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A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks

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Abstract

Phonocardiogram (PCG) plays an important role in evaluating many cardiac abnormalities, such as the valvular heart disease, congestive heart failure and anatomical defects of the heart. However, effective cardiac auscultation requires trained physicians whose work is tough, laborious and subjective. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) detection of PCG recordings without any segmentation of heart sound signals. Hybrid signal processing and artificial intelligence tools, including tunable Q-factor wavelet transform (TQWT), variational mode decomposition (VMD), phase space reconstruction (PSR) and neural networks, are utilized to extract representative features in order to model, identify and detect abnormal patterns in the dynamics of PCG system caused by heart disease. First, heart sound signal is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Second, VMD is employed to decompose the subband of the heart sound signal into different intrinsic modes, in which the first four intrinsic modes contain the majority of the heart sound signal’s energy and are considered to be the predominant intrinsic modes. They are selected to construct the reference variable for analysis. Third, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear PCG system dynamics are preserved. Three-dimensional PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal and abnormal heart sound signals. Finally, PhysioNet/CinC Challenge heart sound database is used for evaluation and the synthetic minority over-sampling technique method is applied to balance the datasets. By using the 10-fold cross-validation style, experimental results demonstrate that the proposed features with dynamical neural networks based classifier yield classification performance with sensitivity, specificity, overall score and accuracy values of 97.73\(\%\), 98.05\(\%\), 97.89\(\%\), and 97.89\(\%\), respectively. The results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic anomaly detection in the clinical application.

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References

  • Adiban M, BabaAli B, Shehnepoor S (2019) I-vector based features embedding for heart sound classification. ar**v preprint ar**v:1904.11914

  • Alam U, Asghar O, Khan SQ, Hayat S, Malik RA (2010) Cardiac auscultation: an essential clinical skill in decline. Br J Cardiol 17(1):8

    Google Scholar 

  • Babu KA, Ramkumar B, Manikandan MS (2018) Automatic identification of S1 and S2 heart sounds using simultaneous PCG and PPG recordings. IEEE Sens J 18(22):9430–9440

    Google Scholar 

  • Beritelli F, Capizzi G, Sciuto GL, Napoli C, Scaglione F (2018) Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks. Biomed Eng Lett 8(1):77–85

    Google Scholar 

  • Boutana D, Benidir M, Barkat B (2011) Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis. IET Signal Process 5(6):527–537

    MathSciNet  Google Scholar 

  • Bozkurt B, Germanakis I, Stylianou Y (2018) A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection. Comput Biol Med 100:132–143

    Google Scholar 

  • Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    MATH  Google Scholar 

  • Cheema A, Singh M (2019) An application of phonocardiography signals for psychological stress detection using non-linear entropy based features in empirical mode decomposition domain. Appl Soft Comput 77:24–33

    Google Scholar 

  • Chen B, He Z, Chen X, Cao H, Cai G, Zi Y (2011) A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis. Meas Sci Technol 22(5):055704

    Google Scholar 

  • Chen M, Fang Y, Zheng X (2014) Phase space reconstruction for improving the classification of single trial EEG. Biomed Signal Process Control 11:10–16

    Google Scholar 

  • Clifford GD, Liu C, Moody B, Springer D, Silva I, Li Q, Mark RG (2016) Classification of normal/abnormal heart sound recordings: the PhysioNet/computing in cardiology challenge 2016. In: 2016 Computing in cardiology conference (CinC), pp 609–612

  • Das S, Pal S, Mitra M (2019) Supervised model for Cochleagram feature based fundamental heart sound identification. Biomed Signal Process Control 52:32–40

    Google Scholar 

  • Deng SW, Han JQ (2016) Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Future Gener Comput Syst 60:13–21

    Google Scholar 

  • Dominguez-Morales JP, Jimenez-Fernandez AF, Dominguez-Morales MJ, Jimenez-Moreno G (2017) Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE Trans Biomed Circuits Syst 12(1):24–34

