Skip to main content

Advertisement

SpringerLink
Log in

An efficient heart murmur recognition and cardiovascular disorders classification system

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

The problem addressed in this work is the detection of a heart murmur and the classification of the associated cardiovascular disorder based on the heart sound signal. For this purpose, a dataset of Phonocardiogram (PCG) signals is acquired using baseline conditions. The dataset is acquired from 283 volunteers using Littman 3200 electronic stethoscope for a normal and four different types of heart murmurs. The samples are labelled and validated through echocardiography test of each participating volunteer. For feature extraction, normalized average Shannon energy with time-domain characteristics of heart sound signal is exploited to segment the PCG signal into its components. To improve the quality of the features, in contrast to the previous methods, all systole and diastole intervals are utilized to extract 50 Mel-Frequency Cepstrum Coefficients (MFCC) based features. Then, the iterative backward elimination method is used to identify and remove the redundant features to reduce the complexity in order to conceive a computationally tractable system. An MFCC feature vector of dimension 26 is selected for training seven different types of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) based classifiers for detection and classification of cardiovascular disorders. Fivefold cross-validation and 20% data holdout validation schemes are used for testing the classifiers. Classification accuracy of 92.6% is achieved using selected features and medium Gaussian SVM classifier. The learning curves show a good bias-variance trade-off indicating a well-fitted and generalized model for making future predictions.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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. WHO (2017) World Health Ranking. http://www.worldlifeexpectancy.com/country-health-profile/pakistan

  2. Kwak C, Kwon O-W (2012) Cardiac disorder classification by heart sound signals using murmur likelihood and hidden markov model state likelihood. IET Signal Process 6(4):326–334

    Article  Google Scholar 

  3. Pedrosa J, Castro A, Vinhoza TT (2014) Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms. In: Engineering in medicine and biology society (EMBC), 2014 36th annual international conference of the IEEE, IEEE, pp 2294–2297

  4. Park MK (2008) Pediatric cardiology for practitioners. Elsevier’s Health Sciences Rights Department in Philadelphia, Toronto

    Google Scholar 

  5. Han J, Ji X, Xintao H, Zhu D, Li K, Jiang X, Cui G, Guo L, Liu T (2013) Representing and retrieving video shots in human-centric brain imaging space. IEEE Trans Image Process 22(7):2723–2736

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wu W, Pirbhulal S, Zhang H, Mukhopadhyay SC (2019) Quantitative assessment for self-tracking of acute stress based on triangulation principle in a wearable sensor system. IEEE J Biomed Health Inform 23(2):703–713

    Article  PubMed  Google Scholar 

  7. Wang Y, Liu H, Zheng W, Xia Y, Li Y, Chen P, Guo K, Xie H (2019) Multi-objective workflow scheduling with deep-q-network-based multi-agent reinforcement learning. IEEE Access 7:39974–39982

    Article  Google Scholar 

  8. Nabih-Ali M, El-Dahshan ELSA, Yahia AS (2017) A review of intelligent systems for heart sound signal analysis. J Med Eng Technol 41(7):553–563

    Article  PubMed  Google Scholar 

  9. Ahmad MS, Khan AS, Khattak JG, Khattak S (2014) A signal processing technique for heart murmur extraction and classification using fuzzy logic controller. Res J Appl Sci Eng Technol 8(1):1–8

    Article  Google Scholar 

  10. Naseri H, Homaeinezhad MR (2013) Detection and boundary identification of phonocardiogram sounds using an expert frequency-energy based metric. Ann Biomed Eng 41(2):279–292

    Article  CAS  PubMed  Google Scholar 

  11. Kang S, Doroshow R, McConnaughey J, Khandoker A, Shekhar R (2015) Heart sound segmentation toward automated heart murmur classification in pediatric patents. In: 8th international conference on signal processing, image processing and pattern recognition (SIP). IEEE, pp 9–12

  12. Zhong L, Wan J, Huang Z, Cao G, Xiao B (2013) Heart murmur recognition based on hidden markov model. J Signal Inform Process 4(02):140

    Article  Google Scholar 

  13. Kotb MA, Nabih H, El Zahraa F, El Falaki M, Shaker CW, Refaey MA, Rjoob KWY (2016) Improving the recognition of heart murmur. Int J Adv Comput Sci Appl (IJACSA) 7:1–10

    Google Scholar 

  14. Zhang W, Han J, Deng S (2017) Heart sound classification based on scaled spectrogram and tensor decomposition. Expert Syst Appl 84:220–231

    Article  Google Scholar 

  15. Hamidah A, Saputra R, Mengko TLR, Mengko R, Anggoro B (2016) Effective heart sounds detection method based on signal’s characteristics. In: International symposium on intelligent signal processing and communication systems (ISPACS). IEEE, pp 1–4

