Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment_Journal of Biomedical Engineering

Authors：

XIA Jun ¹ , SUN Jing ¹ , YANG Hongbo ^2,3 , PAN Jiahua ³ , GUO Tao ^2,3 ,  WANG Weilian ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China;
2. Kunming Medical University, Kunming 650000, P. R. China;
3. Fuwai Cardiovascular Hospital of Yunnan Province, Kunming 650102, P. R. China;

Corresponding author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

Heart sound; Multi-window time-frequency reassignment; Congenital heart disease; Bark-frequency spectral coefficient; Deep learning

DOI：

10.7507/1001-5515.202212037

Video：

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Abstract Full text Figures/Tables Video References Cited by

The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.

Citation： XIA Jun, SUN Jing, YANG Hongbo, PAN Jiahua, GUO Tao, WANG Weilian. Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment. Journal of Biomedical Engineering, 2024, 41(1): 51-59. doi: 10.7507/1001-5515.202212037 Copy

1.	Long Z, Xu Y, Liu W, et al. Mortality trend of heart diseases in China, 2013–2020. Cardiology Plus, 2022, 7(3): 111-117.
2.	Choi S, Jiang Z. Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. Computers in Biology and Medicine, 2010, 40(1): 8-20.
3.	Koçyigit Y. A novel feature extraction method for heart sounds classification// International Work-Conference on Bioinformatics and Biomedical Engineering(IWBBIO). Granada: IWBBIO, 2014: 34-41.
4.	Safara F, Doraisamy S, Azman A, et al. Wavelet packet entropy for heart murmurs classification. Advances in Bioinformatics, 2012, 2012: 327269.
5.	Kay E, Agarwal A. DropConnected neural network trained with diverse features for classifying heart sounds// Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 617-620.
6.	Hong T, Chen H, Li T, et al. Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 593-596.
7.	Yaseen, Son G Y, Kwon S. Classification of heart sound signal using multiple features. Applied Sciences, 2018, 8(12): 2344.
8.	Chen Q, Zhang W, Tian X, et al. Automatic heart and lung sounds classification using convolutional neural networks//2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). Jeju: IEEE, 2016: 1-4.
9.	Markaki M, Germanakis I, Stylianou Y. Automatic classification of systolic heart murmurs// IEEE International Conference on Acoustics. Vancouver: IEEE, 2013: 1301-1305.
10.	Lubis C, Gondawijaya F. Heart sound diagnose system with BFCC, MFCC, and backpropagation neural network//IOP conference series: materials science and engineering. Jakarta: IOP Publishing, 2019, 508(1): 012119.
11.	Rubin J, Abreu R, Ganguli A, et al. Classifying heart sound recordings using deep convolutional neural networks and Mel-frequency cepstral coefficients//2016 Computing in cardiology conference (CinC). Vancouver: IEEE, 2016: 813-816.
12.	Kui H, Pan J, Zong R, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomed Signal Process Control, 2021, 69: 102893.
13.	Gao S, Zheng Y, Guo X. Gated recurrent unit-based heart sound analysis for heart failure screening. Biomedical Engineering Online, 2020, 19(1): 3.
14.	Zhou G, Chen Y, Chien C. On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks. BMC Medical Informatics and Decision Making, 2022, 22(1): 226.
15.	Ghosh D, Debnath D S, Bose S. A comparative study of performance of fpga based Mel filter bank & bark filter bank. arXiv preprint, 2012, arXiv: 1206.1450.
16.	Quiceno-Manrique A F, Godino-Llorente J I, Blanco-Velasco M, et al. Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals. Annals of Biomedical Engineering, 2010, 38(1): 118-137.
17.	Auger F, Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089.
18.	范树凯. 非线性调频信号的时频分析及其应用. 无锡: 江南大学, 2008.
19.	Daubechies I. Time-frequency localization operators: a geometric phase space approach. IEEE Transactions on Information Theory, 1988, 34(4): 605-612.
20.	Reinhold I, Sandsten M. The multitaper reassigned spectrogram for oscillating transients with Gaussian envelopes. Signal Processing, 2022, 198: 108570.
21.	Hansson-Sandsten M, Axmon J. Multiple-window cepstrum analysis for estimation of periodicity. IEEE Transactions on Signal Processing, 2007, 55(2): 474-481.
22.	Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiological Measurement, 2016, 37(12): 2181-2213.
23.	Hansson M, Jönsson P. Estimation of HRV spectrogram using multiple window methods focussing on the high frequency power. Medical Engineering & Physics, 2006, 28(8): 749-761.

