2026-10-29T19:58:58Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/13642026-10-28T15:28:39Zopenairedrivercom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Deep learning-based detection of bowel sound events in continuous recordingsÇelik, YusufBiomedical signal analysisBowel soundsSound event detectionAccurate identification of bowel sound events is essential for the long-term and non-invasive monitoring of gastrointestinal activity. In this study, we present a band-aware multi-band deep learning approach that explicitly accounts for the distinct acoustic characteristics of bowel sounds across different frequency ranges by jointly analyzing low- and high-frequency components. The proposed method operates on time–frequency representations extracted from continuous recordings to produce frame-level bowel sound probabilities, which are subsequently integrated into recording-level decisions through a unified decision-making strategy. Despite the pronounced class imbalance in the dataset, the proposed system achieved accuracy levels exceeding 98% at the frame level and 99% at the recording level, yielding stable and consistent outcomes through probability aggregation and temporal smoothing steps. These results indicate that the proposed approach provides a reliable and well-structured framework for bowel sound event detection and establishes a solid methodological basis for the analysis of long-duration recordings.Nature Research2026Articlehttps://rdlopen.rawdatalibrary.net/r/munzur/1364https://doi.org/10.1038/s41598-026-47018-32045-2322enScientific Reportsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/1364