A Comprehensive Survey of Analysis of Heart Sounds using Machine Learning Techniques to Detect Heart Diseases
Main Article Content
Keywords
congenital heart disease, computer-aided auscultation, deep learning, machine learning, heart sound auscultation, cardiovascular heart disease
Abstract
An estimated 32 per cent of all global deaths were due to cardiovascular diseases (CVD) in 2019 which is a leading cause of death globally. Of these, three-fourths of the deaths occur in low and middle-income nations. The CVD must be detected early for improving patient outcome. Automated heart sound analysis has been studied for more than a few decades using various Digital Signal Processing (DSP) techniques. Attempts have been made in the last decade to apply Machine Learning (ML) in the healthcare domain to make healthcare more accessible. This paper surveys the significant steps that have been taken to detect the most common heart diseases by the application of the machine learning (ML) and deep learning (DL) techniques to analyse the phonocardiograms over the last few years.
References
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2. WHO, ‘Cardiovascular diseases (CVDs)’. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
3. P. Lubaib, A. M. Kv, and A. R. V, ‘P b d s’, Int. J. Biomed. Eng. Sci., vol. 2, no. 3, pp. 1–10, 2015.
4. A. Cheema and M. Singh, ‘Steps Involved in Heart Sound Analysis- A Review of Existing Trends’, Int. J. Eng. Trends Technol., vol. 4, no. 7, pp. 2921–2925, 2013.
5. M. Nabih-Ali, E. S. A. El-Dahshan, and A. S. Yahia, ‘A review of intelligent systems for heart sound signal analysis’, J. Med. Eng. Technol., vol. 41, no. 7, pp. 553–563, 2017, doi: 10.1080/03091902.2017.1382584.
6. M. Abdollahpur, A. Ghaffari, S. Ghiasi, and M. J. Mollakazemi, ‘Detection of pathological heart sounds’, Physiol. Meas., vol. 38, no. 8, pp. 1616–1630, 2017, doi: 10.1088/1361-6579/aa7840.
7. Heart Sounds Topic Review’, Healio.com. https://www.healio.com/cardiology/learn-the-heart/cardiology-review/topic-reviews/heart-sounds#:~:text=A murmur is due to,sound is almost always pathologic. (accessed Jun. 20, 2022).
8. M. Bassiouni, W. Khalifa, E. S. A. El Dahshan, and A. B. M. Salam, ‘A study on PCG as a biometric approach’, 2015 IEEE 7th Int. Conf. Intell. Comput. Inf. Syst. ICICIS 2015, no. December, pp. 161–166, 2016, doi: 10.1109/IntelCIS.2015.7397215.
9. M. E. Karar, S. H. El-Khafif, and M. A. El-Brawany, ‘Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree’, J. Med. Syst., vol. 41, no. 4, 2017, doi: 10.1007/s10916-017-0704-9.
10. S. Alam, R. Banerjee, and S. Bandyopadhyay, ‘Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks’. 2018.
11. W. Hu, J. Lv, D. Liu, and Y. Chen, ‘Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder’, J. Phys. Conf. Ser., vol. 1004, no. 1, 2018, doi: 10.1088/1742-6596/1004/1/012002.
12. Yaseen, G. Y. Son, and S. Kwon, ‘Classification of heart sound signal using multiple features’, Appl. Sci., vol. 8, no. 12, 2018, doi: 10.3390/app8122344.
13. M. Y. Esmail, D. H. Ahmed, and M. Eltayeb, ‘Classification System for Heart Sounds Based on Random Forests’, J. Clin. Eng., vol. 44, no. 2, pp. 76–80, 2019, doi: 10.1097/jce.0000000000000335.
14. S. Aziz, M. U. Khan, M. Alhaisoni, T. Akram, and M. Altaf, ‘Phonocardiogram signal processing for automatic diagnosis of congenital heart disorders through fusion of temporal and cepstral features’, Sensors (Switzerland), vol. 20, no. 13, pp. 1–20, 2020, doi: 10.3390/s20133790.
