Snoring noise removal method for bowel sound signal during sleep_Journal of Biomedical Engineering

Authors：

WANG Guojing ^1,2,3 ,  WANG Weidong ^2,3 , LIU Hongyun ^2,3

1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China;
2. Medical Innovation Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China;
3. Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding author：

WANG Weidong, Email: wangweidong@301hospital.com.cn

Keywords：

Bowel sounds; Snoring noise; Complete ensemble empirical mode decomposition with adaptive noise; Denoising

DOI：

10.7507/1001-5515.202307055

Video：

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Monitoring of bowel sounds is an important method to assess bowel motility during sleep, but it is seriously affected by snoring noise. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was applied to remove snoring noise from bowel sounds during sleep. Specifically, the noisy bowel sounds were first band-pass filtered, then decomposed by the CEEMDAN method, and finally the appropriate components were selected to reconstruct the pure bowel sounds. The results of semi-simulated and real data showed that the CEEMDAN method was better than empirical mode decomposition and wavelet denoising method. The CEEMDAN method is used to remove snoring noise from bowel sounds during sleep, which lays an important foundation for using bowel sounds to assess the intestinal motility during sleep.

Citation： WANG Guojing, WANG Weidong, LIU Hongyun. Snoring noise removal method for bowel sound signal during sleep. Journal of Biomedical Engineering, 2024, 41(2): 288-294. doi: 10.7507/1001-5515.202307055 Copy

1.	Baranwal N, Yu P K, Siegel N S. Sleep physiology, pathophysiology, and sleep hygiene. Prog Cardiovasc Dis, 2023, 77: 59-69.
2.	Wang Z, Wang Z, Lu T, et al. The microbiota-gut-brain axis in sleep disorders. Sleep Med Rev, 2022, 65: 101691.
3.	Titos I, Juginović A, Vaccaro A, et al. A gut-secreted peptide suppresses arousability from sleep. Cell, 2023, 186(7): 1382-1397.e21.
4.	Wang G, Yang Y, Chen S, 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.
5.	Du X, Allwood G, Webberley K M, et al. Noninvasive diagnosis of irritable bowel syndrome via bowel sound features: proof of concept. Clin Transl Gastroen, 2019, 10(3): 1-9.
6.	Ozawa T, Saji E, Yajima R, et al. Reduced bowel sounds in Parkinson’s disease and multiple system atrophy patients. Clin Auton Res, 2011, 21(3): 181-184.
7.	Goto J, Matsuda K, Harii N, et al. Usefulness of a real-time bowel sound analysis system in patients with severe sepsis (pilot study). J Artif Organs, 2015, 18(1): 86-91.
8.	Ficek J, Radzikowski K, Nowak J K, et al. Analysis of gastrointestinal acoustic activity using deep neural networks. Sensors, 2021, 21(22): 7602.
9.	Kim K S, Seo J H, Ryu S H, et al. Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds. Comput Meth Prog Bio, 2011, 104(3): 426-434.
10.	Dimoulas C, Kalliris G, Papanikolaou G, et al. Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring. Expert Syst Appl, 2008, 34(1): 26-41.
11.	Hadjileontiadis L J. Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part I: methodology. IEEE T Bio-Med Eng, 2005, 52(6): 1143-1148.
12.	Kölle K, Aftab M F, Andersson L E, et al. Data driven filtering of bowel sounds using multivariate empirical mode decomposition. Biomed Eng Online, 2019, 18(1): 8-28.
13.	肖苗. 基于CEEMDAN排列熵的先心病心音特征提取与识别研究. 昆明: 云南大学, 2019.
14.	Ranta R, Louis-Dorr V, Heinrich C, et al. Digestive activity evaluation by multichannel abdominal sounds analysis. IEEE Trans Biomed Eng, 2010, 57(6): 1507-1519.
15.	Yoshino H, Abe Y, Yoshino T, et al. Clinical application of spectral analysis of bowel sounds in intestinal obstruction. Dis Colon Rectum, 1990, 33(9): 753-757.
16.	侯丽敏, 施晓宇, 童超, 等. 鼾声的基频分布与SAHS的关联性. 声学技术, 2019, 38(2): 176-181.
17.	Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London A, 1998, 454(1971): 903-995.
18.	Yu Y, Zhang H, Singh V. Forward prediction of runoff data in data-scarce basins with an improved ensemble empirical mode decomposition (EEMD) model. Water, 2018, 10(4): 388-403.
19.	Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise// 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague: IEEE, 2011: 4144-4147.
20.	荆钰霏, 李川涛, 王伟, 等. 基于CEEMDAN-CFAR的单通道脑电信号眼电伪迹去除方法研究. 医疗卫生装备, 2022, 43(4): 1-7.
21.	Du X, Allwood G, Webberley K, et al. Bowel sounds identification and migrating motor complex detection with low-cost piezoelectric acoustic sensing device. Sensors, 2018, 18(12): 4240-4252.
22.	王国静, 王卫东. 基于语音端点检测的全腹部肠鸣音信号识别. 中国医疗器械杂志, 2019, 43(2): 90-93.

