PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The study of the impact of murmurs on heart sounds by using multiple signal classification pseudo-spectrum

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this study was to present a novel framework for the analysis of the impact of murmurs on heart sounds recorded in real clinical environment. Heart sound records were rigorously selected from the PASCAL dataset using an automated signal quality assessment algorithm. Recordings from 159 patients were analyzed for spectral differences in normal, systolic and diastolic murmurs using a Multiple signal classification algorithm pseudo-spectrum. The spectral features evaluated for the first heart sound (S1) and the second heart sound (S2) were: energy, frequency and frequency density. Results show increased energy of fundamental heart sounds in systolic and diastolic murmurs similarly, whilst frequency is decreased inversely. Furthermore, the frequency density of the first and second heart sounds decreases in murmurs and it is shown to be the lowest in systolic murmur cases.
Rocznik
Strony
99--105
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bakr Belkaid Tlemcen, Tlemcen, Algeria
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bakr Belkaid Tlemcen, Tlemcen, Algeria
  • Genie Biomedical Laboratory (GBM), Faculty of Technology, University of Abou Bakr Belkaid Tlemcen, Tlemcen, Algeria
Bibliografia
  • 1. Luisada AA, Aravanis C. Phonocardiography as a Clinical Method of Examination. Medical Clinics of North America. 1957;41(1):235-67. https://doi.org/10.1016/S0025-7125(16)34479-0
  • 2. McGee S. Chapter 41 - Heart Murmurs: General Principles. In: McGee S, editor. Evidence-Based Physical Diagnosis (Third Edition). Philadelphia: W.B. Saunders; 2012. p. 351-70.
  • 3. Debbal SM, Bereksi-Reguig F. Time-frequency analysis of the first and the second heartbeat sounds. Applied Mathematics and Computation. 2007;184(2):1041-52. https://doi.org/10.1016/j.amc.2006.07.005
  • 4. Semmlow JL, Akay M, Welkowitz W. Noninvasive detection of coronary artery disease using parametric spectral analysis methods. IEEE Engineering in Medicine and Biology Magazine. 1990;9(1):33-36. https://doi.org/10.1109/51.62901
  • 5. Larsen BS, Winther S, Nissen L, Diederichsen A, Bøttcher M, Jan Struijk J, et al. Spectral analysis of heart sounds associated with coronary artery disease. Physiological Measurement. 2021;42(10):105013. https://doi.org/10.1088/1361-6579/ac2fb7
  • 6. Semmlow J, Rahalkar K. Acoustic detection of coronary artery disease. Annu Rev Biomed Eng. 2007;9:449-469. https://doi.org/10.1146/annurev.bioeng.9.060906.151840
  • 7. Pathak A, Samanta P, Mandana K, Saha G. Detection of coronary artery atherosclerotic disease using novel features from synchrosqueezing transform of phonocardiogram. Biomedical Signal Processing and Control. 2020;62:102055. https://doi.org/10.1016/j.bspc.2020.102055
  • 8. Akay M, Semmlow JL, Welkowitz W, Bauer MD, Kostis JB. Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds. IEEE Transactions on Biomedical Engineering. 1990;37(4):366-373. https://doi.org/10.1109/10.52343
  • 9. Gauthier D, Akay YM, Paden RG, Pavlicek W, Fortuin FD, Sweeney JK, et al. Spectral Analysis of Heart Sounds Associated With Coronary Occlusions. 6th International Special Topic Conference on Information Technology Applications in Biomedicine, 2007. p. 49-52. https://doi.org/10.1109/ITAB.2007.4407421
  • 10. Semmlow J, Rahalkar K. Acoustic Detection of Coronary Artery Disease. Annual Review of Biomedical Engineering. 2007;9(1):449-69. https://doi.org/10.1146/annurev.bioeng.9.060906.151840
