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Sun YH, Song YY, Sha S, Sun Q, Huang DC, Gao L, Li H, Shi QD. Diagnostic value of digital continuous bowel sounds in critically ill patients with acute gastrointestinal injury: A prospective observational study. World J Gastrointest Surg 2024; 16:3818-3834. [PMID: 39734468 PMCID: PMC11650232 DOI: 10.4240/wjgs.v16.i12.3818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 09/05/2024] [Accepted: 10/22/2024] [Indexed: 11/27/2024] Open
Abstract
BACKGROUND Acute gastrointestinal injury (AGI) is common in intensive care unit (ICU) and worsens the prognosis of critically ill patients. The four-point grading system proposed by the European Society of Intensive Care Medicine is subjective and lacks specificity. Therefore, a more objective method is required to evaluate and determine the grade of gastrointestinal dysfunction in this patient population. Digital continuous monitoring of bowel sounds and some biomarkers can change in gastrointestinal injuries. We aimed to develop a model of AGI using continuous monitoring of bowel sounds and biomarkers. AIM To develop a model to discriminate AGI by monitoring bowel sounds and biomarker indicators. METHODS We conducted a prospective observational study with 75 patients in an ICU of a tertiary-care hospital to create a diagnostic model for AGI. We recorded their bowel sounds, assessed AGI grading, collected clinical data, and measured biomarkers. We evaluated the model using misjudgment probability and leave-one-out cross-validation. RESULTS Mean bowel sound rate and citrulline level are independent risk factors for AGI. Gastrin was identified as a risk factor for the severity of AGI. Other factors that correlated with AGI include mean bowel sound rate, amplitude, interval time, Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, platelet count, total protein level, blood gas potential of hydrogen (pH), and bicarbonate (HCO3 -) level. Two discriminant models were constructed with a misclassification probability of < 0.1. Leave-one-out cross-validation correctly classified 69.8% of the cases. CONCLUSION Our AGI diagnostic model represents a potentially effective approach for clinical AGI grading and holds promise as an objective diagnostic standard for AGI.
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Affiliation(s)
- Yuan-Hui Sun
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
- Shaanxi Province Key Laboratory of Sepsis in Critical Care Medical, Xi'an 710061, Shaanxi Province, China
| | - Yun-Yun Song
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Sha Sha
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Qi Sun
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Deng-Chao Huang
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Lan Gao
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
- Shaanxi Province Key Laboratory of Sepsis in Critical Care Medical, Xi'an 710061, Shaanxi Province, China
| | - Hao Li
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
- Shaanxi Province Key Laboratory of Sepsis in Critical Care Medical, Xi'an 710061, Shaanxi Province, China
| | - Qin-Dong Shi
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
- Shaanxi Province Key Laboratory of Sepsis in Critical Care Medical, Xi'an 710061, Shaanxi Province, China
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Tang Y, Shi P, Yu H. Perception of defecation intent: applied methods and technology trends. BIOMED ENG-BIOMED TE 2024; 69:535-549. [PMID: 38953780 DOI: 10.1515/bmt-2024-0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/10/2024] [Indexed: 07/04/2024]
Abstract
The aging population has led to a widening gap between the supply and demand for defecation care. To address this issue, the development of defecation care devices is the most direct and effective solution. Pre-defecation care devices offer a more personalized and comfortable alternative to the conventional post-defecation care devices currently available on the market. Furthermore, they facilitate greater patient involvement in the care process. Real-time monitoring and accurate identification of defecation intention are key technologies in the development of pre-defecation nursing devices. Automatic and accurate online monitoring of defecation intention can provide accurate early warning information for differentiated defecation assistance and cleansing care, effectively reducing nursing workload and improving patients' quality of life. However, there are relatively few studies on real-time monitoring and accurate identification of defecation intention. This review summarizes the existing defecation intention sensing technologies and their monitoring principles and research status, and explores the potential development direction of defecation intention sensing systems by comparing the characteristics and application conditions of various sensing technologies, which provides a direction for perception strategies for future defecation intention monitoring and early warning research.
