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Proceedings Paper

Singular value decomposition-based segmentation of multi-component signals
Author(s): Sreeraman Rajan; Rajamani Doraiswami
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Paper Abstract

A methodology for segmentation of multi-component signals buried in additive white Gaussian noise using singular value decomposition (SVD) in the time-frequency domain is proposed. The segmentation problem is posed as a binary statistical hypothesis testing problem. Using the Generalized Likelihood Ratio (GLR), the optimal test statistic is shown to be the sum of squares of the norms of the principal components of the signal in the time-frequency domain. The signal-to-noise ratio (SNR) at the dominant signal frequencies is assumed to be sufficiently high to determine the bandwidth of the signal components. The proposed segmentation methodology is evaluated on phonocardiogram (PCG) signals.

Paper Details

Date Published: 9 April 2007
PDF: 12 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657609 (9 April 2007); doi: 10.1117/12.719808
Show Author Affiliations
Sreeraman Rajan, Defence Research and Development Canada-Ottawa (Canada)
Rajamani Doraiswami, Univ. of New Brunswick (Canada)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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