iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S00034-021-01916-1
An Effective CAD System for Heart Sound Abnormality Detection | Circuits, Systems, and Signal Processing Skip to main content

Advertisement

Log in

An Effective CAD System for Heart Sound Abnormality Detection

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

The study of heart sound signals is considered a helpful approach for monitoring heart diseases and for assessing heart hemodynamic condition. In fact, several cardiac disorders are tangible in heart sound signal characteristics such as intensity, time relations and spectral content. To assist cardiologists in cardiovascular pathology screening and prevention, a computer-aided system able to segment and classify phonocardiogram records is proposed. After the detection of the fundamental heart sounds, systole and diastole, various features are extracted and a correlation analysis for avoiding redundancy and for quantify the feature discrimination capacity is made. The performance of the conceived system is evaluated considering the accuracy, the sensitivity and the specificity in classifying heart sound signals as normal or abnormal and is tested adopting the entire collection of records provided by the PhysioNet/CinC Challenge 2016 database. The obtained results show the method ability to aid the interpretation of specialists during their clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. M. Abdollahpur, A. Ghaffari, S. Ghiasi, M.J. Mollakazemi, Detection of pathological heart sounds. Physiol. Meas. 38(8), 161–1630 (2017). https://doi.org/10.1088/1361-6579/aa7840

    Article  Google Scholar 

  2. Z. Abduha, E.A. Nehary, M.A. Waheda, Y.M. Kadah, Classification of heart sounds using fractional Fourier transform based Mel-frequency spectral coefficients and traditional classifiers. Biomed. Signal Process. Control (2020). https://doi.org/10.1016/j.bspc.2019.101788

    Article  Google Scholar 

  3. E.N. Arunkumar, A.F. Hussein, M. Solarte, G. Ramirez-Gonzales, Spectral fault recovery analysis revisited with normal and abnormal heart sound signals. IEEE Access (2018). https://doi.org/10.1109/ACCESS.2018.2876119

    Article  Google Scholar 

  4. A.N. Caleb, B. Roda, Modern-day cardiac auscultatory teaching and its role alongside echocardiography. BC Med. J. 61, 128–130 (2019)

    Google Scholar 

  5. J.F. Chen, X. Dang, Heart sound analysis based on extended features and related factors, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (2019). https://doi.org/10.1109/SSCI44817.2019.9003008

  6. T.E. Chen, S.I. Yang, L.T. Ho, K.H. Tsai, Y.H. Chen, Y.F. Chang, Y.H. Lai, S.S. Wang, Y. Tsao, C.C. Wu, S1 and S2 heart sound recognition using deep neural networks. IEEE Trans. Biomed. Eng. (2017). https://doi.org/10.1109/TBME.2016.2559800

    Article  Google Scholar 

  7. T.H. Chowdhury, K.N. Poudel, Y. Hu, Time-frequency analysis, denoising, compression, segmentation, and classification of PCG signals. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.3020806

    Article  Google Scholar 

  8. G.D. Clifford, C. Liu, B. Moody, J. Millet, S. Schmidt, Q. Li, I. Silva, R.G. Mark, Recent advances in heart sound analysis. Physiol. Meas. (2017). https://doi.org/10.1088/1361-6579/aa7ec8

    Article  Google Scholar 

  9. M. D’Aloia, A. Longo, M. Rizzi, Noisy ECG signal analysis for automatic peak detection. Information (2019). https://doi.org/10.3390/info10020035

    Article  Google Scholar 

  10. M. D'Aloia, A. Longo, R. Russo, S. Stanisci, D. Amendolare, M. Rizzi, M. Vessia, F. Lomastro, An innovative LPWA network scheme to increase system reliability in remote monitoring, in 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) (2017). https://doi.org/10.1109/EESMS.2017.8052684

  11. M. Deng, T. Meng, J. Cao, S. Wang, J. Zhang, H. Fan, Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw. (2020). https://doi.org/10.1016/j.neunet.2020.06.015

    Article  Google Scholar 

  12. S.W. Deng, J.Q. Han, Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Futur. Gener. Comput. Syst. (2016). https://doi.org/10.1016/j.future.2016.01.010

    Article  Google Scholar 

  13. P. Dhar, S. Dutta, V. Mukherjee, Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification. Biomed. Signal Process. Control (2021). https://doi.org/10.1016/j.bspc.2020.102142

