electro-Cardio Graphic (ECG) signals. In real-time monitoring of cardiovascular patients, addressing the scalability challenge requires an adapted representation that enhances dimensionality reduction before the classification process. Our approach focuses on a discretization technique that transforms Time Series (TS) data into a sequence of ordered symbols, thereby enabling simultaneous compression and classification of ECG signals. Experimental results conducted on various ECG databases from the UCR archive benchmark demonstrate a significant improvement over two types of classifiers, namely distance-based and structure-based, and competitive results when compared to shapelet-based classifiers. The proposed algorithm and technique hold promise for enhancing the efficiency and accuracy of ECG signal classification, which is vital for the timely diagnosis and treatment of cardiovascular diseases.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.