Skip to main content
SpringerLink
Log in

Deep residual attention network for human defecation prediction using bowel sounds

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Background and objective

Fecal incontinence may lead to incontinence-associated dermatitis (IAD), affecting the physical health of the patient. Since human defecation is related to intestinal activity, and bowel sounds can reflect bowel motility, a prediction method for human defecation based on deep residual attention network (DRAN) using bowel sounds was proposed to prevent IAD.

Methods

We collected 1140 bowel sounds of 20 seconds from 15 volunteers. These bowel sounds were transformed into time-frequency maps by wavelet packet transform (WPT). Then the time-frequency maps are taken as input to the DRAN. DRAN classified bowel sounds to predict whether the patient would defecate.

Results

After training, the defecation prediction accuracy, precision and recall of DRAN could reach 91.18%, 91.67% and 90.59% respectively,

Conclusion

The result indicates that the proposed method could provide a high-precision defecation prediction result for patients with fecal incontinence, so as to prepare for defecation in advance and prevent the occurrence of IAD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Brown HW, Dyer KY, Rogers RG (2020) Management of Fecal Incontinence. Obstet Gynecol 136(4):811–822

    Article  Google Scholar 

  2. Banharak S et al (2021) Prevention and Care for Incontinence-Associated Dermatitis Among Older Adults: A Systematic Review. J Multidiscip Healthc 14:2983–3004

    Article  Google Scholar 

  3. Zhang Y et al (2021) The effectiveness of faecal collection devices in preventing incontinence-associated dermatitis in critically ill patients with faecal incontinence: A systematic review and meta-analysis. Austr Critical Care 34(1):103–112

    Article  Google Scholar 

  4. Lim Y, Carville K (2019) Prevention and Management of Incontinence-Associated Dermatitis in the Pediatric Population: An Integrative Review. J Wound Ostomy Continence Nurs 46(1):30–37

    Article  Google Scholar 

  5. Coyer F, Campbell J (2018) Incontinence-associated dermatitis in the critically ill patient: an intensive care perspective. Nursing Critical Care 23(4):198–206

    Article  Google Scholar 

  6. Gray M et al (2018) Assessment, Selection, Use, and Evaluation of Body-Worn Absorbent Products for Adults With Incontinence: A WOCN Society Consensus Conference. J Wound Ostomy Continence Nursing 45:243–264

    Article  Google Scholar 

  7. Denat Y, Khorshid L (2011) The Effect of 2 Different Care Products on Incontinence-Associated Dermatitis in Patients With Fecal Incontinence. J Wound Ostomy Continence Nursing 38(2):171–176

    Article  Google Scholar 

  8. Mugita Y et al (2021) Assessing absorbent products' effectiveness for the prevention and management of incontinence-associated dermatitis caused by urinary, faecal or double adult incontinence: A systematic review. J Tissue Viabil 30(4):599–607

    Article  Google Scholar 

  9. Sung VW et al (2014) Symptom Outcomes Important to Women With Anal Incontinence A Conceptual Framework. Obstet Gynecol 123(5):1023–1030

    Article  Google Scholar 

  10. Bampton PA et al (2000) Spatial and temporal organization of pressure patterns throughout the unprepared colon during spontaneous defecation. Am J Gastroenterol 95(4):1027–1035

    Article  Google Scholar 

  11. Crowell MD et al (1991) Method for prolonged ambulatory monitoring of high-amplitude propagated contractions from colon. Am J Phys 261(2 Pt 1):G263–G268

    Google Scholar 

  12. Palit S, Lunniss PJ, Scott SM (2012) The Physiology of Human Defecation. Dig Dis Sci 57(6):1445–1464

    Article  Google Scholar 

  13. Kim KS, Seo JH, Song CG (2011) Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds. Biomed Eng Online 10

  14. Nowak JK et al. (2021) Automated Bowel Sound Analysis: An Overview. Sensors 21(16)

  15. Du, X.H., et al. (2018) Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device. Sensors 18(12)

  16. Ching SS, Tan YK (2012) Spectral analysis of bowel sounds in intestinal obstruction using an electronic stethoscope. World J Gastroenterol 18(33):4585–4592

    Article  Google Scholar 

  17. Ulusar UD (2014) Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics. Comput Biol Med 51:223–228

    Article  Google Scholar 

  18. Dimoulas C et al (2008) Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring. Expert Syst Appl 34(1):26–41

    Article  Google Scholar 

  19. Lin BS et al (2013) Enhancing Bowel Sounds by Using a Higher Order Statistics-Based Radial Basis Function Network. IEEE J Biomed Health Inform 17(3):675–680

    Article  Google Scholar 

  20. Hassan SA et al (2020) Breast cancer masses classification using deep convolutional neural networks and transfer learning. Multimed Tools Appl 79(41-42):30735–30768

    Article  Google Scholar 

  21. Tan JH et al (2018) Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput Biol Med 94:19–26

    Article  Google Scholar 

  22. Juzheng L et al. (2018) Bowel Sound Detection Based on MFCC Feature and LSTM Neural Network. IEEE Biomedical Circuits and Systems Conference (BioCAS) , pp 1-4

  23. He KM et al. (2016) Deep Residual Learning for Image Recognition. 2016 Ieee Conference on Computer Vision and Pattern Recognition (Cvpr), pp 770-778

  24. Hu J, Shen L, Sun G (2018) Squeeze-and-Excitation Networks. 2018 Ieee/Cvf Conference on Computer Vision and Pattern Recognition (Cvpr), pp 7132-7141

  25. Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans Instrum Meas 53(2):431–436

    Article  Google Scholar 

  26. Ngoc PP et al. (2015) EEG Signal Analysis and Artifact Removal by Wavelet Transform. 5th International Conference on Biomedical Engineering in Vietnam 46: 179-183

  27. Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 39:94–102

    Article  Google Scholar 

  28. Tu W, Wei QG (2009) Classification of three-class motor imagery EEG data by combining wavelet packet decomposition and common spatial pattern. 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics 1: 188-191

  29. Yang B et al (2016) Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces. Comput Methods Prog Biomed 129:21–28

    Article  Google Scholar 

  30. Tikkanen PE, Sellin LC (1997) Wavelet and wavelet packet decomposition of RR and RTmax interval time series. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Magnificent Milestones Emerg Opportunities Med Eng 1:313–316

    Article  Google Scholar 

  31. Yildirim O, Baloglu UB, Acharya UR (2020) A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Applic 32(20):15857–15868

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Major Science and Technology Project of China [grant numbers 2020YFC2007600].

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Tie Zhang, Yuanhang Yang, Yanbiao Zou & Shenghong Wu

  2. China Rehabilitation Research Center, Beijing, People’s Republic of China

    Jun Zhao

Authors
  1. Tie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanhang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanbiao Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shenghong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tie Zhang.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Yang, Y., Zou, Y. et al. Deep residual attention network for human defecation prediction using bowel sounds. Multimed Tools Appl 83, 36097–36113 (2024). https://doi.org/10.1007/s11042-023-17091-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17091-1

Keywords

Search

Navigation