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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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Baronetto A, Graf L, Fischer S, Neurath MF, Amft O. Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study. JMIR AI 2024; 3:e51118. [PMID: 38985504 PMCID: PMC11269970 DOI: 10.2196/51118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/29/2024] [Accepted: 04/24/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. OBJECTIVE This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. METHODS We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. RESULTS Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. CONCLUSIONS The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
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Affiliation(s)
- Annalisa Baronetto
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
| | - Luisa Graf
- Chair of Digital Health, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sarah Fischer
- Medical Clinic 1, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
| | - Markus F Neurath
- Medical Clinic 1, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
| | - Oliver Amft
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
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Haraguchi T, Emoto T. Stimulus-Response Plots as a Novel Bowel-Sound-Based Method for Evaluating Motor Response to Drinking in Healthy People. SENSORS (BASEL, SWITZERLAND) 2024; 24:3054. [PMID: 38793909 PMCID: PMC11125318 DOI: 10.3390/s24103054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
Constipation is a common gastrointestinal disorder that impairs quality of life. Evaluating bowel motility via traditional methods, such as MRI and radiography, is expensive and inconvenient. Bowel sound (BS) analysis has been proposed as an alternative, with BS-time-domain acoustic features (BSTDAFs) being effective for evaluating bowel motility via several food and drink consumption tests. However, the effect of BSTDAFs before drink consumption on those after drink consumption is yet to be investigated. This study used BS-based stimulus-response plots (BSSRPs) to investigate this effect on 20 participants who underwent drinking tests. A strong negative correlation was observed between the number of BSs per minute before carbonated water consumption and the ratio of that before and after carbonated water consumption. However, a similar trend was not observed when the participants drank cold water. These findings suggest that when carbonated water is drunk, bowel motility before ingestion affects motor response to ingestion. This study provides a non-invasive BS-based approach for evaluating motor response to food and drink, offering a new research window for investigators in this field.
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Affiliation(s)
- Takeyuki Haraguchi
- Science and Technology, Graduate School of Sciences and Technology for Innovation, Tokushima University, Tokushima 770-8506, Japan;
| | - Takahiro Emoto
- Division of Science and Technology, Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, Japan
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王 国, 王 卫, 刘 洪. [Snoring noise removal method for bowel sound signal during sleep]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:288-294. [PMID: 38686409 PMCID: PMC11058488 DOI: 10.7507/1001-5515.202307055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 03/12/2024] [Indexed: 05/02/2024]
Abstract
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.
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Affiliation(s)
- 国静 王
- 北京航空航天大学 生物与医学工程学院(北京 100191)School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China
- 中国人民解放军总医院 医学创新研究部(北京 100853)Medical Innovation Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China
- 中国人民解放军总医院工业和信息化部生物医学工程与转化医学重点实验室(北京 100853)Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing 100853, P. R. China
| | - 卫东 王
- 北京航空航天大学 生物与医学工程学院(北京 100191)School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China
- 中国人民解放军总医院 医学创新研究部(北京 100853)Medical Innovation Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China
| | - 洪运 刘
- 北京航空航天大学 生物与医学工程学院(北京 100191)School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China
- 中国人民解放军总医院 医学创新研究部(北京 100853)Medical Innovation Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. 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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Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Bărboi O, Iov DE, Nichita L, Ciortescu I, Cijevschi Prelipcean C, Ștefănescu G, Mihai C, Drug VL. The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management. Diagnostics (Basel) 2023; 13:3336. [PMID: 37958232 PMCID: PMC10648815 DOI: 10.3390/diagnostics13213336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual's microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI.
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Affiliation(s)
- Radu Alexandru Vulpoi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy-Iasi Branch, 700481 Iasi, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Oana Bărboi
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Diana-Elena Iov
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Loredana Nichita
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Irina Ciortescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cristina Cijevschi Prelipcean
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Gabriela Ștefănescu
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Cătălina Mihai
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
| | - Vasile Liviu Drug
- Faculty of Medicine, Department of Internal Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700111 Iasi, Romania; (R.A.V.); (A.O.); (D.-E.I.); (L.N.); (I.C.); (C.C.P.); (G.Ș.); (C.M.); (V.L.D.)
- Emergency Clinical Hospital “Saint Spiridon”, Institute of Gastroenterology and Hepatology, 700111 Iasi, Romania
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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: 4.0] [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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8
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Mallegni N, Molinari G, Ricci C, Lazzeri A, La Rosa D, Crivello A, Milazzo M. Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare. BIOSENSORS 2022; 12:835. [PMID: 36290973 PMCID: PMC9599683 DOI: 10.3390/bios12100835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.
