Literature DB >> 21112805

An adaptive singular spectrum analysis approach to murmur detection from heart sounds.

Saeid Sanei1, Mansoureh Ghodsi, Hossein Hassani.   

Abstract

Murmur is the result of various heart abnormalities. A new robust approach for separation of murmur from heart sound has been suggested in this article. Singular spectrum analysis (SSA) has been adapted to the changes in the statistical properties of the data and effectively used for detection of murmur from single-channel heart sound (HS) signals. Incorporating a cleverly selected a priori within the SSA reconstruction process, results in an accurate separation of normal HS from the murmur segment. Another contribution of this work is selection of the correct subspace of the desired signal component automatically. In addition, the subspace size can be identified iteratively. A number of HS signals with murmur have been processed using the proposed adaptive SSA (ASSA) technique and the results have been quantified both objectively and subjectively.
Copyright © 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 21112805     DOI: 10.1016/j.medengphy.2010.11.004

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  11 in total

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6.  Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

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Review 8.  A glance at the applications of Singular Spectrum Analysis in gene expression data.

Authors:  Hossein Hassani; Zara Ghodsi
Journal:  Biomol Detect Quantif       Date:  2015-05-29

9.  An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography.

Authors:  Hai Hu; Shengxin Guo; Ran Liu; Peng Wang
Journal:  PeerJ       Date:  2017-06-28       Impact factor: 2.984

10.  A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method.

Authors:  Jie Zhao; Jiajia Song; Xiaoli Li; Jiannan Kang
Journal:  Brain Behav       Date:  2020-10-30       Impact factor: 2.708

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