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Affiliation(s)
- Yi Tang
- Institute of Rehabilitation Engineering and Technology, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Ping Shi
- Institute of Rehabilitation Engineering and Technology, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Hongliu Yu
- Institute of Rehabilitation Engineering and Technology, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai Engineering Research Center of Assistive Devices, University of Shanghai for Science and Technology, Shanghai, China
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Liu C, Wu L, Xu R, Jiang Z, Xiao X, Song N, Jin Q, Dai Z. Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study. Br J Hosp Med (Lond) 2024; 85:1-15. [PMID: 39212577 DOI: 10.12968/hmed.2024.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational study. The primary objective of the model was to enhance clinical decision-making by providing a more precise assessment of ENAD risk. Methods The study enrolled patients with AP who underwent early enteral nutrition (EN). Real-time collection and analysis of bowel sounds were conducted using an artificial intelligence bowel sounds auscultation system. Univariate analysis, multicollinearity analysis, and logistic regression analysis were employed to identify risk factors associated with ENAD. The random forest algorithm was utilised to establish the prediction model, and partial dependence plots were generated to analyse the impact of risk factors on ENAD risk. Validation of the model was performed using the optimal model Bootstrap resampling method. Predictive performance was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and an area under the receiver operating characteristic (ROC) curve. Results Among the 133 patients included in the study, the incidence of ENAD was 44.4%. Six risk factors were identified, and the model's accuracy was validated through Bootstrap iterations. The prediction accuracy of the model was 81.10%, with a sensitivity of 84.30% and a specificity of 77.80%. The positive predictive value was 82.60%, and the negative predictive value was 80.10%. The area under the ROC curve was 0.904 (95% confidence interval: 0.817-0.997). Conclusion The artificial intelligence bowel sounds auscultation system enhances the assessment of gastrointestinal function in AP patients undergoing EN and facilitates the construction of an ENAD predictive model. The model demonstrates good predictive efficacy, offering an objective basis for precise intervention timing in ENAD management.
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Affiliation(s)
- Chengcheng Liu
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Li Wu
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Rui Xu
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Zhiwei Jiang
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xiaoping Xiao
- Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Nian Song
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Qianhong Jin
- School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Zhengxiang Dai
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
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Zhang T, Hong C, Zou Y, Zhao J. Prediction method of human defecation based on informer audio data augmentation and improved residual network. Heliyon 2024; 10:e34145. [PMID: 39100450 PMCID: PMC11295864 DOI: 10.1016/j.heliyon.2024.e34145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 06/16/2024] [Accepted: 07/04/2024] [Indexed: 08/06/2024] Open
Abstract
Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent. Therefore, a deep learning-based bowel sound recognition method is proposed for human defecation prediction. The wavelet domain based Wiener filter is used to filter the bowel sound data to reduce other noise. Statistical analysis, fast Fourier transform and wavelet packet transform are used to extract the integrated features of bowel sound in time, frequency and time-frequency domain. In particular, an audio signal expansion data algorithm based on the Informer model is proposed to solve the problem of poor generalization of the training model caused by the difficulty of collecting bowel sound in reality. An improved one-dimensional residual network model (1D-IResNet) for defecation classification prediction is designed based on multi-domain features. The experimental results show that the proposed bowel sound augmentation strategy can effectively improve the data sample size and increase the sample diversity. Under the augmented dataset, the training speed of the 1D-IResNet model is accelerated, and the classification accuracy reaches 90.54 %, the F1 score reaches 83.88 %, which achieves a relatively good classification stability while maintaining a high classification index.
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Affiliation(s)
- Tie Zhang
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Cong Hong
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yanbiao Zou
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Jun Zhao
- China Rehabilitation Research Center, Beijing, 100000, China
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Wang G, Chen Y, Liu H, Yu X, Han Y, Wang W, Kang H. Differences in intestinal motility during different sleep stages based on long-term bowel sounds. Biomed Eng Online 2023; 22:105. [PMID: 37919731 PMCID: PMC10623717 DOI: 10.1186/s12938-023-01166-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND AND OBJECTIVES This study focused on changes in intestinal motility during different sleep stages based on long-term bowel sounds. METHODS A modified higher order statistics algorithm was devised to identify the effective bowel sound segments. Next, characteristic values (CVs) were extracted from each bowel sound segment, which included 4 time-domain, 4 frequency-domain and 2 nonlinear CVs. The statistical analysis of these CVs corresponding to the different sleep stages could be used to evaluate the changes in intestinal motility during sleep. RESULTS A total of 6865.81 min of data were recorded from 14 participants, including both polysomnographic data and bowel sound data which were recorded simultaneously from each participant. The average accuracy, sensitivity and specificity of the modified higher order statistics detector were 96.46 ± 2.60%, 97.24 ± 2.99% and 94.13 ± 4.37%. In addition, 217088 segments of effective bowel sound corresponding to different sleep stages were identified using the modified detector. Most of the CVs were statistically different during different sleep stages ([Formula: see text]). Furthermore, the bowel sounds were low in frequency based on frequency-domain CVs, high in energy based on time-domain CVs and low in complexity base on nonlinear CVs during deep sleep, which was consistent with the state of the EEG signals during deep sleep. CONCLUSIONS The intestinal motility varies by different sleep stages based on long-term bowel sounds using the modified higher order statistics detector. The study indicates that the long-term bowel sounds can well reflect intestinal motility during sleep. This study also demonstrates that it is technically feasible to simultaneously record intestinal motility and sleep state throughout the night. This offers great potential for future studies investigating intestinal motility during sleep and related clinical applications.
