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A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-i...

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Autores principales: Zhang, Tie, Huang, Zequan, Zou, Yanbiao, Zhao, Jun, Ke, Yuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501137/
https://www.ncbi.nlm.nih.gov/pubmed/36146430
http://dx.doi.org/10.3390/s22187084
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author Zhang, Tie
Huang, Zequan
Zou, Yanbiao
Zhao, Jun
Ke, Yuwei
author_facet Zhang, Tie
Huang, Zequan
Zou, Yanbiao
Zhao, Jun
Ke, Yuwei
author_sort Zhang, Tie
collection PubMed
description (1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time–frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: 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.
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spelling pubmed-95011372022-09-24 A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features Zhang, Tie Huang, Zequan Zou, Yanbiao Zhao, Jun Ke, Yuwei Sensors (Basel) Article (1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time–frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: 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. MDPI 2022-09-19 /pmc/articles/PMC9501137/ /pubmed/36146430 http://dx.doi.org/10.3390/s22187084 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Tie
Huang, Zequan
Zou, Yanbiao
Zhao, Jun
Ke, Yuwei
A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title_full A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title_fullStr A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title_full_unstemmed A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title_short A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features
title_sort prediction model of defecation based on bp neural network and bowel sound signal features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501137/
https://www.ncbi.nlm.nih.gov/pubmed/36146430
http://dx.doi.org/10.3390/s22187084
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