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Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.
Rocznik
Strony
73--85
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
  • Biomedical Engineering Department, Faculty of Technology, Tlemcen University. Biomedical Engineering Laboratory (GBM), Tlemcen, Algeria. BP 119
  • Biomedical Engineering Department, Faculty of Technology, Tlemcen University. Biomedical Engineering Laboratory (GBM), Tlemcen, Algeria. BP 119
  • Biomedical Engineering Department, Faculty of Technology, Tlemcen University. Biomedical Engineering Laboratory (GBM), Tlemcen, Algeria. BP 119
Bibliografia
  • 1. World Health Organization.Cardiovascular diseases. https://www.who.int/westernpacific/health-topics/cardiovascular-diseases.
  • 2. Debbal SM. Computerized Heart Sounds Analysis. In: Discrete Wavelet Transforms: Biomedical Applications. IntechOpen, 2011. https://doi.org/10.5772/23700
  • 3. Li X, Zhong L, Luo L, et al. Synchronization control of pulsatile ventricular assist devices by combination usage of different physiological signals. Comput Assist Surg. 2019;24:105-112. https://doi.org/10.1080/24699322.2018.1560089
  • 4. Ahmad MS, Mir J, Ullah MO, et al. An efficient heart murmur recognition and cardiovascular disorders classification system. Australas Phys Eng Sci Med. 2019;42:733-743. https://doi.org/10.1007/s13246-019-00778-x
  • 5. Meziani F, Debbal SM, Atbi A. Analysis of phonocardiogram signals using wavelet transform. J Med Eng Technol. 2012;36:283-302. https://doi.org/10.3109/03091902.2012.684830
  • 6. Acharya UR, Sudarshan VK, Koh JEW, et al. Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals. Biomed Signal Process Control. 2017;31:31-43. https://doi.org/10.1016/j.bspc.2016.07.003
  • 7. Mahmoodian N, Schaufler A, Pashazadeh A, et al. Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning. Comput Biol Med. 2019;107:10–17. https://doi.org/10.1016/j.compbiomed.2019.02.001
  • 8. Vejdannik M, Sadr A. Automatic Microstructural Characterization and Classification Using Higher-Order Spectra on Ultrasound Signals. J Nondestruct Eval. 2016;35:16. https://doi.org/10.1007/s10921-015-0332-6
  • 9. Bou Assi E, Gagliano L, Rihana S, et al. Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction. Sci Rep. 2018;8:15491. https://doi.org/10.1038/s41598-018-33969-9
  • 10. Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med. 2014;48:133-149. https://doi.org/10.1016/j.compbiomed.2014.02.012
  • 11. Nikias CL, Mendel JM. Signal processing with higher-order spectra. IEEE Signal Process Mag. 1993;10(3):10-37. https://doi.org/10.1109/79.221324
  • 12. Du X, Dua S, Acharya RU, Chua CK. Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis. J Med Syst. 2012;36:1731-1743. https://doi.org/10.1007/s10916-010-9633-6
  • 13. Nasrolahzadeh M, Mohammadpoory Z, Haddadnia J. Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease. Cogn Neurodyn. 2018;12:583-596. https://doi.org/10.1007/s11571-018-9499-8
  • 14. Mishra M, Pratiher S, Banerjee S, Mukherjee A. Grading heart sounds through variational mode decomposition and higher order spectral features. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) 1–5 (IEEE, 2018). https://doi.org/10.1109/I2MTC.2018.8409620
  • 15. Mookiah MRK, Acharya UR, Lim CM, et al. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl-Based Syst. 2012;33:73-82. https://doi.org/10.1016/j.knosys.2012.02.010
  • 16. Zhou SM, Gan JQ, Sepulveda F. Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface. Inf Sci.2008;178:1629-1640. https://doi.org/10.1016/j.ins.2007.11.012
  • 17. Du X, Dua S, Acharya RU, Chua CK. Classification of Epilepsy Using High-Order Spectra Features and Principle Component Analysis. J Med Syst. 2012;36:1731-1743. https://doi.org/10.1007/s10916-010-9633-6
  • 18. Yugesh CK, Hariharan M, Yuvaraj R, et al. Bispectral features and mean shift clustering for stress and emotion recognition from natural speech. Comput Electr Eng. 2017;62:676-691. https://doi.org/10.1016/j.compeleceng.2017.01.024
  • 19. Ahmad TJ, Ali H, Khan SA. Classification of Phonocardiogram using an Adaptive Fuzzy Inference System. Proc. Int. Conf. Image Process. Comput Vis Pattern Recognit. Proceedings of the 2009 International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2009, July 13-16, 2009, Las Vegas, Nevada, USA.
  • 20. 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). J Med Eng Technol. 2013;37:61-74. https://doi.org/10.3109/03091902.2012.733058
  • 21. Swami A, Mendel JM, Nikias CL. (1998). Higher-order spectral analysis toolbox. The Mathworks Inc, 3, 22-26.
  • 22. eGeneral Medical Inc. USA. eGeneralMedical.com. http://www.egeneralmedical.com/listohearmur.html Accessed 20 Apr 2018.
  • 23. http://www.cardiosource.com/heart sounds. Accessed 20 Apr 2018.
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
Identyfikator YADDA
bwmeta1.element.baztech-8c0d266f-8835-4c36-88f6-eefb89e6cfc8
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