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Affiliation(s)
- Guojing Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Yibing Chen
- Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Hongyun Liu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Xiaohua Yu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Yi Han
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Weidong Wang
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China.
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China.
| | - Hongyan Kang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
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Redij R, Kaur A, Muddaloor P, Sethi AK, Aedma K, Rajagopal A, Gopalakrishnan K, Yadav A, Damani DN, Chedid VG, Wang XJ, Aakre CA, Ryu AJ, Arunachalam SP. Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:2302. [PMID: 36850899 PMCID: PMC9967043 DOI: 10.3390/s23042302] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.
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Affiliation(s)
- Renisha Redij
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Avneet Kaur
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pratyusha Muddaloor
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Arshia K. Sethi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Keirthana Aedma
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashima Yadav
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi N. Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Victor G. Chedid
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Xiao Jing Wang
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Shivaram P. Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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Kutsumi Y, Kanegawa N, Zeida M, Matsubara H, Murayama N. Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone. SENSORS (BASEL, SWITZERLAND) 2022; 23:407. [PMID: 36617005 PMCID: PMC9824196 DOI: 10.3390/s23010407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.
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Zhang T, Huang Z, Zou Y, Zhao J, Ke Y. A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features. SENSORS (BASEL, SWITZERLAND) 2022; 22:7084. [PMID: 36146430 PMCID: PMC9501137 DOI: 10.3390/s22187084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/09/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
(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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Affiliation(s)
- Tie Zhang
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Zequan Huang
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yanbiao Zou
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
| | - Jun Zhao
- China Rehabilitation Research Center, Beijing 100000, China
| | - Yuwei Ke
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
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A Human Defecation Prediction Method Based on Multi-Domain Features and Improved Support Vector Machine. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The difficulty of defecation seriously affects the quality of life of the bedridden elderly. To solve the problem that it is difficult to know the defecation time of the bedridden elderly, this paper proposed a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM) using bowel sound as the original signal. The method includes three stages: multi-domain features extraction, feature optimization, and defecation prediction. In the stage of multi-domain features extraction, statistical analysis, fast Fourier transform (FFT), and wavelet packet transform are used to extract feature information in the time domain, frequency domain, and time-frequency domain. The symmetry of the bowel sound signal in the time domain, frequency domain, and time-frequency domain will change when the human has the urge to defecate. In the feature optimization stage, the Fisher Score (FS) algorithm is introduced to select meaningful and sensitive features according to the importance of each feature, aiming to remove redundant information and improve computational efficiency. In the stage of defecation prediction, SVM is optimized by the gray wolf optimization (GWO) algorithm to realize human defecation prediction. Finally, experimental analysis of the bowel sound data collected during the study is carried out. The experimental result shows that the proposed method could achieve an accuracy of 92.86% in defecation prediction, which proves the effectiveness of the proposed method.
