Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method_Journal of Biomedical Engineering

Authors：

WANG Yuanlin ¹ , SUN Jing ¹ , YANG Hongbo ² , GUO Tao ² , PAN Jiahua ² ,  WANG Weilian ¹

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

Corresponding author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

Heart sound; Fisher discriminant; Raised half sine function; Mel frequency cepstral coefficient; Integrated decision

DOI：

10.7507/1001-5515.202111059

Video：

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Abstract Full text Figures/Tables Video References Cited by

Heart sound analysis is significant for early diagnosis of congenital heart disease. A novel method of heart sound classification was proposed in this paper, in which the traditional mel frequency cepstral coefficient (MFCC) method was improved by using the Fisher discriminant half raised-sine function (F-HRSF) and an integrated decision network was used as classifier. It does not rely on segmentation of the cardiac cycle. Firstly, the heart sound signals were framed and windowed. Then, the features of heart sounds were extracted by using improved MFCC, in which the F-HRSF was used to weight sub-band components of MFCC according to the Fisher discriminant ratio of each sub-band component and the raised half sine function. Three classification networks, convolutional neural network (CNN), long and short-term memory network (LSTM), and gated recurrent unit (GRU) were combined as integrated decision network. Finally, the two-category classification results were obtained through the majority voting algorithm. An accuracy of 92.15%, sensitivity of 91.43%, specificity of 92.83%, corrected accuracy of 92.01%, and F score of 92.13% were achieved using the novel signal processing techniques. It shows that the algorithm has great potential in early diagnosis of congenital heart disease.

Citation： WANG Yuanlin, SUN Jing, YANG Hongbo, GUO Tao, PAN Jiahua, WANG Weilian. Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method. Journal of Biomedical Engineering, 2022, 39(6): 1140-1148. doi: 10.7507/1001-5515.202111059 Copy

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16.	Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Networks, 2020, 130(1): 22-32.
17.	Yan T, Wang D, Zheng M, et al. Fisher’s discriminant ratio based health indicator for locating informative frequency bands for machine performance degradation assessment. Mech Syst Signal Pr, 2022, 162(9): 108053.
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19.	Hu X H, Gansen Z, Lv J Z, et al. Isolated word speech recogniton based on HRSF and improved DTW algorithm// IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT). Macau: ACM, 2012: 270-273.
20.	Xiao B, Xu Y, Bi X, et al. Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing, 2019, 392: 153-159.
21.	Zhang W, Han J, Deng S. Abnormal heart sound detection using temporal quasi-periodic features and long short-term memory without segmentation. Biomed Signal Proces, 2019, 53: 101560.
22.	Lim H I. A study on layers of deep neural networks// 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS). Singapore: IEEE, 2020: 31-34.
23.	Lecun Y, Boser B, Denker J, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 2014, 1(4): 541-551.
24.	Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzz, 1998, 6(2): 107-116.
25.	Mirza A H. Online additive updates with FFT-IFFT operator on the GRU neural networks// Signal Processing and Communications Applications Conference (SIU). Izmir: IEEE, 2018: 1-4.
26.	Dean J, Corrado G S, Monga R, et al. Large scale distributed deep networks. Adv Neural Inf Proces Syst, 2013, 1: 1223-1231.
27.	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.

1. Zimmerman M S, Smith A, Sable C, et al. Global, regional, and national burden of congenital heart disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Child Adolesc, 2020, 4(3): 185-200.
2. Shahmohammadi M, Luo H, Westphal P, et al. Hemodynamics-driven mathematical model of first and second heart sound generation. PLOS Comput Biol, 2021, 17(9): 1009361.
3. Xu Weize, Yu Kai, Ye Jingjing, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artif Intell Med, 2022, 126: 102257.
4. Liu C, Springer D, Clifford G D. Performance of an open-source heart sound segmentation algorithm on eight independent databases. Physiol Meas, 2017, 38(8): 1730-1745.
5. 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). Vancouver: IEEE, 2016: 613-616.
6. Sujit N R, Kumar C S, Rajesh C B. Improving the performance of cardiac abnormality detection from PCG signal// 2016 AIP Conference Proceedings (AIPCP). Ekaterinburg: AIP, 2016: 020053.
7. Chen Q, Zhang W, Xiang T, et al. Automatic heart and lung sounds classification using convolutional neural networks// Signal and Information Processing Association Summit and Conference (APSIPA). Jeju: IEEE, 2016: 1-4.
8. Asmare M, Woldehanna F, Janssens L, et al. Rheumatic heart disease detection using deep learning from spectro-temporal representation of un-segmented heart sounds// 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Montreal: IEEE, 2020: 168-171.
9. Rubin J, Rui A, 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.
10. Khan F A, Abid A, Khan M S. Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features. Physiol Meas, 2020, 41(5): 055006.
11. Gao S, Zheng Y, Guo X. Gated recurrent unit-based heart sound analysis for heart failure screening. BioMed Eng OnLine, 2020, 19(1): 3.
12. Yousefi M, Hansen J. Block-based high performance CNN architectures for frame-level overlapping speech detection. IEEE-ACM T Audio Spe, 2020, 29: 28-40.
13. Chen Y, Lv J, Sun Y, et al. Heart sound segmentation via duration long-short term memory neural network. Appl Soft Comput, 2020, 95: 106540.
14. Astuti W, Sediono W, Aibinu A M, et al. Adaptive short time fourier transform (STFT) analysis of seismic electric signal (SES): a comparison of hamming and rectangular window// Industrial Electronics & Applications (IEA). Bandung: IEEE, 2013: 372-377.
15. Likitha M S, Gupta S, Hasitha K, et al. Speech based human emotion recognition using MFCC// International Conference on Wireless Communications (ICWC). Chennai: IEEE, 2017: 2257-2260.
16. Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Networks, 2020, 130(1): 22-32.
17. Yan T, Wang D, Zheng M, et al. Fisher’s discriminant ratio based health indicator for locating informative frequency bands for machine performance degradation assessment. Mech Syst Signal Pr, 2022, 162(9): 108053.
18. Lu F, Li H. KLDA–an iterative approach to Fisher siscriminant analysis// IEEE International Conference on Image Processing (ICIP). San Antonio: IEEE, 2007: 201-204.
19. Hu X H, Gansen Z, Lv J Z, et al. Isolated word speech recogniton based on HRSF and improved DTW algorithm// IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT). Macau: ACM, 2012: 270-273.
20. Xiao B, Xu Y, Bi X, et al. Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing, 2019, 392: 153-159.
21. Zhang W, Han J, Deng S. Abnormal heart sound detection using temporal quasi-periodic features and long short-term memory without segmentation. Biomed Signal Proces, 2019, 53: 101560.
22. Lim H I. A study on layers of deep neural networks// 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS). Singapore: IEEE, 2020: 31-34.
23. Lecun Y, Boser B, Denker J, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 2014, 1(4): 541-551.
24. Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzz, 1998, 6(2): 107-116.
25. Mirza A H. Online additive updates with FFT-IFFT operator on the GRU neural networks// Signal Processing and Communications Applications Conference (SIU). Izmir: IEEE, 2018: 1-4.
26. Dean J, Corrado G S, Monga R, et al. Large scale distributed deep networks. Adv Neural Inf Proces Syst, 2013, 1: 1223-1231.
27. 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.

Journal of Biomedical Engineering

Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method

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