Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise

• Autorzy: Altuve Miguel , Suárez Luis , Ardila Jeyson

Identyfikatory

DOI

10.1016/j.bbe.2019.12.007

• Języki publikacji: EN

Abstrakty

EN

Phonocardiogram (PCG) recordings contain valuable information about the functioning and state of the heart that is useful in the diagnosis of cardiovascular diseases. The first heart sound (S1) and the second heart sound (S2), produced by the closing of the atrioventricular valves and the closing of the semilunar valves, respectively, are the fundamental sounds of the heart. The similarity in morphology and duration of these heart sounds and their superposition in the frequency domain makes it difficult to use them in computer systems to provide an automatic diagnosis. Therefore, in this paper, we analyzed these heart sounds in the intrinsic mode functions (IMF) domain, which were issued from two time-frequency decomposition techniques, the empirical mode decomposition (EMD) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), with the aim of retrieving useful information on an expanded basis. The decomposition of PCG recordings into IMF allows representing the fundamental cardiac sounds in many oscillating components, increasing thus the observability of the system. Moreover, the time-frequency representation of PCG recordings could provide valuable information to automatically detect heart sounds and diagnose pathologies from characteristic patterns of these heart sounds in the IMF. The analysis was made through the variance and Shannon's entropy of the heart sounds, observed in time windows located among different IMF. In addition, we determined the frequencies ranges of the IMF from the decomposition of the PCG recordings using both techniques. Given that the frequency content of S1 and S2 is different but overlap each other, and the duration of these sounds are also different, these heart sounds were represented in different IMF with different variances and entropies, in both techniques, but the ICEEMDAN offers a more consistent decomposition of S1 and S2 (they were concentrated in IMF 4-6). The decomposition of PCG signals into IMF has allowed us to identify the frequency components of the IMF in which these sounds are found.

Słowa kluczowe

EN

intrinsic mode function; empirical mode decomposition; improved complete ensemble EMD; Shannon's entropy; phonocardiogram; heart sound

PL

funkcja trybu wewnętrznego; rozkład w trybie empirycznym; entropia Shannona; fonokardiogram

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 426--439
• Opis fizyczny: Bibliogr. 59 poz., tab., wykr.