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Wang G, Yang Y, Chen S, Fu J, Wu D, Yang A, Ma Y, Feng X. Flexible dual-channel digital auscultation patch with active noise reduction for bowel sound monitoring and application. IEEE J Biomed Health Inform 2022; 26:2951-2962. [PMID: 35171784 DOI: 10.1109/jbhi.2022.3151927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Bowel sounds (BSs) have important clinical value in the auxiliary diagnosis of digestive diseases, but due to the inconvenience of long-term monitoring and too much interference from environmental noise, they have not been well studied. Most of the current electronic stethoscopes are hard and bulky without the function of noise reduction, and their application for long-term wearable monitoring of BS in noisy clinical environments is very limited. In this paper, a flexible dual-channel digital auscultation patch with active noise reduction is designed and developed, which is wireless, wearable, and conformably attached to abdominal skin to record BS more accurately. The ambient noise can be greatly reduced through active noise reduction based on the adaptive filter. At the same time, some nonstationary noises appearing intermittently (e.g., frictional noise) can also be removed from BS by the cross validation of multichannel simultaneous acquisition. Then, two kinds of typical BS signals are taken as examples, and the feature parameters of the BS in the time domain and frequency domain are extracted through the time-frequency analysis algorithm. Furthermore, based on the short-term energy ratio between the four channels of dual patches, the two-dimensional localization of BS on the abdomen mapping plane is realized. Finally, the continuous wearable monitoring of BS for patients with postoperative ileus (POI) in the noisy ward from pre-operation (POD0) to postoperative Day 7 (POD7) was carried out. The obtained change curve of the occurrence frequency of BS provides guidance for doctors to choose a reasonable feeding time for patients after surgery and accelerate their recovery. Therefore, flexible dual-channel digital auscultation patches with active noise reduction will have promising applications in the clinical auxiliary diagnosis of digestive diseases.
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Ficek J, Radzikowski K, Nowak JK, Yoshie O, Walkowiak J, Nowak R. Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:7602. [PMID: 34833679 PMCID: PMC8618847 DOI: 10.3390/s21227602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022]
Abstract
Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.
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Affiliation(s)
- Jakub Ficek
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
| | - Kacper Radzikowski
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
- Graduate School of Information, Production and Systems, Waseda University, Tokyo 169-8050, Japan;
| | - Jan Krzysztof Nowak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland; (J.K.N.); (J.W.)
| | - Osamu Yoshie
- Graduate School of Information, Production and Systems, Waseda University, Tokyo 169-8050, Japan;
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland; (J.K.N.); (J.W.)
| | - Robert Nowak
- Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.F.); (K.R.)
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Bilionis I, Apostolidis G, Charisis V, Liatsos C, Hadjileontiadis L. Non-invasive Detection of Bowel Sounds in Real-life Settings Using Spectrogram Zeros and Autoencoding. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:915-919. [PMID: 34891439 DOI: 10.1109/embc46164.2021.9630783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gastrointestinal (GI) diseases are amongst the most painful and dangerous clinical cases, due to inefficient recognition of symptoms and thus, lack of early-diagnostic tools. The analysis of bowel sounds (BS) has been fundamental for GI diseases, however their long-term recordings require technical and clinical resources along with the patientt's motionless concurrence throughout the auscultation procedure. In this study, an end-to-end non-invasive solution is proposed to detect BS in real-life settings utilizing a smart-belt apparatus along with advanced signal processing and deep neural network algorithms. Thus, high rate of BS identification and separation from other domestic and urban sounds have been achieved over the realization of an experiment where BS recordings were collected and analyzed out of 10 student volunteers.
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14
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Nowak JK, Nowak R, Radzikowski K, Grulkowski I, Walkowiak J. Automated Bowel Sound Analysis: An Overview. SENSORS (BASEL, SWITZERLAND) 2021; 21:5294. [PMID: 34450735 PMCID: PMC8400220 DOI: 10.3390/s21165294] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 11/24/2022]
Abstract
Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.
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Affiliation(s)
- Jan Krzysztof Nowak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
| | - Robert Nowak
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Kacper Radzikowski
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Ireneusz Grulkowski
- Faculty of Physics, Astronomy and Informatics, Institute of Physics, Nicolaus Copernicus University, 87-100 Toruń, Poland;
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
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Abstract
Irritable bowel syndrome (IBS) is a common and debilitating disorder estimated to affect approximately 11% of the world's population. Typically, IBS is a diagnosis of exclusion after patients undergo a costly and invasive colonoscopy to exclude organic disease. Clinician's and researchers have identified a need for a new cost-effective, accurate, and noninvasive diagnostic test for IBS.
