Skip to main content
SpringerLink
Log in

Learning Optimal Time-Frequency Representations for Heart Sound: A Comparative Study

  • Conference paper
  • First Online:
Proceedings of the 9th Conference on Sound and Music Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 923))

Abstract

Computer audition based methods have increasingly attracted efforts among the community of digital health. In particular, heart sound analysis can provide a non-invasive, real-time, and convenient (anywhere and anytime) solution for preliminary diagnosis and/or long-term monitoring of patients who are suffering from cardiovascular diseases. Nevertheless, extracting excellent time-frequency features from the heart sound is not an easy task. On the one hand, heart sound belongs to audio signals, which may be suitable to be analysed by classic audio/speech techniques. On the other hand, this kind of sound generated by our human body should contain some characteristics of physiological signals. To this end, we propose a comprehensive investigation on time-frequency methods for analysing the heart sound, i.e., short-time Fourier transformation, wavelet transformation, Hilbert-Huang transformation, and Log-Mel transformation. The time-frequency representations will be automatically learnt via pre-trained deep convolutional neural networks. Experimental results show that all the investigated methods can reach a mean accuracy higher than 60.0%. Moreover, we find that wavelet transformation can beat other methods by reaching the highest mean accuracy of 75.1% in recognising normal or abnormal heart sounds.

This work was partially supported by the BIT Teli Young Fellow Program from the Beijing Institute of Technology, China, the China Scholarship Council (No. 202106420019), China, the JSPS Postdoctoral Fellowship for Research in Japan (ID No. P19081) from the Japan Society for the Promotion of Science (JSPS), Japan, and the Grants-in-Aid for Scientific Research (No. 19F19081 and No. 20H00569) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://perso.ens-lyon.fr/patrick.flandrin/emd.html.

  2. 2.

    https://librosa.org/doc/latest/index.html.

  3. 3.

    https://keras.io/.

  4. 4.

    https://www.physionet.org/content/challenge-2016/1.0.0/.

References

  1. Ali N, El-Dahshan ES, Yahia A (2017) Denoising of heart sound signals using discrete wavelet transform. Circ Syst Sig Process 36(11):4482–4497

    Article  Google Scholar 

  2. Arora V, Leekha R, Singh R, Chana I (2019) Heart sound classification using machine learning and phonocardiogram. Mod Phys Lett B 33(26):1–24

    Article  Google Scholar 

  3. Deng M, Meng T, Cao J, Wang S, Zhang J, Fan H (2020) Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Networks 130:22–32

    Article  Google Scholar 

  4. Dong F et al (2020) Machine listening for heart status monitoring: Introducing and benchmarking HSS-the heart sounds Shenzhen corpus. IEEE J Biomed Health Inform 24(7):2082–2092

    Article  Google Scholar 

  5. Gardezi S et al (2018) Cardiac auscultation poorly predicts the presence of valvular heart disease in asymptomatic primary care patients. Heart 104(22):1832–1835

    Article  Google Scholar 

  6. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings CVPR, pp 770–778. IEEE, Las Vegas, Nevada

    Google Scholar 

  7. Karnath B, Thornton W (2002) Auscultation of the heart. Hosp Phys 38(9):39–45

    Google Scholar 

  8. Koike T, Qian K, Kong Q, Plumbley MD, Schuller BW, Yamamoto Y (2020) Audio for audio is better? An investigation on transfer learning models for heart sound classification. In: Proceedings EMBC, pp 74–77. IEEE, Montr\(\acute{e}\)al, Canada

    Google Scholar 

  9. Li F et al (2019) Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Sig Process 2019(1):1–11

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Li S, Li F, Tang S, Xiong W (2020) A review of computer-aided heart sound detection techniques. BioMed Res Int 2020:1–10

    Google Scholar 

  12. Liu C et al (2016) An open access database for the evaluation of heart sound algorithms. Physiol Meas 37(12):2181–2213

    Article  Google Scholar 

  13. Meng H, Yan T, Yuan F, Wei H (2019) Speech emotion recognition from 3D Log-Mel spectrograms with deep learning network. IEEE Access 7:125868–125881

    Article  Google Scholar 

  14. Noman F, Ting CM, Salleh SH, Ombao H (2019) Short-segment heart sound classification using an ensemble of deep convolutional neural networks. In: Proceedings ICASSP, pp 1318–1322. IEEE, Brighton, UK

    Google Scholar 

  15. Qian K et al (2021) Can machine learning assist locating the excitation of snore sound? A review. IEEE J Biomed Health Inform 25(4):1233–1246

