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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (18), p.7084
• Main Authors: Zhang, Tie, Huang, Zequan, Zou, Yanbiao, Zhao, Jun, Ke, Yuwei
• Format: Article
• Language: English
• Subjects: Algorithms; Auscultation; Back propagation networks; bowel sounds; BP classifier; Classification; Data mining; Defecation; defecation prediction; Design; Detectors; feature extraction; Fecal incontinence; Feces; Frequency domain analysis; Gastrointestinal diseases; Hypotheses; Intestinal obstruction; Kalman filters; Kurtosis; Machine learning; Monitoring; Neural networks; Physiological aspects; Physiology; Prediction models; Sensors; Signal processing; Sound; Telemedicine; Time series; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22187084

(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.