Wang Guojing, Wang Weidong, Liu Hongyun
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China.
Medical Innovation Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Apr 25;41(2):288-294. doi: 10.7507/1001-5515.202307055.
Monitoring of bowel sounds is an important method to assess bowel motility during sleep, but it is seriously affected by snoring noise. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was applied to remove snoring noise from bowel sounds during sleep. Specifically, the noisy bowel sounds were first band-pass filtered, then decomposed by the CEEMDAN method, and finally the appropriate components were selected to reconstruct the pure bowel sounds. The results of semi-simulated and real data showed that the CEEMDAN method was better than empirical mode decomposition and wavelet denoising method. The CEEMDAN method is used to remove snoring noise from bowel sounds during sleep, which lays an important foundation for using bowel sounds to assess the intestinal motility during sleep.
监测肠鸣音是评估睡眠期间肠道蠕动的重要方法,但它受到打鼾噪音的严重影响。本文应用具有自适应噪声的完全集成经验模态分解(CEEMDAN)方法去除睡眠期间肠鸣音中的打鼾噪音。具体来说,首先对有噪声的肠鸣音进行带通滤波,然后用CEEMDAN方法进行分解,最后选择合适的分量来重构纯净的肠鸣音。半模拟数据和实际数据的结果表明,CEEMDAN方法优于经验模态分解和小波去噪方法。CEEMDAN方法用于去除睡眠期间肠鸣音中的打鼾噪音,为利用肠鸣音评估睡眠期间的肠道蠕动奠定了重要基础。