  • 11. Khoruamkid S, Visitsattapongse S. A Low-Cost Digital Stethoscope For Normal and Abnormal Heart Sound Classification. 2022 14th Biomedical Engineering International Conference (BMEiCON). https://doi.org/10.1109/BMEiCON56653.2022.10012113
  • 12. Deng M, Meng T, Cao J, Wang S, Zhang J, Fan H. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Networks. 2020;130:22-32. https://doi.org/10.1016/j.neunet.2020.06.015
  • 13. Kui H, Pan J, Zong R, Yang H, Wang W. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomedical Signal Processing and Control. 2021;69:102893. https://doi.org/10.1016/j.bspc.2021.102893
  • 14. Milani MGM, Abas PE, De Silva LC, Nanayakkara ND. Abnormal heart sound classification using phonocardiography signals. Smart Health. 2021;21:100194. https://doi.org/10.1016/j.smhl.2021.100194
  • 15. Chen P, Zhang Q. Classification of heart sounds using discrete time-frequency energy feature based on S transform and the wavelet threshold denoising. Biomedical Signal Processing and Control. 2020;57:101684. https://doi.org/10.1016/j.bspc.2019.101684
  • 16. Chakir F, Jilbab A, Nacir C, Hammouch A. Phonocardiogram signals classification into normal heart sounds and heart murmur sounds. 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA). https://doi.org/10.1109/SITA.2016.7772311
  • 17. Upretee P, Yüksel ME. 13 - Accurate classification of heart sounds for disease diagnosis by using spectral analysis and deep learning methods. In: Lee KC, Roy SS, Samui P, Kumar V, editors. Data Analytics in Biomedical Engineering and Healthcare. Academic Press; 2021. p. 215-232.
  • 18. Upretee P, Yüksel ME. Accurate Classification of Heart Sounds for Disease Diagnosis by A Single Time-Varying Spectral Feature: Preliminary Results/ 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT).
  • 19. Noman F, Ting CM, Salleh SH, Ombao H. Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/ICASSP.2019.8682668
  • 20. Pandey A, Joshi RC, Dutta MK. Automated Classification of Heart Disease using Deep Learning. 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT). https://doi.org/10.1109/InCACCT57535.2023.10141725
  • 21. Berraih SA, Debbal SMEA, yettou NeB. Severity cardiac analysis using the Higher-order spectra. Applied Mathematics and Computation. 2021;409:126389. https://doi.org/10.1016/j.amc.2021.126389
  • 22. Meziani F, Debbal SM, Atbi A. Analysis of phonocardiogram signals using wavelet transform. Journal of Medical Engineering and Technology. 2012;36(6):283-302. https://doi.org/10.3109/03091902.2012.684830
  • 23. Schmidt R. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation. 1986;34(3):276-280. https://doi.org/10.1109/TAP.1986.1143830
  • 24. Bentley P, Nordehn G, Coimbra M, Mannor S. The PASCAL Classifying Heart Sounds Challenge 2011 (CHSC2011). http://www.peterjbentley.com/heartchallenge/index.html
  • 25. Mei N, Wang H, Zhang Y, Liu F, Jiang X, Wei S. Classification of heart sounds based on quality assessment and wavelet scattering transform. Computers in Biology and Medicine. 2021;137:104814. https://doi.org/10.1016/j.compbiomed.2021.104814
  • 26. Akay M, Semmlow JL, Welkowitz W, Bauer MD, Kostis JB. Noninvasive detection of coronary stenoses before and after angioplasty using eigenvector methods. IEEE Transactions on Biomedical Engineering. 1990;37(11):1095-1104. https://doi.org/10.1109/10.61035
  • 27. Trejo-Caballero G, Rostro-González H, Romero-Troncoso RdJ, et al. Multiple signal classification based on automatic order selection method for broken rotor bar detection in induction motors. 2017;99:987-996. https://doi.org/10.1007/s00202-016-0463-5
  • 28. Stoica P, Moses R. Spectral Analysis of Signals. Prentice Hall. 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-890b3411-1718-463b-8f40-4d727650ca55
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.