Heart sound is one of the significant bio-signals to diagnose certain cardiac anomalies. Aiming to provide an automatic heart sounds analysis to medical professional, we present a method of heart sound segmentation based on simplicity and strength. The method has two phases: first phase identifies the timings of S1 and S2 sounds’ start and end using simplicity and strength in wavelet domain, and the second phase determines the S1 and S2 using high frequency information. The method incorporates an adaptive approach for thresholding simplicity and strength profile in timings of the sound components detection, as well as main heart sounds components S1 and S2 in presence of abnormal sound, so called murmur. A comparable result to the state-of-the-art methods has been yielded.
Conference
IEEE-ICASSP, March 2011
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Year 2013 : 2 citations
Amir Mohammad Amiri and Giuliano Armano (2013). “Segmentation and Feature Extraction of Heart Murmurs in Newborns”, Journal of Life Sciences and Technologies, Vol. 1, No. 2, June 2013.
Antipolis, Sophia. Segmentation et Classification des signaux non-stationnaires. Diss. Université de Nice Sophia Antipolis
Year 2012 : 2 citations
1. Ali MOUKADEM (2012), " Segmentation et Classification des signaux non-stationnaires", PhD Thesis, Université de Haute Alsace, France.
Barabasa, Constantin, Maria Jafari, and Mark D. Plumbley. "A robust method for S1/S2 heart sounds detection without ecg reference based on music beat tracking." Electronics and Telecommunications (ISETC), 2012 10th International Symposium on. IEEE, 2012.
Year 2011 : 1 citations
Moukadem, Ali. Segmentation et classification des signaux non-stationnaires: application au traitement des sons cardiaque et à l'aide au diagnostic. Diss. Université de Haute Alsace-Mulhouse, 2011.
