Purpose While bowel sound auscultation represents a key component of abdominal examination, its utility is limited because bowel sounds (BS) are intermittent, variable, and influenced by factors such as diet and digestive state. This renders it challenging to use them for a quantitative assessment of gastrointestinal health.

Methods BS signals were recorded from 84 subjects (39 patients and 45 healthy controls) using an acoustic SonicGuard sensor and categorized into four patterns. Metadata on physiological parameters were collected to examine their influence on BS characteristics and the differences between healthy and patient BS patterns.

Results Bowel sound patterns are significantly influenced by meal timing, caffeine consumption, and medication intake. Significant differences between healthy and patient groups were also observed in sound count, duration, energy, and waveform shape. These differences were mirrored in the performance of machine learning models finetuned for BS patterns classification, with performance depending on the group used for training and evaluation.

Conclusion BS patterns present a promising quantitative indicators of gas-trointestinal health when analyzed alongside relevant physiological parameters.

The authors have declared no competing interest.

This work was supported by intramural funding from Faculty VI, Carl von Ossietzky University of Oldenburg (Forschungspool, Potentialbereich mHealth) and by the Ministry for Science and Culture of Lower Saxony (MWK Niedersachsen) through Fraunhofer IDMT, Institute Part HSA, within the Connected Health project

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The research protocol underlying the data recordings was approved by the Medical Ethics Committee of the University of Oldenburg (2022-056). Before participation, subjects were informed about the purpose of the study and consented to the use of the collected data in an anonymized form.

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