1. Baranwal N, Yu P K, Siegel N S. Sleep physiology, pathophysiology, and sleep hygiene. Prog Cardiovasc Dis, 2023, 77: 59-69.
2. Wang Z, Wang Z, Lu T, et al. The microbiota-gut-brain axis in sleep disorders. Sleep Med Rev, 2022, 65: 101691.
3. Titos I, Juginović A, Vaccaro A, et al. A gut-secreted peptide suppresses arousability from sleep. Cell, 2023, 186(7): 1382-1397.e21.
4. Wang G, Yang Y, Chen S, 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.
5. Du X, Allwood G, Webberley K M, et al. Noninvasive diagnosis of irritable bowel syndrome via bowel sound features: proof of concept. Clin Transl Gastroen, 2019, 10(3): 1-9.
6. Ozawa T, Saji E, Yajima R, et al. Reduced bowel sounds in Parkinson’s disease and multiple system atrophy patients. Clin Auton Res, 2011, 21(3): 181-184.
7. Goto J, Matsuda K, Harii N, et al. Usefulness of a real-time bowel sound analysis system in patients with severe sepsis (pilot study). J Artif Organs, 2015, 18(1): 86-91.
8. Ficek J, Radzikowski K, Nowak J K, et al. Analysis of gastrointestinal acoustic activity using deep neural networks. Sensors, 2021, 21(22): 7602.
9. Kim K S, Seo J H, Ryu S H, et al. Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds. Comput Meth Prog Bio, 2011, 104(3): 426-434.
10. Dimoulas C, Kalliris G, Papanikolaou G, et al. Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring. Expert Syst Appl, 2008, 34(1): 26-41.
11. Hadjileontiadis L J. Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part I: methodology. IEEE T Bio-Med Eng, 2005, 52(6): 1143-1148.
12. Kölle K, Aftab M F, Andersson L E, et al. Data driven filtering of bowel sounds using multivariate empirical mode decomposition. Biomed Eng Online, 2019, 18(1): 8-28.
13. 肖苗. 基于CEEMDAN排列熵的先心病心音特征提取与识别研究. 昆明: 云南大学, 2019.
14. Ranta R, Louis-Dorr V, Heinrich C, et al. Digestive activity evaluation by multichannel abdominal sounds analysis. IEEE Trans Biomed Eng, 2010, 57(6): 1507-1519.
15. Yoshino H, Abe Y, Yoshino T, et al. Clinical application of spectral analysis of bowel sounds in intestinal obstruction. Dis Colon Rectum, 1990, 33(9): 753-757.
16. 侯丽敏, 施晓宇, 童超, 等. 鼾声的基频分布与SAHS的关联性. 声学技术, 2019, 38(2): 176-181.
17. Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London A, 1998, 454(1971): 903-995.
18. Yu Y, Zhang H, Singh V. Forward prediction of runoff data in data-scarce basins with an improved ensemble empirical mode decomposition (EEMD) model. Water, 2018, 10(4): 388-403.
19. Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise// 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague: IEEE, 2011: 4144-4147.
20. 荆钰霏, 李川涛, 王伟, 等. 基于CEEMDAN-CFAR的单通道脑电信号眼电伪迹去除方法研究. 医疗卫生装备, 2022, 43(4): 1-7.
21. Du X, Allwood G, Webberley K, et al. Bowel sounds identification and migrating motor complex detection with low-cost piezoelectric acoustic sensing device. Sensors, 2018, 18(12): 4240-4252.
22. 王国静, 王卫东. 基于语音端点检测的全腹部肠鸣音信号识别. 中国医疗器械杂志, 2019, 43(2): 90-93.

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Snoring noise removal method for bowel sound signal during sleep

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