ANALYSIS OF HEART SOUND SIGNALS IN EBSTEIN'S ANOMALY

Yıl 2022, Cilt: 7 Sayı: 1, 1 - 6, 03.07.2022

Ömer Akgün

https://doi.org/10.52876/jcs.1131979

Cited By: 1

Öz

Ebstein's anomaly is an abnormality in the pediatric heart disease group. The anomaly is described as a structural defect by considering the whole heart. It can be manifested by typical symptoms in auscultation and can be detected with other diagnostic methods such as ECG. Systolic ejection click and murmur are the most important symptoms in the diagnosis of disease. In this study, heart sound signal recorded from a 13-year-old patient was analyzed with different numerical methods along with a normal heart sound signal. The signals were first examined in the time plane and findings in auscultation were observed. The frequency components of the signals were then obtained. Additional frequency components emerged in findings of disease in this plane compared to the normal one. Spectrograms enable to observe the differences in time frequency and amplitude components. Bispectral analysis was performed as a high order spectral analysis method by diversifying the analysis. In bispectral analysis of the anomaly, click and murmurs are manifested by equiphase surfaces distributed at high frequencies.Lastly, the power spectrum density of the signals were examined. The decrease in the additional power peak and power rating of the diseased signal was remarkable.

Anahtar Kelimeler

Ebstein, Heart sound signals, Time frequency analyses, Bispectrum, Power spectrum density

Kaynakça

• A.F. Corno, P.G. Chassot, M. Payot, N. Sekarski, P. Tozzi, L.K.V. Segesser, “Ebstein’s anomaly. One and a half ventricular repair”, Swiss med wkly, Vol.132, 2002, pp. 485-488.
• J.B. Seward, “Ebstein's Anomaly: Ultrasound Imaging and hemodynamic evaluation”, Echocardiography. A Journal of CV Ultrasound. Allied-Tech 6, 1993, pp. 641.
• E.A. Jeyman, Principles and Practice of Echocardiography, Lea-Febiger, Pennsylvania, pp. 840, 1994.
• R.A. Lange, J.E. Cigarroa, Conn's Current Therapy , pp.105-110 , 2019.
• https://www.drtaneryavuz.com/ebstein-anomalisi/, 2021
• https://www.mhttpayoclinic.org/diseases-conditions/ebsteins-anomaly/symptoms-causes/syc-20352127, 2021
• D.P. Singh, K. Mahajan, StatPearls [Internet],StatPearls Publishing, Treasure Island (FL), 2018.
• O. Akgun, Analysis of heart sound signals in mitral valve diseases, PhD thesis, 2011.
• G.E. Guraksin, E. Ucman, O. Deperlioglu, “Performing discrete fourier transform of the heart sounds on the pocket computer”, 14th National Biomedical Engineering Meeting, 2009.
• E. Onal, J. Dikun, “Short-Time Fourier Transform for Different Impulse Measurements”, Balkan Journal of Electrical and Computer Engineering (Bajece), Vol.1, No.1, 2013, pp.44-47.
• M. Mishra, S. Pratiher, S. Banerjee, A. Mukherjee, “Grading heart sounds through variational mode decomposition and higher order spectral features”, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).
• R. Bracewell, The Fourier transform and its applications. 3rd edn. McGraw-Hill, New York, 1999.
• S.K. Mitra, J.F. Kaiser, Handbook for Digital Signal Processing, John Wiley & Sons, NY, NY, 1993.
• A.L.Douglas, “The Discrete Fourier Transform, Part 4: Spectral Leakage”, in Journal of Object Technology, Vol.8, No.7, 2009, pp. 23-24.
• P.B. Marchant, “Time-Frequency Analysis for Biosystem Engineering”, Biosystems Engineering, Vol.85, No.3, 2003, pp. 261-281.
• A. Parkhi, M. Pawar, “Analysis of Deformities in Lung Using Short Time Fourier Transform Spectrogram Analysis on Lung Sound”, International Conference on Computational Intelligence and Communication Networks, 2011
• Y.C. Kim, E.J. Powers, “Digital Bispectral Analysis and it's Applications to Nonlinear Wave Interactions”, IEEE Transactions on Plasma Science, Vol.7, No.2, 2007,pp. 120-131.
• T.C. Akinci, S. Seker, O. Akgun, J. Dikun, G. Erdemir, “Bispectrum and energy analysis of wind speed data”, 2016 The 9th International Conference on Computer and Electrical Engineering (ICCEE 2016), Barcelona, December, 2016.
• C.L. Nikias and M.R. Raghuveer, “Bisepctrum Estimation: A Digital Signal Processing Framework”, Proceedings of the IEEE, Vol.75, No.7, 1987, pp. 869-891.
• J.T. Astola, K.O. Egiazarian, G.I. Khlopov, S.I. Khomenko, I.V. Kurbatov, V.YE. Morozov, A.V. Totsky, “Application of bispectrum estimation gor time-frequency analysis of ground surveillance Doppler radar echo signals”, IEEE Trans. on Instrumentation and Measurement, Vol.57, No.9, 2008, pp.1949–1957.
• M. Zainuddin Lubis, Signal processing for marine acoustic and dolphin using matlab, Edition: 2016, Chapter: 2, Publisher: LAP LAMBERT Academic Publishing is a trademark of OmniScriptum GmbH & Co. KG, Editors: Carolyn Evans, pp.15-25, 2016.
• E.D. Ubeyli, I. Guler, “Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals”, Computers in Biology and Medicine. Vol.33, 2003, pp. 473–493.
• P.S. Akanksha, M. Kayapanda, S. Goutam, “Identification of Coronary Artery Disease using Cross Power Spectral Density”, 2017, 14th IEEE India Council International Conference (INDICON).
• N. Lahcène, A. Hafaifa, A. Kouzou, M. Guemana, S. Abudura, “Detecting rotor faults of SCIG based wind turbine using PSD estimation methods”, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).