Identification of the slow wave of bowel myoelectrical surface recording by empirical mode decomposition.

Yiyao Ye, J Garcia-Casado, J L Martinez-de-Juan, J L Guardiola, J L Ponce
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引用次数: 5

Abstract

Surface electroenterogram (EEnG) is a non-invasive method to study bowel myoelectrical activity. Nevertheless, surface recorded EEnG is contaminated by respiratory, motion artifacts, and other interferences. The goal of this paper is to remove the respiration artifact and ultra-low frequency components from surface EEnG by means of empirical mode decomposition (EMD). Seven recording sessions on abdominal surface of three Beagle dogs were conducted. Power percentages of interferences and of fundamental slow wave were calculated before and after the application of the method. The results show that the interference power is significantly reduced (23 +/- 16% vs. 5 +/- 4%), and fundamental slow wave power is significantly increased (59 +/- 17% vs. 76 +/- 13%). Therefore, the EMD method can be helpful to remove respiration and ultra-low frequency components from the external EEnG recordings.

用经验模态分解识别肠肌电表面记录慢波。
表面肠电图(EEnG)是一种研究肠肌电活动的无创方法。然而,表面记录的eeg受到呼吸、运动伪影和其他干扰的污染。本文的目的是通过经验模态分解(EMD)去除表面eeg中的呼吸伪影和超低频成分。对3只Beagle犬腹部进行了7次记录。计算了应用该方法前后的干扰功率百分比和基波慢波功率百分比。结果表明,干涉功率显著降低(23 +/- 16% vs. 5 +/- 4%),基波慢波功率显著提高(59 +/- 17% vs. 76 +/- 13%)。因此,EMD方法可以帮助从外部eeg记录中去除呼吸和超低频成分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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