    Article  Google Scholar 

  14. A. Giorgio, M. Rizzi, C. Guaragnella, Efficient detection of ventricular late potentials on ECG signals based on wavelet denoising and SVM classification. Information (2019). https://doi.org/10.3390/info10110328

    Article  Google Scholar 

  15. A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation (2000). https://doi.org/10.1161/01.cir.101.23.e215

    Article  Google Scholar 

  16. C. Guaragnella, M. Rizzi, A. Giorgio, Marginal component analysis of ECG signals for beat-to-beat detection of ventricular late potentials. Electronics (2019). https://doi.org/10.3390/electronics8091000

    Article  Google Scholar 

  17. C. Guaragnella, M. Rizzi, Simple and accurate border detection algorithm for melanoma computer aided diagnosis. Diagnostics (2020). https://doi.org/10.3390/diagnostics10060423

    Article  Google Scholar 

  18. A. Had, K. Sabri, M. Aoutoul, Detection of heart valves closure instants in phonocardiogram signals. Wirel. Pers. Commun. (2020). https://doi.org/10.1007/s11277-020-07116-5

    Article  Google Scholar 

  19. M.E. Karar, S.H. El-Khafif, M.A. El-Brawany, Automated diagnosis of heart sounds using rule-based classification tree. J. Med. Syst. (2017). https://doi.org/10.1007/s10916-017-0704-9

    Article  Google Scholar 

  20. J. Kaushik, A. Misal, Segmentation of phonocardiograms signal. Int. J. Eng. Res. Adv. Technol. (2018). https://doi.org/10.31695/IJERAT.2018.3284

    Article  Google Scholar 

  21. P.T. Krishnan, P. Balasubramanian, S. Umapathy, Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network. Phys. Eng. Sci. Med. (2020). https://doi.org/10.1007/s13246-020-00851-w

    Article  Google Scholar 

  22. M. Lam, T. Lee, P. Boey, W. Ng, H. Hey, K. Ho, P. Cheong, Factors influencing cardiac auscultation proficiency in physician trainees. Singapore Med. J. 46(1), 11–14 (2005)

    Google Scholar 

  23. F. Li, H. Tang, S. Shang, K. Mathiak, F. Cong, Classification of heart sounds using convolutional neural network. Appl. Sci. (2020). https://doi.org/10.3390/app10113956

    Article  Google Scholar 

  24. J. Li, L. Ke, Q. Du, X. Ding, X. Chen, D. Wang, Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine. IEEE Access (2019). https://doi.org/10.1109/ACCESS.2019.2959081

    Article  Google Scholar 

  25. J. Li, L. Ke, Q. Du, Classification of heart sounds based on the wavelet fractal and twin support vector machine. Entropy (2019). https://doi.org/10.3390/e21050472

    Article  MathSciNet  Google Scholar 

  26. C. Liu, D. Springer, Q. Li, B. Moody, R.A. Juan, F.J. Chorro, F. Castells, J.M. Roig, I. Silva, A.E. Johnson, Z. Syed, S.E. Schmidt, C.D. Papadaniil, L. Hadjileontiadis, H. Naseri, A. Moukadem, A. Dieterlen, C. Brandt, H. Tang, M. Samieinasab, M.R. Samieinasab, R. Sameni, R.G. Mark, G.D. Clifford, An open access database for the evaluation of heart sound algorithms. Physiol. Meas. (2016). https://doi.org/10.1088/0967-3334/37/12/2181

    Article  Google Scholar 

  27. A. Longo, M. Rizzi, D. Amendolare, S. Stanisci, R. Russo, G. Cice, M. D'Aloia, Localization and monitoring system based on BLE fingerprint method, in CEUR Workshop Proceedings—Workshop on Artificial Intelligence with Application in Health (WAIAH 2017), vol. 1982 (2017), pp. 33–39

  28. P. Lubaib, K.V. Ahammed Muneer, The heart defect analysis based on PCG signals using pattern recognition techniques. Procedia Technol. (2016). https://doi.org/10.1016/j.protcy.2016.05.225

    Article  Google Scholar 

  29. A. Moukadem, A. Dieterlen, N. Hueber, C. Brandt, A robust heart sounds segmentation module based on S-transform. Biomed. Signal Process. Control (2013). https://doi.org/10.1016/j.bspc.2012.11.008