Twórcy

autor

Altuve Miguel

• Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia

autor

Suárez Luis

• Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia

autor

Ardila Jeyson

• Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia

Bibliografia

• [1] Chan P-H, Wong C-K, Poh YC, Pun L, Leung WW-C, Wong Y-F, et al. Diagnostic performance of a smartphone-based photoplethysmographic application for atrial fibrillation screening in a primary care setting. J Am Heart Assoc 2016; 5(7):e003428. http://dx.doi.org/10.1161/JAHA.116.003428.
• [2] Lahdenoja O, Hurnanen T, Iftikhar Z, Nieminen S, Knuutila T, Saraste A, et al. Atrial fibrillation detection via accelerometer and gyroscope of a smartphone. IEEE J Biomed Health Informatics 2017;22(1):108–18. http://dx.doi.org/10.1109/JBHI.2017.2688473.
• [3] Thiyagaraja SR, Dantu R, Shrestha PL, Chitnis A, Thompson MA, Anumandla PT, et al. A novel heart-mobile interface for detection and classification of heart sounds. Biomed Signal Process Control 2018;45:313–24. http://dx.doi.org/10.1016/j.bspc.2018.05.008.
• [4] Chiauzzi E, DasMahapatra P, Cochin E, Bunce M, Khoury R, Dave P. Factors in patient empowerment: a survey of an online patient research network. Patient – Patient-Centered Outcomes Res 2016;9(6):511–23. http://dx.doi.org/10.1007/s40271-016-0171-2.
• [5] WHO. Cardiovascular diseases (CVDs); 2017, http://www.who.int/mediacentre/factsheets/fs317/en/.
• [6] Beratarrechea A, Diez-Canseco F, Irazola V, Miranda J, Ramirez-Zea M, Rubinstein A. Use of m-health technology for preventive interventions to tackle cardiometabolic conditions and other non-communicable diseases in latin America-challenges and opportunities. Prog Cardiovasc Dis 2016;58(6):661–73. http://dx.doi.org/10.1016/j.pcad.2016.03.003.
• [7] Nagel J. New diagnostic and technical aspects of fetal phonocardiography. Eur J Obstet Gynecol Reprod Biol 1986;23(5–6):295–303.
• [8] Topal T, Polat H, Güler I. Software development for the analysis of heartbeat sounds with labview in diagnosis of cardiovascular disease. J Med Syst 2008;32(5):409–21. http://dx.doi.org/10.1007/s10916-008-9146-8.
• [9] Phanphaisarn W, Roeksabutr A, Wardkein P, Koseeyaporn J, Yupapin P. Heart detection and diagnosis based on ECG and EPCG relationships. Med Dev (Auckland NZ) 2011;4:133.
• [10] Kalaivani V, Devi RL, Anusuyadevi V. Phonocardiographic signal and electrocardiographic signal analysis for the detection of cardiovascular diseases. Biosci Biotechnol Res Asia 2018;15(1):79–86. http://dx.doi.org/10.13005/bbra/2610.
• [11] Li Y, Wang X, Liu C, Li L, Yan C, Yao L, et al. Variability of cardiac electromechanical delay with application to the noninvasive detection of coronary artery disease. IEEE Access 2019;7:53115–24. http://dx.doi.org/10.1109/ACCESS.2019.2911555.
• [12] Springer DB, Tarassenko L, Clifford GD. Logistic regression- HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016;63(4):822–32.
• [13] Hall JE, Guyton A. Textbook of medical physiology. 13th ed. Elsevier Health Sciences; 2016.
• [14] Sacks MS, Yoganathan AP. Heart valve function: a biomechanical perspective. Philos Trans R Soc B: Biol Sci 2007;362(1484):1369–91.
• [15] Sacks MS, Merryman WD, Schmidt DE. On the biomechanics of heart valve function. J Biomech 2009;42 (12):1804–24.
• [16] Leatham A. Auscultation of the heart and phonocardiography. Churchill Livingstone; 1975.
• [17] Abbas AK, Bassam R. Phonocardiography signal processing. Synth Lect Biomed Eng 2009;4(1):1–194.
• [18] Naseri H, Homaeinezhad M. Computerized quality assessment of phonocardiogram signal measurement- acquisition parameters. J Med Eng Technol 2012;36(6):308–18.
• [19] Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas 2016;37(12):2181. http://dx.doi.org/10.1088/0967-3334/37/12/2181.
• [20] Gerbarg DS, Taranta A, Spagnuolo M, Hofler JJ. Computer analysis of phonocardiograms. Prog Cardiovasc Dis 1963;5 (4):393–405. http://dx.doi.org/10.1016/S0033-0620(63)80007-9.
• [21] Ari S, Sensharma K, Saha G. DSP implementation of a heart valve disorder detection system from a phonocardiogram signal. J Med Eng Technol 2008;32(2):122–32. http://dx.doi.org/10.1080/03091900600861574.
• [22] Moukadem A, Dieterlen A, Hueber N, Brandt C. A robust heart sounds segmentation module based on s-transform. Biomed Signal Process Control 2013;8(3):273–81. http://dx.doi.org/10.1016/j.bspc.2012.11.008.
• [23] Varghees VN, Ramachandran K, Soman K. Wavelet-based fundamental heart sound recognition method using morphological and interval features. Healthc Technol Lett 2018;5(3):81–7. http://dx.doi.org/10.1049/htl.2016.0109.
• [24] Liu Q, Wu X, Ma X. An automatic segmentation method for heart sounds. Biomed Eng Online 2018;17(1):106. http://dx.doi.org/10.1186/s12938-018-0538-9.
• [25] Sotaquirá M, Alvear D, Mondragón M. Phonocardiogram classification using deep neural networks and weighted probability comparisons. J Med Eng Technol 2018;42(7):510– 7. http://dx.doi.org/10.1080/03091902.2019.1576789.
• [26] Son G-Y, Kwon S, et al. Classification of heart sound signal using multiple features. Appl Sci 2018;8(12):2344. http://dx.doi.org/10.3390/app8122344.
• [27] Xiao B, Xu Y, Bi X, Zhang J, Ma X. Heart sounds classification using a novel 1-d convolutional neural network with extremely low parameter consumption. Neurocomputing 2019. http://dx.doi.org/10.1016/j.neucom.2018.09.101.
• [28] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3(March):1157–82. http://dx.doi.org/10.1162/153244303322753616.
• [29] Tang H, Dai Z, Jiang Y, Li T, Liu C. PCG classification using multidomain features and svm classifier. BioMed Res Int 2018. http://dx.doi.org/10.1155/2018/4205027.
