Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition

Ghafoor Shah; Peter Koch; Constantinos B. Papadias

doi:10.1109/BIBE.2014.14

Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition

Ghafoor Shah, Peter Koch, Constantinos B. Papadias

Department of Electronic Systems

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

Abstract

The detection of heart rate variability (HRV) via cardiac auscultation examination can
be a useful and inexpensive tool which, however, is challenging in the presence of pathological
signals and murmurs. The aim of this research is to analyze acoustic cardiac signals for HRV and
murmur detection. A novel method based on hierarchical decomposition of the single channel
mixture using various nonnegative matrix factorization techniques is proposed, which provides
unsupervised clustering of the underlying component signals. HRV is determined over the
recovered normal cardiac acoustic signals. This novel decomposition technique is compared
against the state-of-the-art techniques; experiments are performed using real-world clinical data,
which show the potential significance of the proposed technique.

Original language	English
Title of host publication	Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on
Publisher	IEEE Computer Society Press
Publication date	2014
Pages	46-53
DOIs	https://doi.org/10.1109/BIBE.2014.14
Publication status	Published - 2014
Event	2014 IEEE International Conference on Bioinformatics and Bioengineering - Boca Raton, Florida, United States Duration: 10 Nov 2014 → 12 Nov 2014 Conference number: 34280

Conference

Conference	2014 IEEE International Conference on Bioinformatics and Bioengineering
Number	34280
Country/Territory	United States
City	Boca Raton, Florida
Period	10/11/2014 → 12/11/2014

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10.1109/BIBE.2014.14

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Cite this

Shah, G., Koch, P., & Papadias, C. B. (2014). Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition. In Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on (pp. 46-53). IEEE Computer Society Press. https://doi.org/10.1109/BIBE.2014.14

@inproceedings{2c6a819832f24140b291047afcda15eb,

title = "Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition",

abstract = "The detection of heart rate variability (HRV) via cardiac auscultation examination can be a useful and inexpensive tool which, however, is challenging in the presence of pathological signals and murmurs. The aim of this research is to analyze acoustic cardiac signals for HRV and murmur detection. A novel method based on hierarchical decomposition of the single channel mixture using various nonnegative matrix factorization techniques is proposed, which provides unsupervised clustering of the underlying component signals. HRV is determined over the recovered normal cardiac acoustic signals. This novel decomposition technique is compared against the state-of-the-art techniques; experiments are performed using real-world clinical data, which show the potential significance of the proposed technique.",

author = "Ghafoor Shah and Peter Koch and Papadias, {Constantinos B.}",

year = "2014",

doi = "10.1109/BIBE.2014.14",

language = "English",

pages = "46--53",

booktitle = "Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on",

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note = "2014 IEEE International Conference on Bioinformatics and Bioengineering ; Conference date: 10-11-2014 Through 12-11-2014",

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Shah, G, Koch, P & Papadias, CB 2014, Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition. in Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on. IEEE Computer Society Press, pp. 46-53, 2014 IEEE International Conference on Bioinformatics and Bioengineering, Boca Raton, Florida, United States, 10/11/2014. https://doi.org/10.1109/BIBE.2014.14

Analysis of acoustic cardiac signals for heart rate variability and murmur detection using nonnegative matrix factorization-based hierarchical decomposition. / Shah, Ghafoor; Koch, Peter; Papadias, Constantinos B.
Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on. IEEE Computer Society Press, 2014. p. 46-53.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - Papadias, Constantinos B.

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N2 - The detection of heart rate variability (HRV) via cardiac auscultation examination can be a useful and inexpensive tool which, however, is challenging in the presence of pathological signals and murmurs. The aim of this research is to analyze acoustic cardiac signals for HRV and murmur detection. A novel method based on hierarchical decomposition of the single channel mixture using various nonnegative matrix factorization techniques is proposed, which provides unsupervised clustering of the underlying component signals. HRV is determined over the recovered normal cardiac acoustic signals. This novel decomposition technique is compared against the state-of-the-art techniques; experiments are performed using real-world clinical data, which show the potential significance of the proposed technique.

AB - The detection of heart rate variability (HRV) via cardiac auscultation examination can be a useful and inexpensive tool which, however, is challenging in the presence of pathological signals and murmurs. The aim of this research is to analyze acoustic cardiac signals for HRV and murmur detection. A novel method based on hierarchical decomposition of the single channel mixture using various nonnegative matrix factorization techniques is proposed, which provides unsupervised clustering of the underlying component signals. HRV is determined over the recovered normal cardiac acoustic signals. This novel decomposition technique is compared against the state-of-the-art techniques; experiments are performed using real-world clinical data, which show the potential significance of the proposed technique.

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DO - 10.1109/BIBE.2014.14

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BT - Bioinformatics and Bioengineering (BIBE), 2014 IEEE 14th International Conference on

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