{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:51:48Z","timestamp":1781844708301,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["GrantNo.2023YFS0327, No. 2024YFFK0044 and No. 2024YFFK0089"],"award-info":[{"award-number":["GrantNo.2023YFS0327, No. 2024YFFK0044 and No. 2024YFFK0089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid aging of the global population, constipation has become a major gastrointestinal concern among elderly individuals. Bowel sounds provide a non-invasive acoustic signal for assessing gastrointestinal function, but their automatic analysis remains challenging due to sparsity and non-stationarity. This study proposes a two-stage bowel sound analysis framework based on continuous abdominal recordings. First, a Convolutional Neural Network-MAMBA (CNN-MAMBA) model was used for salient bowel sound detection. Second, a patient-level constipation classification model was developed using multi-view spectral representations and a Convolutional Neural Network-Conformer-Multiple Instance Learning (CNN-Conformer-MIL) architecture. On a held-out test set, the detection model achieved an accuracy of 0.87, an F1-score of 0.78, and a ROC-AUC of 0.93. For patient-level classification under binary Bristol Stool Form Scale (BSFS) grouping, five-fold cross-validation yielded a mean accuracy of 0.665 and an F1-score of 0.755. All BSFS labels were annotated by clinical physicians and temporally aligned with bowel sound recording. Given the modest improvement and cross-validation variability, the patient-level results should be interpreted as preliminary feasibility evidence. These findings suggest that bowel sound analysis may serve as an auxiliary screening or longitudinal monitoring tool rather than a stand-alone diagnostic system.<\/jats:p>","DOI":"10.3390\/s26123768","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:59:47Z","timestamp":1781503187000},"page":"3768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A CNN-MAMBA-Based Framework for Salient Bowel Sound Detection and Gastrointestinal Health Assessment"],"prefix":"10.3390","volume":"26","author":[{"given":"Zixuan","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7168-2737","authenticated-orcid":false,"given":"Ling","family":"He","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-1122","authenticated-orcid":false,"given":"Junyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Anorectal Surgery, The Sichuan Second Hospital of Traditional Chinese Medicine, Chengdu 610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Anorectal Surgery, The Sichuan Second Hospital of Traditional Chinese Medicine, Chengdu 610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2026, April 21). Ageing and Health, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s00508-023-02156-w","article-title":"Global prevalence of con-stipation in older adults: A systematic review and meta-analysis","volume":"135","author":"Salari","year":"2023","journal-title":"Wien. Klin. Wochenschr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.4082\/kjfm.18.0182","article-title":"Chronic Constipation in the Elderly Patient: Updates in Evaluation and Management","volume":"41","author":"Mari","year":"2020","journal-title":"Korean J. Fam. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1038\/nrgastro.2016.53","article-title":"Diagnosis and management of chronic constipation in adults","volume":"13","author":"Rao","year":"2016","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1053\/j.gastro.2012.10.028","article-title":"American Gastroenterological Association Technical Review on Constipation","volume":"144","author":"Bharucha","year":"2013","journal-title":"Gastroenterology"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nowak, J.K., Nowak, R., Radzikowski, K., Grulkowski, I., and Walkowiak, J. (2021). Automated bowel sound analysis: An overview. Sensors, 21.","DOI":"10.3390\/s21165294"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kutsumi, Y., Kanegawa, N., Zeida, M., Matsubara, H., and Murayama, N. (2023). Automated bowel sound and motility analysis with CNN using a smartphone. Sensors, 23.","DOI":"10.3390\/s23010407"},{"key":"ref_8","first-page":"100763","article-title":"Automated bowel sounds analysis in clinical practice: A review of technologies","volume":"29","author":"Lui","year":"2026","journal-title":"Biosens. Bioelectron. X"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, Z., Zou, Y., Zhao, J., and Ke, Y. (2022). A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features. Sensors, 22.","DOI":"10.3390\/s22187084"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TASLP.2022.3178225","article-title":"Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model","volume":"30","author":"Sitaula","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e51118","DOI":"10.2196\/51118","article-title":"Multiscale bowel sound event spotting in highly imbalanced wearable monitoring data: Algorithm development and validation study","volume":"3","author":"Baronetto","year":"2024","journal-title":"JMIR AI"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107446","DOI":"10.1016\/j.engappai.2023.107446","article-title":"A lightweight deep learning based bowel sounds segmentation algorithm for gastrointestinal (GI) monitoring","volume":"127","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, Y., Zhang, M., Xie, Z., and Liu, Q. (2025). Enhancing bowel sound recognition with self-attention and self-supervised pre-training. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0311503"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kalahasty, R., Yerrapragada, G., Lee, J., Gopalakrishnan, K., Kaur, A., Muddaloor, P., Sood, D., Parikh, C., Gohri, J., and Panjwani, G.A. (2025). A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects. Sensors, 25.","DOI":"10.3390\/s25154735"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mansour, Z., Uslar, V.N., Weyhe, D., Hollosi, D., and Strodthoff, N. (2026). Benchmarking machine learning for bowel sound pattern classification-From tabular features to pretrained models. PLoS ONE, 21.","DOI":"10.1371\/journal.pone.0338911"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Baronetto, A., Fischer, S., Neurath, M.F., and Amft, O. (2025). Automated inflammatory bowel disease detection using wearable bowel sound event spotting. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1514757"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Basak, S. (2026). AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices. Curr. Issues Mol. Biol., 48.","DOI":"10.3390\/cimb48050532"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"920","DOI":"10.3109\/00365529709011203","article-title":"Stool form scale as a useful guide to intestinal transit time","volume":"32","author":"Lewis","year":"1997","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1053\/j.gastro.2016.03.035","article-title":"Rome IV-Functional GI Disorders: Disorders of Gut-Brain Interaction","volume":"150","author":"Drossman","year":"2016","journal-title":"Gastroenterology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.jpeds.2012.11.082","article-title":"Stool consistency, but not frequency, correlates with total gastrointestinal transit time in children","volume":"162","author":"Russo","year":"2013","journal-title":"J. Pediatr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1136\/gutjnl-2015-309618","article-title":"Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates","volume":"65","author":"Vandeputte","year":"2016","journal-title":"Gut"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","unstructured":"Gu, A., and Dao, T. (2024). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Getreuer, P., Hughes, T., Lyon, R.F., and Saurous, R.A. (2016). Trainable Frontend for Robust and Far-Field Keyword Spotting. arXiv.","DOI":"10.1109\/ICASSP.2017.7953242"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., and Wu, Y. (2020). Conformer: Convolution-augmented transformer for speech recognition. arXiv.","DOI":"10.21437\/Interspeech.2020-3015"},{"key":"ref_27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_28","unstructured":"Ilse, M., Tomczak, J., and Welling, M. (2018, January 10\u201315). Attention-based deep multiple instance learning. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_30","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_31","unstructured":"Sun, Y., Dong, L., Huang, S., Ma, S., Xia, Y., Xue, J., Wang, J., and Wei, F. (2023). Retentive network: A successor to transformer for large language models. arXiv."},{"key":"ref_32","unstructured":"Poli, M., Massaroli, S., Nguyen, E., Fu, D.Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and R\u00e9, C. (2023). Hyena hierarchy: Towards larger convolutional language models. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3768\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:10:55Z","timestamp":1781842255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3768"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123768"],"URL":"https:\/\/doi.org\/10.3390\/s26123768","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]}}}