    Article  Google Scholar 

  30. V. Nivitha Varghees, K.I. Ramachandran, Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope. IEEE Sens. J. (2017). https://doi.org/10.1109/JSEN.2017.2694970

    Article  Google Scholar 

  31. D.M. Nogueira, C.A. Ferreira, E.F. Gomes, A.M. Jorge, Classifying heart sounds using images of motifs, MFCC and temporal features. J. Med. Syst. (2019). https://doi.org/10.1007/s10916-019-1286-5

    Article  Google Scholar 

  32. F. Noman, S. Salleh, C. Ting, S.B. Samdin, H. Ombao, H. Hussain, A Markov-switching model approach to heart sound segmentation and classification. IEEE J. Biomed. Health Inform. (2020). https://doi.org/10.1109/JBHI.2019.2925036

    Article  Google Scholar 

  33. J. Oliveira, T. Mantadelis, F. Renna, P. Gomes, M. Coimbra, On modifying the temporal modeling of HSMMs for pediatric heart sound segmentation, in 2017 IEEE International Workshop on Signal Processing Systems (SiPS) (2017). https://doi.org/10.1109/SiPS.2017.8110004

  34. J. Oliveira, F. Renna, M.T. Coimbra, Adaptive sojourn time HSMM for heart sound segmentation. IEEE J. Biomed. Health Inform. (2019). https://doi.org/10.1109/JBHI.2018.2841197

    Article  Google Scholar 

  35. İ. Özkan, A. Yilmaz, G. Çelebı, Hybrid segmentation algorithm using Mel-frequency cepstrum and wavelet transform for phonocardiography records, in 27th Signal Processing and Communications Applications Conference (SIU) (2019). https://doi.org/10.1109/SIU.2019.8806586

  36. M. Palinka, G. De Luca Canto, L.A. Rodrigues, C. Bataglion, S. Siéssere, M. Semprini, S.C. Regalo, The real role of sensitivity, specificity and predictive values in the clinical assessment. J. Clin. Sleep Med. (2016). https://doi.org/10.5664/jcsm.5506

    Article  Google Scholar 

  37. Z. Ren, N. Cummins, V. Pandit, J. Han, K. Qian, B. Schuller, Learning image-based representations for heart sound classification, in 2018 International Conference on Digital Health (DH’18) (2018). https://doi.org/10.1145/3194658.3194671

  38. F. Renna, J. Oliveira, M.T. Coimbra, Deep convolutional neural networks for heart sound segmentation. IEEE J. Biomed. Health Inform. (2019). https://doi.org/10.1109/JBHI.2019.2894222

    Article  Google Scholar 

  39. M. Rizzi, M. Daloia, G. Cice, Computer aided evaluation (CAE) of morphologic changes in pigmented skin lesions, in New Trends in Image Analysis and Processing-ICIAP 2015 Workshops. Lecture Notes in Computer Science, vol. 9281, ed. by V. Murino, E. Puppo, D. Sona, M. Cristani, C. Sansone (Springer, Cham, 2015), pp. 250–257. https://doi.org/10.1007/978-3-319-23222-5_31

    Chapter  Google Scholar 

  40. M. Rizzi, M. D’Aloia, A. Longo, Digital watermarking for healthcare: a survey of ECG watermarking methods in telemedicine. Int. J. Comput. Sci. Eng. (2020). https://doi.org/10.1504/IJCSE.2020.111432

    Article  Google Scholar 

  41. M. Rizzi, M. D’Aloia, Computer aided system for breast cancer diagnosis. Biomed. Eng. Appl. Basis Commun. (2014). https://doi.org/10.4015/S1016237214500331

    Article  Google Scholar 

  42. M. Rizzi, C. Guaragnella, Skin lesion segmentation using image bit-plane multilayer approach. Appl. Sci. (2020). https://doi.org/10.3390/app10093045

    Article  Google Scholar 

  43. D.S.V. Sankar, L.P. Roy, Principal component analysis (PCA) approach to segment primary components from pathological phonocardiogram, in 2014 International Conference on Communication and Signal Processing (2014). https://doi.org/10.1109/ICCSP.2014.6949976