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16
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Allwood G, Du X, Webberley KM, Osseiran A, Marshall BJ. Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis. IEEE Rev Biomed Eng 2019; 12:240-253. [DOI: 10.1109/rbme.2018.2874037] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Du X, Allwood G, Webberley KM, Osseiran A, Marshall BJ. Bowel Sounds Identification and Migrating Motor Complex Detection with Low-Cost Piezoelectric Acoustic Sensing Device. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4240. [PMID: 30513934 PMCID: PMC6308494 DOI: 10.3390/s18124240] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 11/22/2018] [Accepted: 11/29/2018] [Indexed: 12/16/2022]
Abstract
Interpretation of bowel sounds (BS) provides a convenient and non-invasive technique to aid in the diagnosis of gastrointestinal (GI) conditions. However, the approach's potential is limited by variation between BS and their irregular occurrence. A short, manual auscultation is sufficient to aid in diagnosis of only a few conditions. A longer recording has the potential to unlock additional understanding of GI physiology and clinical utility. In this paper, a low-cost and straightforward piezoelectric acoustic sensing device was designed and used for long BS recordings. The migrating motor complex (MMC) cycle was detected using this device and the sound index as the biomarker for MMC phases. This cycle of recurring motility is typically measured using expensive and invasive equipment. We also used our recordings to develop an improved categorization system for BS. Five different types of BS were extracted: the single burst, multiple bursts, continuous random sound, harmonic sound, and their combination. Their acoustic characteristics and distribution are described. The quantities of different BS during two-hour recordings varied considerably from person to person, while the proportions of different types were consistent. The sensing devices provide a useful tool for MMC detection and study of GI physiology and function.
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Affiliation(s)
- Xuhao Du
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Gary Allwood
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Katherine Mary Webberley
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
| | - Adam Osseiran
- School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia.
| | - Barry J Marshall
- The Marshall Centre for Infectious Diseases Research and Training (M504), The University of Western Australia, Crawley, WA 6009, Australia.
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18
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Inderjeeth AJ, Webberley KM, Muir J, Marshall BJ. The potential of computerised analysis of bowel sounds for diagnosis of gastrointestinal conditions: a systematic review. Syst Rev 2018; 7:124. [PMID: 30115115 PMCID: PMC6097214 DOI: 10.1186/s13643-018-0789-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 07/30/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Gastrointestinal (GI) conditions are highly prevalent, and their standard diagnostic tests are costly and carry risks. There is a need for new, cost-effective, non-invasive tests. Our main objective was to assess the potential for use of bowel sounds computerised analysis in the diagnosis of GI conditions. METHODS The systematic review followed the PRISMA requirements. Searches were made of four databases (PubMed, MEDLINE, Embase, and IEEE Xplore) and the references of included papers. Studies of all types were included. The titles and abstracts were screened by one author. Full articles were reviewed and data collected by two authors independently. A third reviewer decided on inclusion in the event of disagreement. Bias and applicability were assessed via a QUADAS tool adapted to accommodate studies of multiple types. RESULTS Two thousand eight hundred eighty-four studies were retrieved; however, only 14 studies were included. Most of these simply assessed associations between a bowel sound feature and a condition. Four studies also included assessments of diagnostic accuracy. We found many significant associations between a bowel sound feature and a GI condition. Receiver operating characteristic curve analyses revealed high sensitivity and specificity for an irritable bowel syndrome test, and a high negative predictive value for a test for post-operative ileus. Assessment of methodological quality identified weaknesses in all studies. We particularly noted a high risk of bias in patient selection. Because of the limited number of trials included and the variety in conditions, technology, and statistics, we were unable to conduct pooled analyses. CONCLUSIONS Due to concerns over quality and small sample sizes, we cannot yet recommend an existing BSCA diagnostic test without additional studies. However, the preliminary results found in the included studies and the technological advances described in excluded studies indicate excellent future potential. Research combining sophistical clinical and engineering skills is likely to be fruitful. SYSTEMATIC REVIEW REGISTRATION The review protocol (review ID number 42016054028) was developed by three authors (AI, KMW, and JM) and was published in the PROSPERO International prospective register of systematic reviews. It can be accessed from https://www.crd.york.ac.uk/PROSPERO/ .
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Affiliation(s)
- Andrisha-Jade Inderjeeth
- North Metropolitan Health Service, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.,The Marshall Centre for Infectious Diseases Research and Training, School of Biomedical Sciences, QEII Medical Site, The University of Western Australia, Perth, Western Australia, Australia
| | - K Mary Webberley
- The Marshall Centre for Infectious Diseases Research and Training, School of Biomedical Sciences, QEII Medical Site, The University of Western Australia, Perth, Western Australia, Australia.
