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Intrinsic Mode Decomposition of Physiological Signals for Feature Extraction

[+] Author Affiliations
B. Samanta, C. Nataraj

Villanova University, Villanova, PA

Paper No. DETC2009-86806, pp. 233-239; 7 pages
doi:10.1115/DETC2009-86806
From:
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 22nd Biennial Conference on Mechanical Vibration and Noise, Parts A and B
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4898-2 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME

abstract

This paper presents a study for extracting features from physiological signals using intrinsic mode decomposition. The complex, nonlinear and non-stationary biomedical signals are first decomposed into intrinsic mode functions (IMF). Next each IMF is subjected to morphological signal processing (MSP) for extracting features, namely, pattern spectrum entropy (PSEn), that characterize the shape-size complexity of the component signals. These along with other features like energy (E) and sample entropy (SampEn) are extracted from the individual IMF as well as the cumulative sums of IMF for characterizing the signals. The procedure is illustrated using heart sound signals digitally recorded during cardiac auscultation representing different cardiac conditions. The study examines the effectiveness of IMF based features in the assessment of cardiac state.

Copyright © 2009 by ASME

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