  44. P. Sharma, S.A. Imtiaz, E. Rodriguez-Villegas, An algorithm for heart rate extraction from acoustic recordings at the neck. IEEE Trans. Biomed. Eng. (2019). https://doi.org/10.1109/TBME.2018.2836187

    Article  Google Scholar 

  45. K. Shi, S. Schellenberger, F. Michler, T. Steigleder, A. Malessa, F. Lurz, C. Ostgathe, R. Weigel, A. Koelpin, Automatic signal quality index determination of radar-recorded heart sound signals using ensemble classification. IEEE Trans. Biomed. Eng. (2020). https://doi.org/10.1109/TBME.2019.2921071

    Article  Google Scholar 

  46. V. Singh, R.R. Watson, Lifestyle features and heart disease, in Lifestyle in Heart Health and Disease. ed. by R.R. Watson, S. Zibadi (Academic Press, London, 2018), pp. 223–226. https://doi.org/10.1016/B978-0-12-811279-3.00017-3

    Chapter  Google Scholar 

  47. S.A. Singh, S. Majumder, Short unsegmented PCG classification based on ensemble classifier. Turk. J. Electr. Eng. Comput. Sci. (2020). https://doi.org/10.3906/elk-1905-165

    Article  Google Scholar 

  48. S.A.J. Singh, T.G. Meiteia, S. Majumder, Short PCG classification based on deep learning, in Deep Learning Techniques for Biomedical and Health Informatics. ed. by B. Agarwal, V. Balas, L. Jain, R. Poonia, M. Sharma (Elsevier, New York, 2020). https://doi.org/10.1016/B978-0-12-819061-6.00006-9

    Chapter  Google Scholar 

  49. S.L. Strunic, F. Rios-Gutierrez, R. Alba-Flores, G. Nordehn, S. Burns, Detection and classification of cardiac murmurs using segmentation techniques and artificial neural networks, in 2007 IEEE Symposium on Computational Intelligence and Data Mining (2007), pp. 397–404

  50. V.G. Sujadevi, K.P. Soman, R. Vinayakumar, A.U. Prem Sankar, Deep models for phonocardiography (PCG) classification, in 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT) (2017). https://doi.org/10.1109/INTELCCT.2017.8324047]

  51. H. Tang, Z. Dai, Y. Jiang, T. Li, C. Liu, PCG classification using multidomain features and SVM classifier. Biomed. Res. Int. (2018). https://doi.org/10.1155/2018/420502

    Article  Google Scholar 

  52. R. Trevethan, Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front. Public Health (2017). https://doi.org/10.3389/fpubh.2017.00307

    Article  Google Scholar 

  53. K.J. van Stralen, V.S. Stel, J.B. Reitsma, F.W. Dekker, C. Zoccali, K.J. Jage, Diagnostic methods I: sensitivity, specificity, and other measures of accuracy. Kidney Int. (2009). https://doi.org/10.1038/ki.2009.92

    Article  Google Scholar 

  54. B. Xiao, Y. Xua, X. Bi, J. Zhang, X. Mac, Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing (2020). https://doi.org/10.1016/j.neucom.2018.09.101

    Article  Google Scholar 

  55. A. Yadav, M.K. Dutta, C.M. Travieso, J.B. Alonso, Automatic classification of normal and abnormal PCG recording heart sound recording using Fourier transform, in 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) (2018). https://doi.org/10.1109/IWOBI.2018.8464131

  56. W. Zhang, J. Han, S. Deng, Heart sound classification based on scaled spectrogram and tensor decomposition. Expert Syst. Appl. (2017). https://doi.org/10.1016/j.eswa.2017.05.014

    Article  Google Scholar 

  57. W. Zhang, J. Han, Towards heart sound classification without segmentation using convolutional neural network, in 2017 Computing in Cardiology (CinC) (2017). https://doi.org/10.22489/CinC.2017.254-164

Download references

Funding

The research was supported by Politecnico di Bari—FRA.

Author information

Authors and Affiliations

Authors

Contributions

Authors have contributed to the paper in equal measure.

Corresponding author

Correspondence to Cataldo Guaragnella.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giorgio, A., Guaragnella, C. & Rizzi, M. An Effective CAD System for Heart Sound Abnormality Detection. Circuits Syst Signal Process 41, 2845–2870 (2022). https://doi.org/10.1007/s00034-021-01916-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01916-1

Keywords

Navigation