Liu Siqi, Wan Xianrong, Xie Deqiang, Jiang Congqing, Ren Xianghai
Electronic Information School, Wuhan University, Wuhan, 430072.
Zhongnan Hospital of Wuhan University, Clinical Center of Intestinal and Colorectal Diseases of Hubei Province, Wuhan, 430071.
Zhongguo Yi Liao Qi Xie Za Zhi. 2022 Sep 30;46(5):473-480. doi: 10.3969/j.issn.1671-7104.2022.05.001.
Bowel sounds is an important indicator to monitor and reflect intestinal motor function, and traditional manual auscultation requires high professional knowledge and rich clinical experience of doctors. In addition, long-time auscultation is time-consuming and laborious， which may lead to misjudgment caused by subjective error. To solve the problem, firstly, the wavelet transform is used to preprocess the bowel sounds signal for noise reduction and enhancement. Secondly, three typical features of intestinal sound were extracted. According to the combination of these features, a three-stage decision was designed to carry out multi-parameter and multi-feature joint threshold detection. This algorithm realized the detection of bowel sound signal and the location of its start and end points, making it possible that the complete bowel sound signal was extracted effectively. In this study, a large number of clinical data and label of bowel sounds were collected, and a new effective evaluation method was proposed to verify the proposed method. The accuracy rate is 83.51%. Results of this study will provide systematic support and theoretical guarantee for the diagnosis of intestinal diseases and the monitoring of postoperative intestinal function recovery of patients.
肠鸣音是监测和反映肠道运动功能的重要指标，传统的人工听诊需要医生具备较高的专业知识和丰富的临床经验。此外，长时间听诊既耗时又费力，可能会因主观误差导致误诊。为了解决这个问题，首先，使用小波变换对肠鸣音信号进行预处理，以降低噪声并增强信号。其次，提取了肠鸣音的三个典型特征。根据这些特征的组合，设计了一个三阶段决策，进行多参数和多特征联合阈值检测。该算法实现了肠鸣音信号的检测及其起止点的定位，使得有效提取完整的肠鸣音信号成为可能。在本研究中，收集了大量肠鸣音的临床数据和标注，并提出了一种新的有效评估方法来验证所提出的方法。准确率为83.51%。本研究结果将为肠道疾病的诊断以及患者术后肠道功能恢复的监测提供系统支持和理论保障。
