PDF(Pubmed)
Abstract：
BACKGROUND: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively.
OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system.
METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors.
RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution.
CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system.
METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors.
RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution.
CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
摘要：
背景：腹部听诊（即听肠鸣音（BS））可用于分析消化。BS的自动检索对于非侵入性评估胃肠道疾病将是有益的。
目的：本研究旨在开发一种多尺度斑点模型，以检测来自可穿戴式监测系统的连续音频数据中的BS。
方法：我们设计了一种基于Efficient-U-Net（EffUNet）体系结构的斑点模型，用于分析一次10秒的音频片段，并以25毫秒的时间分辨率对BS进行斑点分析。从18名健康参与者和9名炎症性肠病（IBD）患者的不同消化阶段收集评估数据。音频数据是在白天使用嵌入数字麦克风的智能T恤记录的。数据集由独立评估者注释，具有实质一致性(Cohenκ在0.70和0.75之间)，产生136小时的标记数据。总的来说,分析了11,482个BS，BS持续时间介于18毫秒和6.3秒之间。BS在数据集中的份额(BS比率)为0.0089。我们根据噪声水平分析了性能，BS持续时间,和BS事件率。我们还报告发现时间错误。
结果：对于健康志愿者和IBD患者，对BS事件发现的留单参与者交叉验证的中位F1评分为0.73。EffUNet在不同噪声条件下检测到BS，召回率为0.73，精度为0.72。特别是,对于超过4dB的信噪比，超过83%的BS得到认可，精度为0.77或更高。对于1.5秒或更短的BS持续时间，EffUNet召回率降至0.60以下。在BS比率大于0.05时，我们模型的精度超过0.83。对于健康的参与者和IBD患者，插入和删除定时错误是最大的，在整个音频数据集上，总共有15.54分钟的插入错误和13.08分钟的删除错误。在我们的数据集上，EffUNet优于提供类似时间分辨率的现有BS斑点模型。
结论：EffUNet斑点器对背景噪声具有鲁棒性，可以检索不同持续时间的BS。EffUNet在未经修改的音频数据中优于以前的BS检测方法，包含高度稀疏的BS事件。
目的：本研究旨在开发一种多尺度斑点模型，以检测来自可穿戴式监测系统的连续音频数据中的BS。
方法：我们设计了一种基于Efficient-U-Net（EffUNet）体系结构的斑点模型，用于分析一次10秒的音频片段，并以25毫秒的时间分辨率对BS进行斑点分析。从18名健康参与者和9名炎症性肠病（IBD）患者的不同消化阶段收集评估数据。音频数据是在白天使用嵌入数字麦克风的智能T恤记录的。数据集由独立评估者注释，具有实质一致性(Cohenκ在0.70和0.75之间)，产生136小时的标记数据。总的来说,分析了11,482个BS，BS持续时间介于18毫秒和6.3秒之间。BS在数据集中的份额(BS比率)为0.0089。我们根据噪声水平分析了性能，BS持续时间,和BS事件率。我们还报告发现时间错误。
结果：对于健康志愿者和IBD患者，对BS事件发现的留单参与者交叉验证的中位F1评分为0.73。EffUNet在不同噪声条件下检测到BS，召回率为0.73，精度为0.72。特别是,对于超过4dB的信噪比，超过83%的BS得到认可，精度为0.77或更高。对于1.5秒或更短的BS持续时间，EffUNet召回率降至0.60以下。在BS比率大于0.05时，我们模型的精度超过0.83。对于健康的参与者和IBD患者，插入和删除定时错误是最大的，在整个音频数据集上，总共有15.54分钟的插入错误和13.08分钟的删除错误。在我们的数据集上，EffUNet优于提供类似时间分辨率的现有BS斑点模型。
结论：EffUNet斑点器对背景噪声具有鲁棒性，可以检索不同持续时间的BS。EffUNet在未经修改的音频数据中优于以前的BS检测方法，包含高度稀疏的BS事件。