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Affiliation(s)
- Norma Mallegni
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Giovanna Molinari
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Claudio Ricci
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Andrea Lazzeri
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Davide La Rosa
- ISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy
| | - Antonino Crivello
- ISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy
| | - Mario Milazzo
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
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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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Bondareva E, Constantinides M, Eggleston MS, Jablonski I, Mascolo C, Radivojevic Z, Scepanovic S. Stress Inference from Abdominal Sounds using Machine Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1985-1988. [PMID: 36083920 DOI: 10.1109/embc48229.2022.9871165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Stress is often considered the 21st century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has significant side effects on physical and mental health. In this work we propose a methodology for detecting stress using abdominal sounds. For this study, eight participants were either exposed to a stressful (Stroop test) or a relaxing (guided meditation) stimulus for ten days. In total, we collected 104 hours of abdominal sounds using a custom wearable device in a belt form-factor. We explored the effect of various features on the binary stress classification accuracy using traditional machine learning methods. Namely, we observed the impact of using acoustic features on their own, as well as in combination with features representing current mood state, and hand-crafted domain-specific features. After feature extraction and reduction, by utilising a multilayer perceptron classifier model we achieved 77% accuracy in detecting abdominal sounds under stress exposure. Clinical relevance- This feasibility study confirms the link between the gastrointestinal system and stress and uncovers a novel approach for stress inference via abdominal sounds using machine learning.
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Cook J, Umar M, Khalili F, Taebi A. Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions. Bioengineering (Basel) 2022; 9:bioengineering9040149. [PMID: 35447708 PMCID: PMC9032059 DOI: 10.3390/bioengineering9040149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/27/2022] [Accepted: 03/30/2022] [Indexed: 11/16/2022] Open
Abstract
In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.
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Affiliation(s)
- Jadyn Cook
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA;
| | - Muneebah Umar
- Department of Biological Sciences, Mississippi State University, 295 Lee Blvd, Starkville, MS 39762, USA;
| | - Fardin Khalili
- Department of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL 32114, USA;
| | - Amirtahà Taebi
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA;
- Correspondence: ; Tel.: +1-(662)-325-5987
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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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Wang N, Testa A, Marshall BJ. Development of a bowel sound detector adapted to demonstrate the effect of food intake. Biomed Eng Online 2022; 21:1. [PMID: 34983542 PMCID: PMC8729116 DOI: 10.1186/s12938-021-00969-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022] Open
Abstract
Objective Bowel sounds (BS) carry useful information about gastrointestinal condition and feeding status. Interest in computerized bowel sound-based analysis has grown recently and techniques have evolved rapidly. An important first step for these analyses is to extract BS segments, whilst neglecting silent periods. The purpose of this study was to develop a convolutional neural network-based BS detector able to detect all types of BS with accurate time stamps, and to investigate the effect of food consumption on some acoustic features of BS with the proposed detector. Results Audio recordings from 40 volunteers were collected and a BS dataset consisting of 6700 manually labelled segments was generated for training and testing the proposed BS detector. The detector attained 91.06% and 90.78% accuracy for the validation dataset and across-subject test dataset, respectively, with a well-balanced sensitivity and specificity. The detection rates evaluated on different BS types were also satisfactory. Four acoustic features were evaluated to investigate the food effect. The total duration and spectral bandwidth of BS showed significant differences before and after food consumption, while no significant difference was observed in mean-crossing rate values. Conclusion We demonstrated that the proposed BS detector is effective in detecting all types of BS, and providing an accurate time stamp for each BS. The characteristics of BS types and the effect on detection accuracy is discussed. The proposed detector could have clinical application for post-operative ileus prognosis, and monitoring of food intake.