    Google Scholar 

  • Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    MathSciNet  MATH  Google Scholar 

  • Feng W, Dauphin G, Huang W, Quan Y, Bao W, Wu M, Li Q (2019) Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 12(7):2159–2169

    Google Scholar 

  • Gavrovska A, Zajic G, Bogdanovic V, Rel** I, Rel** B (2016) Paediatric heart sound signal analysis towards classification using multifractal spectra. Physiol Meas 37(9):1556

    MATH  Google Scholar 

  • Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2003) PhysioBank, physioToolkit, and physioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Google Scholar 

  • Gorinevsky D (1995) On the persistency of excitation in radial basis function network identification of nonlinear systems. IEEE Trans Neural Netw 6(5):1237–1244

    Google Scholar 

  • Hamidi M, Ghassemian H, Imani M (2018) Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control 39:351–359

    Google Scholar 

  • Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 137:247–259

    Google Scholar 

  • Hassani K, Bajelani K, Navidbakhsh M, Doyle DJ, Taherian F (2014) Heart sound segmentation based on homomorphic filtering. Perfusion 29(4):351–359

    Google Scholar 

  • Huang B, Kunoth A (2013) An optimization based empirical mode decomposition scheme. J Comput Appl Math 240:174–183

    MathSciNet  MATH  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454(1971):903–995

    MathSciNet  MATH  Google Scholar 

  • Humayun AI, Ghaffarzadegan S, Ansari MI, Feng Z, Hasan T (2020) Towards domain invariant heart sound abnormality detection using learnable filterbanks. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2020.2970252

    Article  Google Scholar 

  • Jain PK, Tiwari AK (2018) A robust algorithm for segmentation of phonocardiography signal using tunable quality wavelet transform. J Med Biol Eng 38(3):396–410

    Google Scholar 

  • Johnson MT, Povinelli RJ, Lindgren AC, Ye J, Liu X, Indrebo KM (2005) Time-domain isolated phoneme classification using reconstructed phase spaces. IEEE Trans Speech Audio Process 13(4):458–466

    Google Scholar 

  • Lal GJ, Gopalakrishnan EA, Govind D (2018) Epoch estimation from emotional speech signals using variational mode decomposition. Circuits Syst Signal Process 37(8):3245–3274

    MathSciNet  MATH  Google Scholar 

  • Langley P, Murray A (2017) Heart sound classification from unsegmented phonocardiograms. Physiol Meas 38(8):1658

    Google Scholar 

  • Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Methods Programs Biomed 116(1):10–25

    Google Scholar 

  • Li Y, Xu M, Wei Y, Huang W (2015) Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition. Mech Mach Theory 94:9–27

    Google Scholar 

  • Li J, Ke L, Du Q, Ding X, Chen X, Wang D (2019a) Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine. IEEE Access 7:179339–179348

    Google Scholar 

  • Li J, Ke L, Du Q (2019b) Classification of heart sounds based on the wavelet fractal and twin support vector machine. Entropy 21(5):472

    MathSciNet  Google Scholar 

  • Liang QZ, Guo XM, Zhang WY, Dai WD, Zhu XH (2015) Identification of heart sounds with arrhythmia based on recurrence quantification analysis and Kolmogorov entropy. J Med Biol Eng 35(2):209–217

    Google Scholar 

  • Liu L, Wang H, Wang Y, Tao T, Wu X (2010) Feature analysis of heart sound based on the improved Hilbert-Huang transform. In: 3rd IEEE international conference on computer science and information technology, pp 378–381

  • Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Syed Z (2016) An open access database for the evaluation of heart sound algorithms. Physiol Meas 37(12):2181

    Google Scholar 

  • Merigó JM, Casanovas M (2011) Induced aggregation operators in the Euclidean distance and its application in financial decision making. Expert Syst Appl 38:7603–7608

    Google Scholar 

  • Mert A (2016) ECG feature extraction based on the bandwidth properties of variational mode decomposition. Physiol Meas 37(4):530