| | - Josephine Muir
- The Marshall Centre for Infectious Diseases Research and Training, School of Biomedical Sciences, QEII Medical Site, The University of Western Australia, Perth, Western Australia, Australia
| | - Barry J Marshall
- North Metropolitan Health Service, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.,The Marshall Centre for Infectious Diseases Research and Training, School of Biomedical Sciences, QEII Medical Site, The University of Western Australia, Perth, Western Australia, Australia
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19
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Emoto T, Abeyratne UR, Gojima Y, Nanba K, Sogabe M, Okahisa T, Akutagawa M, Konaka S, Kinouchi Y. Evaluation of human bowel motility using non-contact microphones. Biomed Phys Eng Express 2016. [DOI: 10.1088/2057-1976/2/4/045012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Ulusar UD. Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics. Comput Biol Med 2014; 51:223-8. [PMID: 24971526 DOI: 10.1016/j.compbiomed.2014.05.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 05/29/2014] [Accepted: 05/30/2014] [Indexed: 11/19/2022]
Abstract
Loss of gastrointestinal motility is a significant medical setback for patients who experience abdominal surgery and contributes to the most common reason for prolonged hospital stays. Recent clinical studies suggest that initiating feeding early after abdominal surgery is beneficial. Early feeding is possible when the patients demonstrate bowel motility in the form of bowel sounds (BS). This work provides a data collection, processing and analysis methodology for detection of recovery of gastrointestinal track motility by observing BSs in auscultation recordings. The approach is suitable for real-time long-term continuous monitoring in clinical environments. The system was developed using a Naive Bayesian algorithm for pattern classification, and Minimum Statistics and spectral subtraction for noise attenuation. The solution was tested on 59h of recordings and 94.15% recognition accuracy was observed.
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Affiliation(s)
- Umit D Ulusar
- Computer Engineering Department, Engineering Faculty, Akdeniz University Kampus, 07058 Antalya, Turkey.
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21
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Ching SS, Tan YK. Spectral analysis of bowel sounds in intestinal obstruction using an electronic stethoscope. World J Gastroenterol 2012; 18:4585-92. [PMID: 22969233 PMCID: PMC3435785 DOI: 10.3748/wjg.v18.i33.4585] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Revised: 02/10/2012] [Accepted: 03/10/2012] [Indexed: 02/06/2023] Open
Abstract
AIM: To determine the value of bowel sounds analysis using an electronic stethoscope to support a clinical diagnosis of intestinal obstruction.
METHODS: Subjects were patients who presented with a diagnosis of possible intestinal obstruction based on symptoms, signs, and radiological findings. A 3M™ Littmann® Model 4100 electronic stethoscope was used in this study. With the patients lying supine, six 8-second recordings of bowel sounds were taken from each patient from the lower abdomen. The recordings were analysed for sound duration, sound-to-sound interval, dominant frequency, and peak frequency. Clinical and radiological data were reviewed and the patients were classified as having either acute, subacute, or no bowel obstruction. Comparison of bowel sound characteristics was made between these subgroups of patients. In the presence of an obstruction, the site of obstruction was identified and bowel calibre was also measured to correlate with bowel sounds.
RESULTS: A total of 71 patients were studied during the period July 2009 to January 2011. Forty patients had acute bowel obstruction (27 small bowel obstruction and 13 large bowel obstruction), 11 had subacute bowel obstruction (eight in the small bowel and three in large bowel) and 20 had no bowel obstruction (diagnoses of other conditions were made). Twenty-five patients received surgical intervention (35.2%) during the same admission for acute abdominal conditions. A total of 426 recordings were made and 420 recordings were used for analysis. There was no significant difference in sound-to-sound interval, dominant frequency, and peak frequency among patients with acute bowel obstruction, subacute bowel obstruction, and no bowel obstruction. In acute large bowel obstruction, the sound duration was significantly longer (median 0.81 s vs 0.55 s, P = 0.021) and the dominant frequency was significantly higher (median 440 Hz vs 288 Hz, P = 0.003) when compared to acute small bowel obstruction. No significant difference was seen between acute large bowel obstruction and large bowel pseudo-obstruction. For patients with small bowel obstruction, the sound-to-sound interval was significantly longer in those who subsequently underwent surgery compared with those treated non-operatively (median 1.29 s vs 0.63 s, P < 0.001). There was no correlation between bowel calibre and bowel sound characteristics in both acute small bowel obstruction and acute large bowel obstruction.
CONCLUSION: Auscultation of bowel sounds is non-specific for diagnosing bowel obstruction. Differences in sound characteristics between large bowel and small bowel obstruction may help determine the likely site of obstruction.
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