Segmentation of Pathological Heart Sound Signal Using Empirical Mode Decomposition
| Autor: | Braham Barkat, Messaoud Benidir, Daoud Boutana |
|---|---|
| Přispěvatelé: | Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Baverel, Myriam |
| Rok vydání: | 2013 |
| Předmět: |
Phonocardiogram
Audio signal Stationary process [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Noise (signal processing) business.industry Computer science Acoustic energy 020206 networking & telecommunications Pattern recognition 02 engineering and technology Signal Hilbert–Huang transform [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing cardiovascular system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | Proceedings of the 4th International Conference on Signal Processing Systems ICSPS 2012 ICSPS 2012, Dec 2012, Kuala Lumpur, Malaysia. pp.1-5 IJCEE-International Journal of Computer and Electrical Engineering IJCEE-International Journal of Computer and Electrical Engineering, International Academy Publishing (IAP), 2013, 5 (1), pp.26-29. ⟨10.7763/ijcee.2013.v5.655⟩ |
| ISSN: | 1793-8163 |
| DOI: | 10.7763/ijcee.2013.v5.655 |
| Popis: | International audience; The Phonocardiogram (PCG) is the graphical recording of acoustic energy produced by the mechanical activity of various cardiac. Due to the complicated mechanisms involved in the generation of in the PCG signal, it is considered as multicomponent non stationary signal. Empirical mode decomposition (EMD) allows decomposing an observed multicomponent signal into a set of monocomponent signals, called Intrinsic Mode Functions (IMFs). The goal of this paper is to segment some pathological HS signals into the murmurs related to cardiac diseases. EMD approach allows to automatically selecting the most appropriate IMFs characterizing the murmur using the noise only model. Real-life signals are used in the various cases such as Early Aortic Stenosis (EAS), Late Aortic Stenosis (LAS), Mitral Regurgitation (MR) and Aortic Regurgitation (AR) to validate, and demonstrate the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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