NDLI: Latent topic multi-instance learning approach for automated ECG classification

Content Provider	IEEE Xplore Digital Library
Author	Li Sun Yanping Lv Shaozi Li Xuzhou Li
Copyright Year	2011
Description	Author affiliation: Cognitive Science Department of Xiamen University, China (Li Sun; Yanping Lv; Shaozi Li; Xuzhou Li)
Abstract	This paper presents a new latent topic multiple instance learning (LTMIL) for automated ECG classification. Due to the characteristics of multiple beats constituting an ECG and the high cost of having all the beats manually labeled, supervised machine learning techniques have achieved limited success in ECG classification. In this paper, we first discuss the rational for applying multiple instance learning (MIL) to automated ECG classification and propose a new MIL strategy called LTMIL for it which integrates the intra and inter ECG difference. It is a three hierarchical model. Firstly, we cluster all unlabeled beats into k topics using variable weighting, by which beats are reshaped to be dense and separable. An ECG and its beats are then represented as mixtures over topics. Consequently, the intra and inter ECG difference can be fully embodied in the difference between mixtures over topics. Finally, any supervised learning techniques can be applied to classification of transformed ECGs. Our experimental results on real ECG datasets from the PTB diagnostic database demonstrate that compared with existing multiple instance learning and supervised machine learning algorithms, the proposed algorithm is able to automatically classify ECG without labeling beats and improves the classification quality in terms of sensitivity and specificity.
Starting Page	546
Ending Page	550
File Size	144220
Page Count	5
File Format	PDF
ISBN	9781612847016
e-ISBN	9781612847047
DOI	10.1109/ITiME.2011.6132169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Sensitivity Mixture of Topics ECG Classification Electrocardiography Sensitivity and specificity Feature extraction Classification algorithms Multiple Instance Learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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