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基于多元经验模态分解的数据驱动肠鸣音滤波方法。

Data driven filtering of bowel sounds using multivariate empirical mode decomposition.

机构信息

Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Department of Endocrinology, St. Olavs University Hospital, Trondheim, Norway.

出版信息

Biomed Eng Online. 2019 Mar 20;18(1):28. doi: 10.1186/s12938-019-0646-1.

DOI:10.1186/s12938-019-0646-1
PMID:30894187
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6425713/
Abstract

BACKGROUND

The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of rather sudden bursts. Realistic recordings of abdominal sounds are contaminated with noise and artifacts from which the bowel sounds must be differentiated.

METHODS

The proposed intrinsic mode function-fractal dimension (IMF-FD) filtering utilizes the property of the multivariate empirical mode decomposition (MEMD) to behave as a series of band pass filters. The MEMD decomposes the abdominal signal into its different frequency components. The resulting intrinsic mode functions (IMFs) are modulated in amplitude and frequency where transient sonic events occur. Based on the complexity of the IMFs, measured by their fractal dimension (FD) in sliding windows, the information-carrying IMFs are selected. The filtered signal is formed as the superposition of all selected IMFs. The IMF-FD filter not only enhances the non-linear components of the original signal but also segments them from the rest. Another important aspect of this work is that typical artifacts that occur in the same frequency range as bowel sounds can be subsequently eliminated by heuristic rules.

CONCLUSIONS

The method is tested on a realistic, contaminated data set with promising performance: close to 100% of the manually labeled bowel sounds are identified.

摘要

背景

腹部声音的分析有助于诊断胃肠疾病。源于胃和肠道的声音，即所谓的肠鸣音，以各种形式出现。它们被描述为松散的连续或突发的簇状。腹部声音的真实记录受到噪声和伪影的污染，必须将肠鸣音与这些噪声和伪影区分开来。

方法

所提出的固有模态函数-分形维数（IMF-FD）滤波利用多元经验模态分解（MEMD）的特性，作为一系列带通滤波器。MEMD 将腹部信号分解为不同的频率分量。由此产生的固有模态函数（IMF）在发生瞬态声事件时会在幅度和频率上进行调制。基于 IMF 的复杂度，通过在滑动窗口中测量其分形维数（FD）来选择携带信息的 IMF。滤波后的信号由所有选定的 IMF 叠加形成。IMF-FD 滤波器不仅增强了原始信号的非线性成分，而且还将它们与其他成分区分开来。这项工作的另一个重要方面是，通过启发式规则，可以随后消除与肠鸣音处于相同频率范围内的典型伪影。

结论

该方法在一个具有实际污染数据的数据集上进行了测试，具有很有前景的性能：接近 100%的手动标记的肠鸣音被识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/446a11628ce2/12938_2019_646_Fig14_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/73e47e688b7f/12938_2019_646_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/f061fe6d520d/12938_2019_646_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/ec8a3e69da69/12938_2019_646_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/a7d26e9e83f4/12938_2019_646_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/518d0a399919/12938_2019_646_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/bab8729330c2/12938_2019_646_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b07/6425713/25547546e56d/12938_2019_646_Fig11_HTML.jpg
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