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Affiliation(s)
- Ning Wang
- The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, 6009, Australia.
| | - Alison Testa
- Noisy Guts Pty Ltd, Level 2, L-block, QEII Medical Site, Nedlands, WA, 6009, Australia
| | - Barry J Marshall
- The Marshall Centre for Infectious Diseases Research and Training, University of Western Australia, Perth, 6009, Australia
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Kordi M, Dehghan MJ, Shayesteh AA, Azizi A. The impact of artificial intelligence algorithms on management of patients with irritable bowel syndrome: A systematic review. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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15
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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: 2.7] [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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Yao Y, Luo R, Xiong S, Zhang C, Zhang Y. Protective effects of curcumin against rat intestinal inflammation‑related motility disorders. Mol Med Rep 2021; 23:391. [PMID: 33760185 PMCID: PMC8008224 DOI: 10.3892/mmr.2021.12030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 12/15/2020] [Indexed: 02/07/2023] Open
Abstract
Intestinal inflammation frequently occurs alongside dysmotility, which is characterized by altered myosin light chain phosphorylation levels. Curcumin, an active component from the ginger family, is reported to confer anti‑inflammatory effects. However, the effects of curcumin on both diarrhea and constipation associated inflammation remains to be elucidated. The present study was designed to investigate the effects of curcumin on diarrhea and constipation and to determine the related mechanisms. Sprague‑Dawley rats were used to establish diarrhea and constipation models via intracolonic acetic acid (4%) instillation or cold water gavage for 2 weeks, respectively. Blood samples were collected to measure the serum levels of the cytokines TNF‑α and IL‑1β using ELISA kits. Western blotting was performed to measure NF‑κB, RhoA, Rho‑related kinase 2, phosphorylated MLC20, phosphorylated myosin phosphorylated target subunit 1, 130k Da‑MLC kinase (MLCK), c‑kit tyrosine kinase protein expression, and reverse transcription‑quantitative PCR was conducted to measure MLCK expression levels. The results indicated that curcumin reversed the elevations in the pro‑inflammatory cytokines IL‑1β and TNF‑α by inhibiting the NF‑κB pathway in rats with diarrhea and constipation. The results also indicated that myosin light chain (MLC) phosphorylation in intestinal smooth muscle was positively and negatively associated with the motility of inflammation‑related diarrhea and constipation in rats, respectively. Curcumin significantly reversed the increased MLC phosphorylation in the jejunum of the rats with diarrhea, significantly enhanced the reductions in inflammatory mediators, including TNF‑α and IL‑1β, of rats with constipation and significantly ameliorated the related hyper‑motility and hypo‑motility in rats with both diarrhea and constipation. In conclusion, the potential roles of the MLC kinase, c‑kit tyrosine and Rho A/Rho‑associated kinase 2 pathways, which are involved in curcumin‑induced amelioration of inflammation‑related diarrhea and constipation, were explored in the present study. Results from the present study suggested that curcumin has potential therapeutic value for treating intestinal inflammation and inflammation‑related motility disorders.
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Affiliation(s)
- Yang Yao
- Department of Basic Medicine, Chongqing Three Gorges Medical College, Chongqing 404120, P.R. China
| | - Ranyuan Luo
- Department of Basic Medicine, Chongqing Three Gorges Medical College, Chongqing 404120, P.R. China
| | - Shu Xiong
- Department of Basic Medicine, Chongqing Three Gorges Medical College, Chongqing 404120, P.R. China
| | - Chang Zhang
- Department of Basic Medicine, Chongqing Three Gorges Medical College, Chongqing 404120, P.R. China
| | - Yukun Zhang
- Department of Basic Medicine, Chongqing Three Gorges Medical College, Chongqing 404120, P.R. China
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Zhao K, Jiang H, Wang Z, Chen P, Zhu B, Duan X. Long-Term Bowel Sound Monitoring and Segmentation by Wearable Devices and Convolutional Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:985-996. [PMID: 32833642 DOI: 10.1109/tbcas.2020.3018711] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The system features a wearable BS sensor that can record BSs for days long and transmit them wirelessly in real-time. With the system, a total of 20 subjects' BS data under the hospital environment were collected. Each subject is recorded for 24 hours. Through manual screening and annotation, from every subject's BS data, 400 segments were extracted, in which half are BS event-contained segments. Thus, a BS dataset that contains 20 × 400 sound segments is formed. Afterwards, CNNs are introduced for BS segment recognition. Specifically, this study proposes a novel CNN design method that makes it possible to transfer the popular CNN modules in image recognition into the BS segmentation domain. Experimental results show that in holdout evaluation with corrected labels, the designed CNN model achieves a moderate accuracy of 91.8% and the highest sensitivity of 97.0% compared with the similar works. In cross validation with noisy labels, the designed CNN delivers the best generability. By using a CNN visualizing technique-class activation maps, it is found that the designed CNN has learned the effective features of BS events. Finally, the proposed CNN design method is scalable to different sizes of datasets.
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