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<p>Accurate 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.</p>

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Deep learning-based detection of bowel sound events in continuous recordings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月30日

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  • Yusuf Çelik

摘要

Accurate 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.