Disease Classification and Biomarker Discovery Using ECG Data.
In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 8 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
11/24/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113630105&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630105 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/24/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630105 113630105 113630105 10.1155/2015/680381 113630105 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Disease Classification and Biomarker Discovery Using ECG Data. aug: au: Huang, Rong Zhou, Yingchun affil: Department of Statistics and Actuarial Sciences, East China Normal University, Shanghai 200241, China sug: subj: Biological Markers Electrocardiography Heart Diseases Classification Discriminant Analysis Human Algorithms Data Analysis Software Descriptive Statistics Sensitivity and Specificity Step-Wise Multiple Regression Funding Source ab: In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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