    Google Scholar 

  • Messner E, Zohrer M, Pernkopf F (2018) Heart sound segmentation-an event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng 65(9):1964–1974

    Google Scholar 

  • Michael S (2005) Applied nonlinear time series analysis: applications in physics, physiology and finance (Vol 52). World Scientific, Singapore

    Google Scholar 

  • Mishra M, Banerjee S, Thomas DC, Dutta S, Mukherjee A (2018) Detection of third heart sound using variational mode decomposition. IEEE Trans Instrum Meas 67(7):1713–1721

    Google Scholar 

  • Mishra M, Pratiher S, Menon H, Mukherjee A (2020) Identification of S1 and S2 heart sounds using spectral and convex hull features. IEEE Sens J 20(8):4311–4320

    Google Scholar 

  • Nishad A, Pachori RB, Acharya UR (2018) Application of TQWT based filter-bank for sleep apnea screening using ECG signals. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0867-3

    Article  Google Scholar 

  • Nogueira DM, Ferreira CA, Gomes EF, Jorge AM (2019) Classifying heart sounds using images of Motifs, MFCC and temporal features. J Med Syst 43(6):168

    Google Scholar 

  • Noman FM, Salleh SH, Ting CM, Samdin SB, Ombao H, Hussain H (2020) A Markov-switching model approach to heart sound segmentation and classification. IEEE J Biomed Health Inform 24(3):705–716

    Google Scholar 

  • Papadaniil CD, Hadjileontiadis LJ (2013) Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health Inform 18(4):1138–1152

    Google Scholar 

  • Park C, Looney D, Van Hulle MM, Mandic DP (2011) The complex local mean decomposition. Neurocomputing 74(6):867–875

    Google Scholar 

  • Patidar S, Pachori RB (2014) Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Syst Appl 41(16):7161–7170

    Google Scholar 

  • Patidar S, Pachori RB, Upadhyay A, Acharya UR (2017) An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Appl Soft Comput 50:71–78

    Google Scholar 

  • Potes C, Parvaneh S, Rahman A, Conroy B (2016) Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. In: 2016 computing in cardiology conference (CinC), pp 621–624

  • Rivera WA, Xanthopoulos P (2016) A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets. Expert Syst Appl 66:124–135

    Google Scholar 

  • Safara F, Doraisamy S, Azman A, Jantan A, Ramaiah ARA (2013) Multi-level basis selection of wavelet packet decomposition tree for heart sound classification. Comput Biol Med 43(10):1407–1414

    Google Scholar 

  • Salman AH, Ahmadi N, Mengko R, Langi AZ, Mengko TL (2016) Empirical mode decomposition (EMD) based denoising method for heart sound signal and its performance analysis. Int J Electr Comput Eng 6(5):1–8

    Google Scholar 

  • Sauer T, Yorke JA, Casdagli M (1991) Embedology. J Stat Phys 65(3–4):579–616

    MathSciNet  MATH  Google Scholar 

  • Selesnick I (2011) Wavelet transform with tunable Q-factor. IEEE Trans Signal Process 59(8):3560–3575

    MathSciNet  MATH  Google Scholar 

  • Shervegar MV, Bhat GV (2018) Heart sound classification using Gaussian mixture model. Porto Biomed J 3(1):e4

    Google Scholar 

  • Singh SA, Majumder S (2019) Classification of unsegmented heart sound recording using KNN classifier. J Mech Med Biol 19(04):1950025

    Google Scholar 

  • Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrol 258(1–4):149–162

    Google Scholar 

  • Som A, Krishnamurthi N, Venkataraman V, Turaga P (2016) Attractor-shape descriptors for balance impairment assessment in Parkinson’s disease. In: IEEE conference on engineering in medicine and biology society, pp 3096–3100

  • Springer DB, Tarassenko L, Clifford GD (2015) Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 63(4):822–832

