Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study.

Background: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion.

Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams.

We analyse pretrained and non-pretrained deep neural models to detect 10-seconds Bowel Sounds (BS) audio segments in continuous audio data streams. The models include MobileNet, EfficientNet, and Distilled Transformer architectures. Models were initially trained on AudioSet and then transferred and evaluated on 84 hours of labelled audio data of eighteen healthy participants.