• [30] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc Lond Ser A: Math Phys Eng Sci 1998;454(1971):903–95.
• [31] Huang NE, Wu Z. A review on Hilbert–Huang transform: method and its applications to geophysical studies. Rev Geophys 2008;46(2). http://dx.doi.org/10.1029/2007RG000228.
• [32] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A: Math Phys Eng Sci 1998;454(1971):903–95. http://dx.doi.org/10.1098/rspa.1998.0193.
• [33] Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009;1(1):1–41.
• [34] Torres ME, Colominas MA, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2011. p. 4144–7.
• [35] Colominas MA, Schlotthauer G, Torres ME. Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed Signal Process Control 2014;14:19–29.
• [36] Labate D, La Foresta F, Occhiuto G, Morabito FC, Lay- Ekuakille A, Vergallo P. Empirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: a comparison. IEEE Sens J 2013;13(7):2666–74. http://dx.doi.org/10.1109/JSEN.2013.2257742.
• [37] Motin MA, Karmakar CK, Palaniswami M. Selection of empirical mode decomposition techniques for extracting breathing rate from PPG. IEEE Signal Process Lett 2019;26 (4):592–6. http://dx.doi.org/10.1109/LSP.2019.2900923.
• [38] Ren Y, Suganthan P, Srikanth N. A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods. IEEE Trans Sustain Energy 2014;6(1):236–44. http://dx.doi.org/10.1109/TSTE.2014.2365580.
• [39] Kærgaard K, Jensen SH, Puthusserypady S. A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ecg denoising. Biomed Signal Process Control 2016;25:178–87. http://dx.doi.org/10.1016/j.bspc.2015.11.012.
• [40] Zhan L, Li C. A comparative study of empirical mode decomposition-based filtering for impact signal. Entropy 2016;19(1):13. http://dx.doi.org/10.3390/e19010013.
• [41] Hassan AR, Subasi A. Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed 2016;136:65–77. http://dx.doi.org/10.1016/j.cmpb.2016.08.013.
• [42] Wang L, Li X, Ma C, Bai Y. Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. J Hydrol 2019;573:733–45. http://dx.doi.org/10.1016/j.jhydrol.2019.03.101.
• [43] Charleston-Villalobos S, Aljama-Corrales A, Gonzalez- Camarena R. Analysis of simulated heart sounds by intrinsic mode functions. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2006. p. 2848–51.
• [44] Ari S, Saha G. Classification of heart sounds using empirical mode decomposition based features. Int J Med Eng Informatics 2008;1(1):91–108. http://dx.doi.org/10.1504/IJMEI.2008.019473.
• [45] Papadaniil CD, Hadjileontiadis LJ. Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health Informatics 2013;18(4):1138–52. http://dx.doi.org/10.1109/JBHI.2013.2294399.
• [46] Jimenez JA, Becerra MA, Delgado-Trejos E. Heart murmur detection using ensemble empirical mode decomposition and derivations of the MEL-frequency cepstral coefficients on 4-area phonocardiographic signals. Computing in Cardiology 2014. IEEE; 2014. p. 493–6.
• [47] Jusak J, Puspasari I, Susanto P. Heart murmurs extraction using the complete ensemble empirical mode decomposition and the Pearson distance metric. 2016 International Conference on Information & Communication Technology and Systems (ICTS). IEEE; 2016. p. 140–5.
• [48] Botha J, Scheffer C, Lubbe W, Doubell A. Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition. Aust Phys Eng Sci Med 2010;33(2):171–83. http://dx.doi.org/10.1007/s13246-010-0021-9.
• [49] Zheng Y, Guo X, Qin J, Xiao S. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput Methods Programs Biomed 2015;122(3):372–83. http://dx.doi.org/10.1016/j.cmpb.2015.09.001.
• [50] Gavrovska A, Slavkovic´ M, Reljin I, Reljin B. Application of wavelet and EMD-based denoising to phonocardiograms. International Symposium on Signals, Circuits and Systems ISSCS2013. IEEE; 2013. p. 1–4.
• [51] Sun H, Chen W, Gong J. An improved empirical mode decomposition-wavelet algorithm for phonocardiogram signal denoising and its application in the first and second heart sound extraction. 2013 6th International Conference on Biomedical Engineering and Informatics. IEEE; 2013. p. 187–91. http://dx.doi.org/10.1109/BMEI.2013.6746931.
• [52] Salman AH, Ahmadi N, Mengko R, Langi AZ, Mengko TL. Empirical mode decomposition (EMD) based denoising method for heart sound signal and its performance analysis. Int J Electr Comput Eng (2088-8708) 2016;6(5):2197–204.
• [53] Mondal A, Bhattacharya P, Saha G. Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique. J Med Eng Technol 2011;35 (6–7):344–53. http://dx.doi.org/10.3109/03091902.2011.595529.
• [54] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23):e215–20. http://dx.doi.org/10.1161/01.CIR.101.23.e215.
• [55] Colominas MA, Schlotthauer G, TORRES ME, Flandrin P. Noise-assisted EMD methods in action. Adv Adapt Data Anal 2012;4(4):1250025. http://dx.doi.org/10.1142/S1793536912500252.
• [56] Oppenheim AV, Schafer RW. Discrete-time signal processing. Pearson Education; 2014.
• [57] Chizner MA. Cardiac auscultation: rediscovering the lost art. Curr Probl Cardiol 2008;33(7):326–408. http://dx.doi.org/10.1016/j.cpcardiol.2008.03.003.
• [58] Arnott P, Pfeiffer G, Tavel M. Spectral analysis of heart sounds: relationships between some physical characteristics and frequency spectra of first and second heart sounds in normals and hypertensives. J Biomed Eng 1984;6(2):121–8. http://dx.doi.org/10.1016/0141-5425(84)90054-2.
• [59] Rangayyan RM, Lehner RJ. Phonocardiogram signal analysis: a review. Crit Rev Biomed Eng 1987;15(3):211–36.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).