Abstract
Bowel sounds (BS) are typically momentary and have low amplitude, making them difficult to detect accurately through manual auscultation. This leads to significant variability in clinical assessment. Digital acoustic sensors allow the acquisition of high-quality BS and enable automated signal analysis, offering the potential to provide clinicians with both objective and quantitative feedback on bowel activity. This study presents an automated pipeline for bowel sound segmentation and classification using a wearable acoustic SonicGuard sensor. BS signals from 83 subjects were recorded using a SonicGuard sensor. Data from 40 subjects were manually annotated by clinical experts and used to train an automatic annotation algorithm, while the remaining subjects were used for further model evaluation. An energy-based event detection algorithm was developed to detect BS events. Detected sound segments were then classified into BS patterns using a pretrained Audio Spectrogram Transformer (AST) model. Model performance was evaluated separately for healthy individuals and patients. The best configuration used two specialized models, one trained on healthy subjects and one on patients, achieving (accuracy: 0.97, AUROC: 0.98) for healthy group and (accuracy: 0.96, AUROC: 0.98) for patient group. The auto-annotation method reduced manual labeling time by approximately 70%, and expert review showed that less than 12% of automatically detected segments required correction. The proposed automated segmentation and classification system enables quantitative assessment of bowel activity, providing clinicians with an objective diagnostic tool that may improve the diagnostic of gastrointestinal function and support the annotation of large-scale datasets.
Abstract (translated)
肠鸣音（BS）通常是短暂的，振幅较低，通过手动听诊难以准确检测，这会导致临床评估中的显著变异性。数字声学传感器能够获取高质量的肠鸣音信号，并允许进行自动信号分析，为临床医生提供有关肠道活动的客观和量化反馈的可能性。这项研究提出了一种使用可穿戴声学SonicGuard传感器进行肠鸣音分割和分类的自动化流水线方法。 研究人员记录了83名受试者的BS信号，其中40名受试者的数据由临床专家手动标注并用于训练自动注释算法，其余受试者则用于进一步评估模型性能。开发了一种基于能量的事件检测算法来识别肠鸣音事件。检测到的声音片段随后使用预训练的音频光谱转换器（AST）模型进行分类。 对于健康个体和患者群体分别评估了该模型的性能。最佳配置采用两种专门针对不同人群训练的模型：一种在健康受试者上训练，另一种在患者群体上训练，在健康组中实现了0.97的准确率和0.98的AUROC值；在患者组中，则为0.96的准确率和0.98的AUROC值。 自动注释方法将手动标注时间减少了大约70%，并且专家审查发现，仅约12%的自动检测片段需要纠正。提出的自动化分割和分类系统能够对肠道活动进行量化评估，向临床医生提供客观诊断工具，有助于改善胃肠道功能的诊断，并支持大规模数据集的注释工作。
URL
https://arxiv.org/abs/2603.07215
