Benchmarking machine learning for bowel sound pattern classification from tabular features to pretrained models
Journal:
arXiv
Published Date:
Feb 21, 2025
Abstract
The development of electronic stethoscopes and wearable recording sensors
opened the door to the automated analysis of bowel sound (BS) signals. This
enables a data-driven analysis of bowel sound patterns, their interrelations,
and their correlation to different pathologies. This work leverages a BS
dataset collected from 16 healthy subjects that was annotated according to four
established BS patterns. This dataset is used to evaluate the performance of
machine learning models to detect and/or classify BS patterns. The selection of
considered models covers models using tabular features, convolutional neural
networks based on spectrograms and models pre-trained on large audio datasets.
The results highlight the clear superiority of pre-trained models, particularly
in detecting classes with few samples, achieving an AUC of 0.89 in
distinguishing BS from non-BS using a HuBERT model and an AUC of 0.89 in
differentiating bowel sound patterns using a Wav2Vec 2.0 model. These results
pave the way for an improved understanding of bowel sounds in general and
future machine-learning-driven diagnostic applications for gastrointestinal
examinations
