Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing_Journal of Biomedical Engineering

Authors：

 MO Hongqiang ^1,2 , TIAN Xiang ³ , LI Bin ^1,2 , TIAN Junzhang ⁴

1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China;
2. Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City, Guangzhou 510641, P. R. China;
3. School of Electronics and Information Engineering, South China University of Technology, Guangzhou 510641, P. R. China;
4. Guangdong Second Provincial General Hospital, Guangzhou 510317, P. R. China;

Corresponding author：

MO Hongqiang, Email: hqiangmo@scut.edu.cn

Keywords：

Auscultation; Ambient-noise reduction; Normalized least-mean-square adaptive filtering; Linear preprocessing

DOI：

10.7507/1001-5515.202307058

Video：

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Abstract Full text Figures/Tables Video References Cited by

Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment or even divergence. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.

1.	Wang Gang, Yang Yingyun, Chen Siyu, et al. Flexible dual-channel digital auscultation patch with active noise reduction for bowel sound monitoring and application. IEEE J Biomed Health Inform 2022, 26(7): 2951-2962.
2.	Mallegni N, Molinari G, Ricci C, et al. Sensing devices for detecting and processing acoustic signals in healthcare. Biosensors, 2022, 12(10): 835.
3.	Yilmaz G, Rapin M, Pessoa D, et al. A wearable stethoscope for long-term ambulatory respiratory health monitoring. Sensors, 2020, 20(18): 5124.
4.	Ren Haoran, Jin Hailong, Chen Chen, et al. A novel cardiac auscultation monitoring system based on wireless sensing for healthcare. IEEE J Trans Eng Health Med, 2018, 6: 1-12.
5.	Seah J J, Zhao J, Wang D Y, et al. Review on the advancements of stethoscope types in chest auscultation. Diagnostics, 2023, 13(9): 1545.
6.	Emmanouilidou D, McCollum E D, Park D E, et al. Computerized lung sound screening for pediatric auscultation in noisy field environments. IEEE Trans Biomed Eng, 2018, 65(7): 1564-1574.
7.	Allwood G, Du X H, Webberley K M, et al. Advances in acoustic signal processing techniques for enhanced bowel sound analysis. IEEE Rev Biomed Eng, 2019, 12: 240-253.
8.	Joseph S R, Robert P J, Thomas P S, et al. Noise reduction stethoscope for United States navy application. Groton: Naval Submarine Medical Research Laboratory, 2000.
9.	Nelson G. Stethoscope design for auscultation in high noise environments. Minneapolis: University of Minnesota, 2015.
10.	Patel S B, Callahan T F, Callahan M G, et al. An adaptive noise reduction stethoscope for auscultation in high noise environments. J Acoust Soc Am, 1998, 103(5): 2483-2491.
11.	Nelson G, Rajamani R, Erdman A. Noise control challenges for auscultation on medical evacuation helicopters. Appl Acoust, 2014, 80: 68-78.
12.	Zhou Jin, Kwan C. Enhancing auscultation capability in spacecraft// Cong F, Leung A, Wei Q. Proceedings of the 14th International Symposium on Neural Networks. Lecture Notes in Computer Science. Cham: Springer, 2017, 10262: 416-426.
13.	Jiao Y Z, Cheung Y P R, Mok P C M. A modified Log-LMS adaptive filter with low signal distortion for biomedical applications// Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 5210-5213.
14.	Jiao Y Z, Cheung Y P R, Chow W Y W, et al. A novel gradient adaptive step size LMS algorithm with dual adaptive filters// Proceedings of the 35th Annual International Conference of the IEEE EMBS. Osaka: IEEE, 2013: 4803-4806.
15.	Jiao Y Z, Cheung Y P R, Chow W Y W, et al. Signed-gradient adaptive step size LMS algorithm for biomedical applications// Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 3208-3211.
16.	Zhu Wenzhao, Shi Shengguo, Luo Lei, et al. A novel adaptive state detector-based post-filtering active control algorithm for Gaussian noise environment with impulsive interference. Appl Sci, 2019, 9: 1176.
17.	Gansler T, Gay S L, Sondhi M M, et al. Double-talk robust fast converging algorithms for network echo cancellation. IEEE Trans Speech Audio Process, 2000, 8(6): 656-663.
18.	Vega L R, Rey H, Benesty J, et al. A new robust variable step-size NLMS algorithm. IEEE Trans Signal Process, 2008, 56(5): 1878-1893.
19.	Hur J, Lee M, Kim D, et al. A variable step-size robust saturation algorithm against impulsive noises. IEEE Trans Circuits Syst II, 2020, 67(10): 2279-2283.
20.	Shao M, Nikias C L. Signal processing with fractional lower order moments: stable processes and their applications. Proc IEEE, 1993, 81(7): 986-1010.
21.	Kalluri S, Arce G R. Adaptive weighted myriad filter algorithms for robust signal processing in α-stable noise environments. IEEE Trans Signal Process, 1998, 46(2): 322-334.
22.	Yang Jiali, Zhang Qiang, Luo Yongjiang, et al. A fractional-order gradient-descent total least mean p-norm adaptive filtering algorithm in impulsive noise environments. IEEE Trans Circuits Syst II, 2023, 70(3): 1204-1208.
23.	Song I, Park P, Newcomb R W. A normalized least mean squares algorithm with a step-size scaler against impulsive measurement noise. IEEE Trans Circuits Syst II, 2013, 60(7): 442-445.
24.	Zhou Jiajie, He Shunzhan, Mo Hongqiang, et al. A modified dual microphone adaptive filter for auscultation// Proceedings of the IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, Dalian: IEEE, 2019: 409-413.
25.	何顺展. 自适应降噪心音听诊系统开发与实现. 广州: 华南理工大学, 2020.
26.	Tarrab M, Feuer A. Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data. IEEE Trans Inf Theory, 1988, 34(4): 680-691.
27.	Kushner H J. Approximation and weak convergence methods for random processes with applications to stochastic system theory. Cambridge: MIT Press, 1984.
28.	王琪. 听诊器双通道自适应滤波器模型辨识方法研究与实现. 广州: 华南理工大学, 2022.

