| Feature extraction from parametric time-frequency representations for heart murmur detection. | |
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MedLine Citation:
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PMID: 20517648 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The detection of murmurs from phonocardiographic recordings is an interesting problem that has been addressed before using a wide variety of techniques. In this context, this article explores the capabilities of an enhanced time-frequency representation (TFR) based on a time-varying autoregressive model. The parametric technique is used to compute the TFR of the signal, which serves as a complete characterization of the process. Parametric TFRs contain a large quantity of data, including redundant and irrelevant information. In order to extract the most relevant features from TFRs, two specific approaches for dimensionality reduction are presented: feature extraction by linear decomposition, and tiling partition of the t-f plane. In the first approach, the feature extraction was carried out by means of eigenplane-based PCA and PLS techniques. Likewise, a regular partition and a refined Quadtree partition of the t-f plane were tested for the tiled-TFR approach. As a result, the feature extraction methodology presented, which searches for the most relevant information immersed on the TFR, has demonstrated to be very effective. The features extracted were used to feed a simple k-nn classifier. The experiments were carried out using 45 phonocardiographic recordings (26 normal and 19 records with murmurs), segmented to extract 548 representative individual beats. The results using these methods point out that better accuracy and flexibility can be accomplished to represent non-stationary PCG signals, showing evidences of improvement with respect to other approaches found in the literature. The best accuracy obtained was 99.06 +/- 0.06%, evidencing high performance and stability. Because of its effectiveness and simplicity of implementation, the proposed methodology can be used as a simple diagnostic tool for primary health-care purposes. |
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Authors:
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L D Avendaño-Valencia; J I Godino-Llorente; M Blanco-Velasco; G Castellanos-Dominguez |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't Date: 2010-06-02 |
Journal Detail:
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Title: Annals of biomedical engineering Volume: 38 ISSN: 1521-6047 ISO Abbreviation: Ann Biomed Eng Publication Date: 2010 Aug |
Date Detail:
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Created Date: 2010-07-09 Completed Date: 2010-12-07 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0361512 Medline TA: Ann Biomed Eng Country: United States |
Other Details:
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Languages: eng Pagination: 2716-32 Citation Subset: IM |
Affiliation:
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Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia, Km. 9, Vía al Aeropuerto, Campus la Nubia, Caldas, Manizales, Colombia. ldavendanov@unal.edu.co |
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| MeSH Terms | |
Descriptor/Qualifier:
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Heart Murmurs
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diagnosis*,
physiopathology Humans Least-Squares Analysis Logistic Models Phonocardiography / methods Principal Component Analysis Time Factors |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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