Document Detail

ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection.
MedLine Citation:
PMID:  22929363     Owner:  NLM     Status:  Publisher    
This paper presents a useful technique for totally automatic detection of myocardial infarction from patients ECGs. Due to the large number of heartbeats constituting an ECG and the high cost of having all the heartbeats manually labeled, supervised learning techniques have achieved limited success in ECG classification. In this paper, we first discuss the rationale for applying multiple instance learning (MIL) to automated ECG classification and then propose a new MIL strategy called LTMIL, by which ECGs are mapped into a topic space defined by a number of topics identified over all the unlabeled training heartbeats and SVM is directly applied to the ECG-level topic vectors. Our experimental results on real ECG datasets from the PTB diagnostic database demonstrate that, compared with existing multiple instance learning and supervised learning algorithms, the proposed algorithm is able to automatically detect ECGs with myocardial ischemia without labeling any heartbeats. Moreover, it improves classification quality in terms of both sensitivity and specificity.
L Sun; Y Lu; K Yang; S Li
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-8-23
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  -     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2012 Aug 
Date Detail:
Created Date:  2012-8-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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