15. S. Gao, Y. Zheng, and X. Guo, ‘Gated recurrent unit-based heart sound analysis for heart failure screening’, Biomed. Eng. Online, vol. 19, no. 1, pp. 1–17, 2020, doi: 10.1186/s12938-020-0747-x.
16. T. Koike, S. Member, K. Qian, Q. Kong, S. Member, and M. D. Plumbley, ‘Audio for Audio is Better ? An Investigation on Transfer Learning Models for Heart Sound Classification’, pp. 31–34, 2020.
17. J. Lv et al., ‘Artificial intelligence-assisted auscultation in detecting congenital heart disease’, Eur. Hear. J. - Digit. Heal., vol. 2, no. 1, pp. 119–124, 2021, doi: 10.1093/ehjdh/ztaa017.
18. Y. He, W. Li, W. Zhang, S. Zhang, X. Pi, and H. Liu, ‘Research on segmentation and classification of heart sound signals based on deep learning’, Appl. Sci., vol. 11, no. 2, pp. 1–15, 2021, doi: 10.3390/app11020651.
19. Y. Zeinali and S. T. A. Niaki, ‘Heart sound classification using signal processing and machine learning algorithms’, Mach. Learn. with Appl., vol. 7, p. 100206, Mar. 2022, doi: 10.1016/j.mlwa.2021.100206.
20. H. Kui, J. Pan, R. Zong, H. Yang, and W. Wang, ‘Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks’, Biomed. Signal Process. Control, vol. 69, no. June, p. 102893, 2021, doi: 10.1016/j.bspc.2021.102893.
21. M. A. Soto-Murillo et al., ‘Automatic evaluation of heart condition according to the sounds emitted and implementing six classification methods’, Healthc., vol. 9, no. 3, pp. 1–15, 2021, doi: 10.3390/healthcare9030317.
22. E. S. Oktarina, N. Akhmad Setiawan, and I. Ardiyanto, ‘A New Tele-Healthcare System of Heart Sound Signal Classification based on Gated Recurrent Unit’, in International Conference on Artificial Intelligence and Big Data Analytics, 2021, pp. 1–6, doi: 10.1109/icaibda53487.2021.9689694.
23. N. Ibrahim, N. Jamal, M. N. A. H. Sha’abani, and L. F. Mahadi, ‘A Comparative Study of Heart Sound Signal Classification Based on Temporal, Spectral and Geometric Features’, in Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020, 2020, pp. 24–29, doi: 10.1109/IECBES48179.2021.9398810.
24. M. G. M. Milani, P. E. Abas, L. C. De Silva, and N. D. Nanayakkara, ‘Abnormal heart sound classification using phonocardiography signals’, Smart Heal., vol. 21, no. February, p. 100194, 2021, doi: 10.1016/j.smhl.2021.100194.
25. T. Koike, S. Member, K. Qian, and S. Member, ‘Transferring Cross-Corpus Knowledge : An
Investigation on Data Augmentation for Heart Sound Classification Transferring Cross-Corpus Knowledge : An Investigation on Data Augmentation for Heart Sound Classification’, no. October, 2021, doi: 10.1109/EMBC46164.2021.9629714.
26. M. Wang, B. Guo, Y. Hu, Z. Zhao, C. Liu, and H. Tang, ‘Transfer Learning Models for Detecting Six Categories of Phonocardiogram Recordings’, J. Cardiovasc. Dev. Dis., vol. 9, no. 3, 2022, doi: 10.3390/jcdd9030086.
27. M. K. Alnajjar and S. S. Abu-naser, ‘Heart Sounds Analysis and Classification for Cardiovascular Diseases Diagnosis using Deep Learning’, vol. 6, no. 1, pp. 7–23, 2022.
28. T. S. Roy, J. K. Roy, and N. Mandal, ‘Classifier identification using deep learning and machine learning algorithms for the detection of valvular heart diseases’, Biomed. Eng. Adv., vol. 3, no. May, p. 100035, 2022, doi: 10.1016/j.bea.2022.100035.