    Article  Google Scholar 

  16. Qian K et al (2020) Computer audition for healthcare: opportunities and challenges. Front Digit Health 2:1–4

    Article  Google Scholar 

  17. Qian K, Ren Z, Dong F, Lai W, Schuller B, Yamamoto Y (2019) Deep wavelets for heart sound classification. In: Proceedings ISPACS, pp 1–2. IEEE, Taiwan, China

    Google Scholar 

  18. Qian K et al (2021) Computer audition for fighting the SARS-CoV-2 corona crisis – introducing the multi-task speech corpus for COVID-19. IEEE Internet Things J 1–12 (in press)

    Google Scholar 

  19. Ren H, Jin H, Chen C, Ghayvat H, Chen W (2018) A novel cardiac auscultation monitoring system based on wireless sensing for healthcare. IEEE J Transl Eng Health Med 6:1–12

    Article  Google Scholar 

  20. Ren Z, Cummins N, Pandit V, Han J, Qian K, Schuller B (2018) Learning image-based representations for heart sound classification. In: Proceedings DHA, pp 143–147. ACM, New York, USA

    Google Scholar 

  21. Renna F, Oliveira J, Coimbra MT (2019) Deep convolutional neural networks for heart sound segmentation. IEEE J Biomed Health Inform 23(6):2435–2445

    Article  Google Scholar 

  22. Ryu H, Park J, Shin H (2016) Classification of heart sound recordings using convolution neural network. In: Proceedings CinC, pp 1153–1156. IEEE, Vancouver, Canada

    Google Scholar 

  23. Safdar S, Zafar S, Zafar N, Khan F (2018) Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artif Intell Rev 50(4):597–623

    Article  Google Scholar 

  24. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  25. Sun J, Kang L, Wang W (2017) Heart sound signals based on CNN classification research. In: Proceedings ICBBS, pp 44–48. ACM, Singapore

    Google Scholar 

  26. Tschannen M, Kramer T, Marti G, Heinzmann M, Wiatowski T (2016) Heart sound classification using deep structured features. In: Proceedings CinC, pp 565–568. IEEE, Vancouver, Canada

    Google Scholar 

  27. Tseng YL, Ko PY, Jaw FS (2012) Detection of the third and fourth heart sounds using Hilbert-Huang transform. Biomed Eng Online 11(1):1–13

    Article  Google Scholar 

  28. Uğuz H (2012) Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput Appl 21(7):1617–1628

    Article  Google Scholar 

  29. Upretee P, Yüksel ME (2021) 13 - accurate classification of heart sounds for disease diagnosis by using spectral analysis and deep learning methods. Data Anal Biomed Eng Healthcare 215–232 (in press)

    Google Scholar 

  30. Wang Y, Li W, Zhou J, Li X, Pu Y (2014) Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD. Future Gener Comput Syst 37:488–495

    Article  Google Scholar 

  31. Son GY, Kwon S (2018) Classification of heart sound signal using multiple features. Appl Sci 8(12):1–14

    Google Scholar 

  32. Yuan Y, Xun G, Jia K, Zhang A (2017) A multi-view deep learning method for epileptic seizure detection using short-time Fourier transform. In: Proceedings ACM BCB, pp 213–222. ACM, Boston, Massachusetts

    Google Scholar 

  33. Zhang W, Han J, Deng S (2017) Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Sig Process Control 32:20–28

    Article  Google Scholar 

  34. Zhang W, Hana J, Deng S (2020) Analysis of heart sound anomalies using ensemble learning. Biomed Sig Process Control 62:1–14

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Medical Technology, Beijing Institute of Technology, Beijing, China

    Zhihua Wang, Zhihao Bao, Kun Qian & Bin Hu

  2. School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, China

    Zhihua Wang

  3. GLAM – Group on Language, Audio, and Music, Imperial College London, London, UK

    Björn W. Schuller

  4. Educational Physiology Laboratory, The University of Tokyo, Bunkyo, Tokyo, Japan

    Zhihua Wang & Yoshiharu Yamamoto

Authors
  1. Zhihua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihao Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kun Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Björn W. Schuller
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yoshiharu Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Kun Qian or Bin Hu .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

    Xi Shao

  2. Beijing Institute of Technology, Beijing, China

    Kun Qian

  3. Communication University of China, Beijing, China

    Xin Wang

  4. Zhejiang University, Hangzhou, Zhejiang, China

    Kejun Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Z., Bao, Z., Qian, K., Hu, B., Schuller, B.W., Yamamoto, Y. (2023). Learning Optimal Time-Frequency Representations for Heart Sound: A Comparative Study. In: Shao, X., Qian, K., Wang, X., Zhang, K. (eds) Proceedings of the 9th Conference on Sound and Music Technology. Lecture Notes in Electrical Engineering, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-19-4703-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-4703-2_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4702-5

  • Online ISBN: 978-981-19-4703-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation