A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network_Journal of Biomedical Engineering

Authors：

 WANG Zichao ¹ , JIN Yanrui ¹ , ZHAO Liqun ² , LIU Chengliang ¹

1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R.China;
2. Department of Cardiology, Shanghai First People’s Hospital, Shanghai 200080, P.R.China;

Corresponding author：

WANG Zichao, Email: wangzch@sjtu.edu.cn

Keywords：

heart sound classification; feature extraction; extreme gradient boosting; long short-term memory network

DOI：

10.7507/1001-5515.202006025

Video：

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Abstract Full text Figures/Tables Video References Cited by

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.

Citation： WANG Zichao, JIN Yanrui, ZHAO Liqun, LIU Chengliang. A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network. Journal of Biomedical Engineering, 2021, 38(1): 10-20. doi: 10.7507/1001-5515.202006025 Copy

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2.	Moukadem A, Dieterlen A, Hueber N, et al. A robust heart sounds segmentation module based on S-transform. Biomed Signal Process Control, 2013, 8(3): 273-281.
3.	Pedrosa J, Castro A, Vinhoza T T. Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014: 2294-2297.
4.	Schmidt S E, Holst-Hansen C, Graff C, et al. Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiol Meas, 2010, 31(4): 513-529.
5.	Springer D B, Tarassenko L, Clifford G D. Logistic Regression-HSMM-Based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
6.	Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 2016, 37(12): 2181-2213.
7.	Messner E, Zohrer M, Pernkopf F. Heart sound segmentation—an event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng, 2018, 65(9): 1964-1974.
8.	Renna F, Oliveira J, Coimbra M T. Deep convolutional neural networks for heart sound segmentation. IEEE J Biomed Health Inform, 2019, 23(6): 2435-2445.
9.	Potes C, Parvaneh S, Rahman A, et al. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds//2016 Computing in Cardiology Conference (CinC), IEEE, 2016: 621-624.
10.	Chen Jianfei, Dang Xin. Heart sound analysis based on extended features and related factors//2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2019: 2189-2194.
11.	Oztavli E, Aptoula E. Effect of early and late fusion on heart sound classification//Signal Processing and Communications Applications Conference, IEEE, 2018: 1-4.
12.	Zabihi M, Rad A B, Kiranyaz S, et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation//2016 Computing in Cardiology Conference (CinC), IEEE, 2016: 613-616.
13.	Yadav A, Dutta M K, Travieso C M, et al. Automatic classification of normal and abnormal PCG recording heart sound recording using fourier transform//2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), IEEE, 2018: 1-9.
14.	Upretee P, Yüksel M E. Accurate classification of heart sounds for disease diagnosis by a single time-varying spectral feature: preliminary results//2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). Kayseri, TURKEY: IEEE, 2019: 1-4.
15.	Li Jinghui, Ke Li, Du Qiang, et al. Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine. IEEE Access, 2019, 7: 179339-179348.
16.	Hamidi M, Ghassemian H, Imani M. Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control, 2018, 39: 351-359.
17.	Ren Zhao, Cummins N, Pandit V, et al. Learning image-based representations for heart sound classification// International Conference. 2018: 143-147.
18.	Li Fen, Liu Ming, Zhao Yuejin, et al. Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Signal Process, 2019, 2019(1): 59.
19.	Krishnan P T, Balasubramanian P, Umapathy S. Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network. Phys Eng Sci Med, 2020, 43(2): 505-515.
20.	Meintjes A, Lowe A, Legget M. Fundamental heart sound classification using the continuous wavelet transform and convolutional neural networks//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2018: 409-412.
21.	Noman F, Ting C M, Salleh S H, et al. Short-segment heart sound classification using an ensemble of deep convolutional neural networks//ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019: 1318-1322.
22.	Singh S A, Majumder S, Mishra M. Classification of short unsegmented heart sound based on deep learning//2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, 2019: 1-6.
23.	Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84-90.
24.	Qian Kun, Ren Zhao, Dong Fengquan, et al. Deep wavelets for heart sound classification//2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), IEEE, 2019: 1-2.
25.	Chen Tianqi, Guestrin C. XGBoost: A Scalable Tree Boosting System// The International Conference on Knowledge Discovery and Data Mining. 2016. arXiv: 1603.02754.
26.	Shi H, Wang H, Huang Y, et al. A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput Methods Programs Biomed, 2019, 171: 1-10.
27.	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
28.	Sainath T N, Vinyals O, Senior A, et al. Convolutional, long short-term memory, fully connected deep neural networks//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2015: 4580-4584.

1. World Health Organization. Cardiovascular diseases (CVDs). [2017-05-17]. https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
2. Moukadem A, Dieterlen A, Hueber N, et al. A robust heart sounds segmentation module based on S-transform. Biomed Signal Process Control, 2013, 8(3): 273-281.
3. Pedrosa J, Castro A, Vinhoza T T. Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014: 2294-2297.
4. Schmidt S E, Holst-Hansen C, Graff C, et al. Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiol Meas, 2010, 31(4): 513-529.
5. Springer D B, Tarassenko L, Clifford G D. Logistic Regression-HSMM-Based heart sound segmentation. IEEE Trans Biomed Eng, 2016, 63(4): 822-832.
6. Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 2016, 37(12): 2181-2213.
7. Messner E, Zohrer M, Pernkopf F. Heart sound segmentation—an event detection approach using deep recurrent neural networks. IEEE Trans Biomed Eng, 2018, 65(9): 1964-1974.
8. Renna F, Oliveira J, Coimbra M T. Deep convolutional neural networks for heart sound segmentation. IEEE J Biomed Health Inform, 2019, 23(6): 2435-2445.
9. Potes C, Parvaneh S, Rahman A, et al. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds//2016 Computing in Cardiology Conference (CinC), IEEE, 2016: 621-624.
10. Chen Jianfei, Dang Xin. Heart sound analysis based on extended features and related factors//2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2019: 2189-2194.
11. Oztavli E, Aptoula E. Effect of early and late fusion on heart sound classification//Signal Processing and Communications Applications Conference, IEEE, 2018: 1-4.
12. Zabihi M, Rad A B, Kiranyaz S, et al. Heart sound anomaly and quality detection using ensemble of neural networks without segmentation//2016 Computing in Cardiology Conference (CinC), IEEE, 2016: 613-616.
13. Yadav A, Dutta M K, Travieso C M, et al. Automatic classification of normal and abnormal PCG recording heart sound recording using fourier transform//2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), IEEE, 2018: 1-9.
14. Upretee P, Yüksel M E. Accurate classification of heart sounds for disease diagnosis by a single time-varying spectral feature: preliminary results//2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). Kayseri, TURKEY: IEEE, 2019: 1-4.
15. Li Jinghui, Ke Li, Du Qiang, et al. Heart sound signal classification algorithm: a combination of wavelet scattering transform and twin support vector machine. IEEE Access, 2019, 7: 179339-179348.
16. Hamidi M, Ghassemian H, Imani M. Classification of heart sound signal using curve fitting and fractal dimension. Biomed Signal Process Control, 2018, 39: 351-359.
17. Ren Zhao, Cummins N, Pandit V, et al. Learning image-based representations for heart sound classification// International Conference. 2018: 143-147.
18. Li Fen, Liu Ming, Zhao Yuejin, et al. Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Signal Process, 2019, 2019(1): 59.
19. Krishnan P T, Balasubramanian P, Umapathy S. Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network. Phys Eng Sci Med, 2020, 43(2): 505-515.
20. Meintjes A, Lowe A, Legget M. Fundamental heart sound classification using the continuous wavelet transform and convolutional neural networks//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2018: 409-412.
21. Noman F, Ting C M, Salleh S H, et al. Short-segment heart sound classification using an ensemble of deep convolutional neural networks//ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019: 1318-1322.
22. Singh S A, Majumder S, Mishra M. Classification of short unsegmented heart sound based on deep learning//2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, 2019: 1-6.
23. Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84-90.
24. Qian Kun, Ren Zhao, Dong Fengquan, et al. Deep wavelets for heart sound classification//2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), IEEE, 2019: 1-2.
25. Chen Tianqi, Guestrin C. XGBoost: A Scalable Tree Boosting System// The International Conference on Knowledge Discovery and Data Mining. 2016. arXiv: 1603.02754.
26. Shi H, Wang H, Huang Y, et al. A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput Methods Programs Biomed, 2019, 171: 1-10.
27. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
28. Sainath T N, Vinyals O, Senior A, et al. Convolutional, long short-term memory, fully connected deep neural networks//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2015: 4580-4584.

Journal of Biomedical Engineering

A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network

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