29. J. S. Khan, M. Kaushik, A. Chaurasia, M. K. Dutta, and R. Burget, ‘Computer Methods and Programs in Biomedicine Cardi-Net : A deep neural network for classification of cardiac disease using phonocardiogram signal’, Comput. Methods Programs Biomed., vol. 219, p. 106727, 2022, doi: 10.1016/j.cmpb.2022.106727.
30. Q. Abbas, A. Hussain, and A. R. Baig, ‘Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers’, Diagnostics, 2022, doi: 10.3390/ diagnostics12123109.
31. X. Bao, Y. Xu, and E. N. Kamavuako, ‘The Effect of Signal Duration on the Classification of Heart Sounds : A Deep Learning Approach’, sensors, 2022, doi: 10.3390/s22062261.
32. S. Takezaki and K. Kishida, ‘Data Augmentation and the Improvement of the Performance of Convolutional Neural Networks for Heart Sound Classification’, IAENG Int. J. Comput. Sci., vol. 49, no. 4, 2022.
33. W. N. Al-sharu, A. Mohammad, A. Member, S. Qazan, and A. Alqudah, ‘Detection of Valvular Heart Diseases Using Fourier Transform and Simple CNN Model’, IAENG Int. J. Comput. Sci., vol. 49, no. 4, 2022.
34. Y. Al-Issa and A. M. Alqudah, ‘A lightweight hybrid deep learning system for cardiac valvular disease classification’, Sci. Rep., vol. 12, no. 1, pp. 1–20, 2022, doi: 10.1038/s41598-022-18293-7.
35. S. A. Singh, S. Majumder, and M. Mishra, ‘Classification of short unsegmented heart sound based on deep learning’, I2MTC 2019 - 2019 IEEE Int. Instrum. Meas. Technol. Conf. Proc., vol. 2019-May, pp. 1–6, 2019, doi: 10.1109/I2MTC.2019.8826991.
36. S. B. Shuvo, S. N. Ali, S. I. Swapnil, M. S. Al-Rakhami, and A. Gumaei, ‘CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings’, IEEE Access, vol. 9, pp. 36955–36967, 2021, doi: 10.1109/ACCESS.2021.3063129.
37. F. Renna, J. Oliveira, and M. T. Coimbra, ‘Deep Convolutional Neural Networks for Heart Sound Segmentation’, IEEE J. Biomed. Heal. Informatics, vol. 23, no. 6, pp. 2435–2445, 2019, doi: 10.1109/JBHI.2019.2894222.
38. J. Rubin, R. Abreu, A. Ganguli, S. Nelaturi, I. Matei, and K. Sricharan, ‘Recognizing abnormal heart sounds using deep learning’, arXiv, 2017.
39. Wikipedia, ‘U-Net’, 2022. https://en.wikipedia.org/wiki/U-Net (accessed May 24, 2022).
40. F. Noman, C. Ting, S. Salleh, and H. Ombao, ‘Short-Segment Heart Sound Classification Using An Ensemble Of Deep Convolutional Neural Networks School of Biomedical Engineering & Health Sciences , Universiti Teknologi Malaysia , Malaysia Statistics Program , King Abdullah University of Science and Tec’, 44th IEEE Int. Conf. Acoust. Speech Signal Process., no. Dl, pp. 1318–1322, 2019.
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May 17, 2023
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S.Sathyanarayanan, Srikanta Murthy, & Sanjay Chitnis. (2023). A Comprehensive Survey of Analysis of Heart Sounds using Machine Learning Techniques to Detect Heart Diseases. Journal of Population Therapeutics and Clinical Pharmacology, 30(11), 375-384. https://doi.org/10.47750/jptcp.2023.30.11.038
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How to Cite
S.Sathyanarayanan, Srikanta Murthy, & Sanjay Chitnis. (2023). A Comprehensive Survey of Analysis of Heart Sounds using Machine Learning Techniques to Detect Heart Diseases. Journal of Population Therapeutics and Clinical Pharmacology, 30(11), 375-384. https://doi.org/10.47750/jptcp.2023.30.11.038