    Google Scholar 

  • Sujadevi VG, Mohan N, Kumar SS, Akshay S, Soman KP (2019) A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition. Biomed Eng Lett 9(4):413–424

    Google Scholar 

  • Sun S, Jiang Z, Wang H, Fang Y (2014) Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. Comput Methods Programs Biomed 114(3):219–230

    Google Scholar 

  • Sun Y, Li J, Liu J, Chow C, Sun B, Wang R (2015) Using causal discovery for feature selection in multivariate numerical time series. Mach Learn 101(1–3):377–395

    MathSciNet  MATH  Google Scholar 

  • Takens F (1981) Detecting strange attractors in turbulence. In: Rand DA, Young L-S (eds) Dynamical systems and turbulence, Warwick 1980. Springer, Berlin, pp 366–381

    Google Scholar 

  • Varghees VN, Ramachandran KI (2014) A novel heart sound activity detection framework for automated heart sound analysis. Biomed Signal Process Control 13:174–188

    Google Scholar 

  • Varghees VN, Ramachandran KI (2017) Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope. IEEE Sens J 17(12):3861–3872

    Google Scholar 

  • Venkataraman V, Turaga P (2016) Shape distributions of nonlinear dynamical systems for video-based inference. IEEE Trans Pattern Anal Mach Intell 38(12):2531–2543

    Google Scholar 

  • Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Networks 17(1):130–146

    Google Scholar 

  • Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617–630

    Google Scholar 

  • Wang C, Hill DJ (2009) Deterministic learning theory for identification, recognition and control. CRC Press, Boca Raton

    Google Scholar 

  • Wang Y, Liu F, Jiang Z, He S, Mo Q (2017) Complex variational mode decomposition for signal processing applications. Mech Syst Signal Process 86:75–85

    Google Scholar 

  • Wang Q, Zhou X, Wang C, Liu Z, Huang J, Zhou Y, Cheng JZ (2019) WGAN-based synthetic minority over-sampling technique: improving semantic fine-grained classification for lung nodules in CT images. IEEE Access 7:18450–18463

    Google Scholar 

  • Whitaker BM, Suresha PB, Liu C, Clifford GD, Anderson DV (2017) Combining sparse coding and time-domain features for heart sound classification. Physiol Meas 38(8):1701

    Google Scholar 

  • **ao B, Xu Y, Bi X, Zhang J, Ma X (2019) Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.09.101

    Article  Google Scholar 

  • **e Y, **e K, **e S (2019) Underdetermined blind source separation for heart sound using higher-order statistics and sparse representation. IEEE Access 7:87606–87616

    Google Scholar 

  • Xu B, Jacquir S, Laurent G, Bilbault JM, Binczak S (2013) Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions. In: 35th annual international conference of the IEEE engineering in medicine and biology society, pp 3274–3277

  • Xue YJ, Cao JX, Wang DX, Du HK, Yao Y (2016) Application of the variational-mode decomposition for seismic time-frequency analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 9(8):3821–3831

    Google Scholar 

  • Zhang WJ, Han JQ, Deng SW (2017) Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process Control 32:20–28

    Google Scholar 

  • Zhang WJ, Han JQ, Deng SW (2019) Abnormal heart sound detection using temporal quasi-periodic features and long short-term memory without segmentation. Biomed Signal Process Control 53:101560

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61773194), by the Natural Science Foundation of Fujian Province (Grant No. 2018J01542), by the Program for New Century Excellent Talents in Fujian Province University and by the Training Program of Innovation and Entrepreneurship for Undergraduates (Grant No. 201911312009).

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  1. School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, People’s Republic of China

    Wei Zeng, Jian Yuan, Qinghui Wang, Fenglin Liu & Ying Wang

  2. Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Chengzhi Yuan

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  1. Wei Zeng
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Zeng, W., Yuan, J., Yuan, C. et al. A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks. Artif Intell Rev 54, 1613–1647 (2021). https://doi.org/10.1007/s10462-020-09875-w

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