Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care. It helps early detection of neonatal bowel dysfunctions and allows timely intervention. This paper presents a neonatal bowel sound detection method to assist the auscultation. Specifically, a Convolutional Neural Network (CNN) is proposed to classify peristalsis and non-peristalsis sounds. The classification is then optimized using a Laplace Hidden Semi-Markov Model (HSMM). The proposed method is validated on abdominal sounds from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results show that the method can effectively detect bowel sounds with accuracy and area under curve (AUC) score being 89.81% and 83.96% respectively, outperforming 13 baseline methods. Furthermore, the proposed Laplace HSMM refinement strategy is proven capable to enhance other bowel sound detection models. The outcomes of this work have the potential to facilitate future telehealth applications for neonatal care.

中文翻译：

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使用卷积神经网络和拉普拉斯隐半马尔可夫模型检测新生儿肠鸣音


腹部听诊是一种方便、安全且廉价的评估肠道状况的方法，这对于新生儿护理至关重要。它有助于及早发现新生儿肠道功能障碍并及时干预。本文提出一种辅助听诊的新生儿肠鸣音检测方法。具体来说，提出了卷积神经网络（CNN）来对蠕动和非蠕动声音进行分类。然后使用拉普拉斯隐半马尔可夫模型 (HSMM) 优化分类。所提出的方法在我们三级新生儿重症监护病房 (NICU) 收治的 49 名新生儿的腹部声音上得到了验证。结果表明，该方法能够有效检测肠鸣音，准确率和曲线下面积（AUC）评分分别为89.81%和83.96%，优于13种基线方法。此外，所提出的拉普拉斯 HSMM 细化策略被证明能够增强其他肠音检测模型。这项工作的成果有可能促进未来新生儿护理的远程医疗应用。