  16. 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

    Article  Google Scholar 

  17. Zheng Y, Guo X, Ding X (2015) A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification. Expert Syst Appl 42(5):2710–2721

    Article  Google Scholar 

  18. Varghees VN, Ramachandran KI (2015) Heart murmur detection and classification using wavelet transform and hilbert phase envelope. In: Twenty first national conference on communications (NCC). IEEE, pp 1–6

  19. Jimenez JA, Becerra MA, Delgado-Trejos E (2014) Heart murmur detection using ensemble empirical mode decomposition and derivations of the mel-frequency cepstral coefficients on 4-area phonocardiographic signals. In: Computing in cardiology conference (CinC). IEEE, pp 493–496

  20. Kristomo D, Hidayat R, Soesanti I, Kusjani A (2016) Heart sound feature extraction and classification using autoregressive power spectral density (ar-psd) and statistics features. In: AIP conference proceedings. AIP Publishing, 1755, p 090007

  21. Sun S, Wang H, Jiang Z, Fang Y, Tao T (2014) Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system. Expert Syst Appl 41(4):1769–1780

    Article  Google Scholar 

  22. Dominguez-Morales JP, Jimenez-Fernandez AF, Dominguez-Morales MJ, Jimenez-Moreno G (2018) Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE Trans Biomed Circuits Syst 12(1):24–34

    Article  PubMed  Google Scholar 

  23. Gharehbaghi A, Borga M, Sjöberg BJ, Ask P (2015) A novel method for discrimination between innocent and pathological heart murmurs. Med Eng Phys 37(7):674–682

    Article  PubMed  Google Scholar 

  24. Eslamizadeh G, Barati R (2017) Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods. Artif intell Med 78:23–40

    Article  PubMed  Google Scholar 

  25. Ganguly A, Sharma M (2017) Detection of pathological heart murmurs by feature extraction of phonocardiogram signals. J Appl Adv Res 2(4):200–205

    Article  Google Scholar 

  26. Chakir F, Jilbab A, Nacir C, Hammouch A (2016) Phonocardiogram signals classification into normal heart sounds and heart murmur sounds. In: 11th international conference on intelligent systems: theories and applications (SITA). IEEE, pp 1–4

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

    Article  Google Scholar 

  28. Mason D (2000) Listening to the heart: a comprehensive collection of heart sounds and murmurs. Davis, Worcester

    Google Scholar 

  29. egeneral medical heart murmur database. http://www.egeneralmedical.com/listohearmur.html. Accessed 174 Apr 2018

  30. Heart Sound. Murmur library. University of Michigan (2011) http://www.med.umich.edu/lrc/psb_open/html/repo/primer_heartsound/primer_heartsound.html. Accessed 17 Apr 2018

  31. Bioscience heart sound database (2011) http://int-prop.lf2.cuni.cz/heart_sounds/h14/sound.html

  32. Bentley P, Nordehn G, Coimbra M, Mannor S, Getz R (2011) The pascal classifying heart sounds challenge 2011 (chsc2011) results. http://www.peterjbentley.com/heartchallenge/index.html

  33. Cardionics Inc. http://www.cardionics.com/learningcardiac.html

  34. Physionet Challenge. https://www.physionet.org/challenge/2016

  35. Kumar D, Carvalho Pd, Antunes M, Henriques J, Maldonado M, Schmidt R, Habetha J (2006) Wavelet transform and simplicity based heart murmur segmentation. In: 2006 Computers in Cardiology. IEEE, pp 173–176

Download references

Acknowledgements

We would like to thank Dr. Imran, Head of Cardiology Department, Ayub Teaching Hospital, Abottabad, Pakistan, who allowed the utilization of hospital room. We are also thankful to Dr. Shahid Khattak, Chairman Electrical Engineering Department Comsats institute of information technology, Abbottabad for providing us an Electronic Stethoscope for dataset capturing.

Author information

Authors and Affiliations

  1. Electrical Engineering Department, University of Engineering and Technology Taxila, Taxila, Pakistan

    M. Sheraz Ahmad, Junaid Mir, Muhammad Obaid Ullah & Muhammad Laiq Ur Rahman Shahid

  2. Computer Science Department, University of Engineering and Technology Taxila, Taxila, Pakistan

    Muhammad Adnan Syed

Authors
  1. M. Sheraz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junaid Mir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Obaid Ullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Laiq Ur Rahman Shahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Adnan Syed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junaid Mir.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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

Ahmad, M.S., Mir, J., Ullah, M.O. et al. An efficient heart murmur recognition and cardiovascular disorders classification system. Australas Phys Eng Sci Med 42, 733–743 (2019). https://doi.org/10.1007/s13246-019-00778-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-019-00778-x

Keywords

Search

Navigation