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2021 | Vol. 27, Iss. 1 | 63--72
Tytuł artykułu

Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds.
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Rocznik
Strony
63--72
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B.Belkaid-Tlemcen BP 119, Tlemcen; Algeria BP 119
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B.Belkaid-Tlemcen BP 119, Tlemcen; Algeria BP 119
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B.Belkaid-Tlemcen BP 119, Tlemcen; Algeria BP 119
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B.Belkaid-Tlemcen BP 119, Tlemcen; Algeria BP 119, adebbal@yahoo.fr
Bibliografia
  • 1. Tilkian A, Conover M. Enderstanding heart sounds and murmurs: with an introduction to lung sounds, 4th edn. Philadelphia, Saunders, 2001.
  • 2. Debbal SD, Bereksi-Reguig F. Complementary analysis to heart sounds while using the short time fourier and the continuous wavelet transforms. Biomedical Engineering: Applications, Basis and Communications. 2007;19(5):331–339. https://doi.org/10.4015/S1016237207000434/
  • 3. Stagmo ML, Persson J. Cardiac and Cardiovascular Systems, Lund University Publications. 2008, ISBN: 978-91-44-01989-5
  • 4. Novey D, Pencar M, Stang, J. The guide to heart sounds: Normal and abnormal, CRC Press Inc, Florida,1990.
  • 5. Shaver J, Salerni R, Reddy P. Normal and abnormal heart sound in cardiac diagnosis. Part I: Systolic sounds. Curr Probl Cardiol;10(3):2-68, 1995. https://doi.org/10.1016/0146-2806(85)90007-6
  • 6. Leung T, White P, Cook J, et al. Analysis of the second heart sound for diagnosis of pediatric heart diseases, IEEE Proc Sci Meas Technol. 1998:145(6):285-290. https://doi.org/10.1049/ip-smt:19982326
  • 7. Yaseen, Son GY, Kwon S. Classification of Heart Sound Signal Using Multiple Features. Appl Sci. 2018;8(12):2344. https://doi.org/10.3390/app8122344
  • 8. Boussa M, Atouf I, Atibi M, et al. Comparison of MFCC and DWT features extractors applied to PCG classification, 11th International Conference on Intelligent Systems: Theories and Applications (SITA), 2016. https://doi.org/10.1109/SITA.2016.7772312
  • 9. Rosół M, Więckowski Ł. Embedded heart rate analysis based on sound sensing, 24th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 2019, 629-633. https://doi.org/10.1109/MMAR.2019.8864661
  • 10. http://www.cardiosource.com/heartsounds. Accessed April 25, 2018.
  • 11. American College of Cardiology. http://www.Egeneral medical.com. Accessed November 25, 2006.
  • 12. Heart Sounds and Murmurs. http://www.dundee.ac.uk/medther/Cardiology/hsmur.html. Accessed November 5, 2006.
  • 13. Omari.T, Debbal SM. Etude de degré de sévérité pathologique des sténoses aortiques, magister thesis, Tlemcen University, 2009.
  • 14. Ahmad TJ, Ali H, Khan SA. Classification of Phonocardiogram using an Adaptive Fuzzy Inference System. Proceedings of International Conference on Image Processing, Computer Vision (IPCV), Monte Carlo Resort, Las Vegas, Nevada, USA, 2009: 609-614, 2009.
  • 15. Meziani F, Debbal SM, Atbi A. Analyse du Degré de Sévérité Pathologique de La sténose aortiques(AS) par Application de La transformée en Ondelettes Continue (TOC), a l’occasion de: International Conférence on MultiMedia Information Procession: CMIP'2012, Algérie. 2012
  • 16. Meziani F, Debbal SM, Atbi A. Analysis of the pathological severity degree of aortic stenosis (AS) and mitral stenosis (MS) using the discrete wavelet transform (DWT). Journal of Medical Engineering & Technology. 2013;37(1):61-74. https://doi.org/10.3109/03091902.2012.733058
  • 17. Chua KC, Chandran V, Acharya UR, Lim CM. Cardiac state diagnosis using higher order spectra of heart rate variability. Journal of Medical Engineering & Technology. 2008;32(2):145-155. https://doi.org/10.1080/03091900601050862
  • 18. Goshvarpour A, Goshvarpour A, Rahati S, Saadatian V. Bispectrum estimation of electroencephalogram signal during meditation. Iran J Psychiatry Behav Sci. 2012;6(2):48-54.
  • 19. Nikias C, Raghuveer M. Bispectrum estimation: a digital signal processing framework. Proc IEEE. 1987;75(7):869-91. https://doi.org/10.1109/PROC.1987.13824
  • 20. Chua KC, Chandran V, Acharya R, Lim CM. Higher Order Spectral (HOS) Analysis Of Epileptic EEG Signals, Engineering in Medicine and Biology Society. Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007:6495- 6498. https://doi.org/10.1109/IEMBS.2007.4353847
  • 21. Debbal S, Bereksi-Reguig F. Analyse spectro-temporelle des bruits cardiaques par les transformees discrete et continue d’ondelettes. Sciences & Technologie. B, Sciences De l’ingénieur. 2005(23):5-15.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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