1. Long Z, Xu Y, Liu W, et al. Mortality trend of heart diseases in China, 2013–2020. Cardiology Plus, 2022, 7(3): 111-117.
2. Choi S, Jiang Z. Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique. Computers in Biology and Medicine, 2010, 40(1): 8-20.
3. Koçyigit Y. A novel feature extraction method for heart sounds classification// International Work-Conference on Bioinformatics and Biomedical Engineering(IWBBIO). Granada: IWBBIO, 2014: 34-41.
4. Safara F, Doraisamy S, Azman A, et al. Wavelet packet entropy for heart murmurs classification. Advances in Bioinformatics, 2012, 2012: 327269.
5. Kay E, Agarwal A. DropConnected neural network trained with diverse features for classifying heart sounds// Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 617-620.
6. Hong T, Chen H, Li T, et al. Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network// 2016 Computing in Cardiology Conference (CinC). Vancouver: IEEE, 2016: 593-596.
7. Yaseen, Son G Y, Kwon S. Classification of heart sound signal using multiple features. Applied Sciences, 2018, 8(12): 2344.
8. Chen Q, Zhang W, Tian X, et al. Automatic heart and lung sounds classification using convolutional neural networks//2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). Jeju: IEEE, 2016: 1-4.
9. Markaki M, Germanakis I, Stylianou Y. Automatic classification of systolic heart murmurs// IEEE International Conference on Acoustics. Vancouver: IEEE, 2013: 1301-1305.
10. Lubis C, Gondawijaya F. Heart sound diagnose system with BFCC, MFCC, and backpropagation neural network//IOP conference series: materials science and engineering. Jakarta: IOP Publishing, 2019, 508(1): 012119.
11. Rubin J, Abreu R, Ganguli A, et al. Classifying heart sound recordings using deep convolutional neural networks and Mel-frequency cepstral coefficients//2016 Computing in cardiology conference (CinC). Vancouver: IEEE, 2016: 813-816.
12. Kui H, Pan J, Zong R, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomed Signal Process Control, 2021, 69: 102893.
13. Gao S, Zheng Y, Guo X. Gated recurrent unit-based heart sound analysis for heart failure screening. Biomedical Engineering Online, 2020, 19(1): 3.
14. Zhou G, Chen Y, Chien C. On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks. BMC Medical Informatics and Decision Making, 2022, 22(1): 226.
15. Ghosh D, Debnath D S, Bose S. A comparative study of performance of fpga based Mel filter bank & bark filter bank. arXiv preprint, 2012, arXiv: 1206.1450.
16. Quiceno-Manrique A F, Godino-Llorente J I, Blanco-Velasco M, et al. Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals. Annals of Biomedical Engineering, 2010, 38(1): 118-137.
17. Auger F, Flandrin P. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Transactions on Signal Processing, 1995, 43(5): 1068-1089.
18. 范树凯. 非线性调频信号的时频分析及其应用. 无锡: 江南大学, 2008.
19. Daubechies I. Time-frequency localization operators: a geometric phase space approach. IEEE Transactions on Information Theory, 1988, 34(4): 605-612.
20. Reinhold I, Sandsten M. The multitaper reassigned spectrogram for oscillating transients with Gaussian envelopes. Signal Processing, 2022, 198: 108570.
21. Hansson-Sandsten M, Axmon J. Multiple-window cepstrum analysis for estimation of periodicity. IEEE Transactions on Signal Processing, 2007, 55(2): 474-481.
22. Liu C, Springer D, Li Q, et al. An open access database for the evaluation of heart sound algorithms. Physiological Measurement, 2016, 37(12): 2181-2213.
23. Hansson M, Jönsson P. Estimation of HRV spectrogram using multiple window methods focussing on the high frequency power. Medical Engineering & Physics, 2006, 28(8): 749-761.

Journal of Biomedical Engineering

Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment

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