1. Wang Gang, Yang Yingyun, Chen Siyu, et al. Flexible dual-channel digital auscultation patch with active noise reduction for bowel sound monitoring and application. IEEE J Biomed Health Inform 2022, 26(7): 2951-2962.
2. Mallegni N, Molinari G, Ricci C, et al. Sensing devices for detecting and processing acoustic signals in healthcare. Biosensors, 2022, 12(10): 835.
3. Yilmaz G, Rapin M, Pessoa D, et al. A wearable stethoscope for long-term ambulatory respiratory health monitoring. Sensors, 2020, 20(18): 5124.
4. Ren Haoran, Jin Hailong, Chen Chen, et al. A novel cardiac auscultation monitoring system based on wireless sensing for healthcare. IEEE J Trans Eng Health Med, 2018, 6: 1-12.
5. Seah J J, Zhao J, Wang D Y, et al. Review on the advancements of stethoscope types in chest auscultation. Diagnostics, 2023, 13(9): 1545.
6. Emmanouilidou D, McCollum E D, Park D E, et al. Computerized lung sound screening for pediatric auscultation in noisy field environments. IEEE Trans Biomed Eng, 2018, 65(7): 1564-1574.
7. Allwood G, Du X H, Webberley K M, et al. Advances in acoustic signal processing techniques for enhanced bowel sound analysis. IEEE Rev Biomed Eng, 2019, 12: 240-253.
8. Joseph S R, Robert P J, Thomas P S, et al. Noise reduction stethoscope for United States navy application. Groton: Naval Submarine Medical Research Laboratory, 2000.
9. Nelson G. Stethoscope design for auscultation in high noise environments. Minneapolis: University of Minnesota, 2015.
10. Patel S B, Callahan T F, Callahan M G, et al. An adaptive noise reduction stethoscope for auscultation in high noise environments. J Acoust Soc Am, 1998, 103(5): 2483-2491.
11. Nelson G, Rajamani R, Erdman A. Noise control challenges for auscultation on medical evacuation helicopters. Appl Acoust, 2014, 80: 68-78.
12. Zhou Jin, Kwan C. Enhancing auscultation capability in spacecraft// Cong F, Leung A, Wei Q. Proceedings of the 14th International Symposium on Neural Networks. Lecture Notes in Computer Science. Cham: Springer, 2017, 10262: 416-426.
13. Jiao Y Z, Cheung Y P R, Mok P C M. A modified Log-LMS adaptive filter with low signal distortion for biomedical applications// Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 5210-5213.
14. Jiao Y Z, Cheung Y P R, Chow W Y W, et al. A novel gradient adaptive step size LMS algorithm with dual adaptive filters// Proceedings of the 35th Annual International Conference of the IEEE EMBS. Osaka: IEEE, 2013: 4803-4806.
15. Jiao Y Z, Cheung Y P R, Chow W Y W, et al. Signed-gradient adaptive step size LMS algorithm for biomedical applications// Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 3208-3211.
16. Zhu Wenzhao, Shi Shengguo, Luo Lei, et al. A novel adaptive state detector-based post-filtering active control algorithm for Gaussian noise environment with impulsive interference. Appl Sci, 2019, 9: 1176.
17. Gansler T, Gay S L, Sondhi M M, et al. Double-talk robust fast converging algorithms for network echo cancellation. IEEE Trans Speech Audio Process, 2000, 8(6): 656-663.
18. Vega L R, Rey H, Benesty J, et al. A new robust variable step-size NLMS algorithm. IEEE Trans Signal Process, 2008, 56(5): 1878-1893.
19. Hur J, Lee M, Kim D, et al. A variable step-size robust saturation algorithm against impulsive noises. IEEE Trans Circuits Syst II, 2020, 67(10): 2279-2283.
20. Shao M, Nikias C L. Signal processing with fractional lower order moments: stable processes and their applications. Proc IEEE, 1993, 81(7): 986-1010.
21. Kalluri S, Arce G R. Adaptive weighted myriad filter algorithms for robust signal processing in α-stable noise environments. IEEE Trans Signal Process, 1998, 46(2): 322-334.
22. Yang Jiali, Zhang Qiang, Luo Yongjiang, et al. A fractional-order gradient-descent total least mean p-norm adaptive filtering algorithm in impulsive noise environments. IEEE Trans Circuits Syst II, 2023, 70(3): 1204-1208.
23. Song I, Park P, Newcomb R W. A normalized least mean squares algorithm with a step-size scaler against impulsive measurement noise. IEEE Trans Circuits Syst II, 2013, 60(7): 442-445.
24. Zhou Jiajie, He Shunzhan, Mo Hongqiang, et al. A modified dual microphone adaptive filter for auscultation// Proceedings of the IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, Dalian: IEEE, 2019: 409-413.
25. 何顺展. 自适应降噪心音听诊系统开发与实现. 广州: 华南理工大学, 2020.
26. Tarrab M, Feuer A. Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data. IEEE Trans Inf Theory, 1988, 34(4): 680-691.
27. Kushner H J. Approximation and weak convergence methods for random processes with applications to stochastic system theory. Cambridge: MIT Press, 1984.
28. 王琪. 听诊器双通道自适应滤波器模型辨识方法研究与实现. 广州: 华南理工大学, 2022.

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