基于经验模式分解的实时单通道 EOG 移除

Q2 Engineering
Kien Nguyen Trong, Nhat Nguyen Luong, Hanh Tan, Duy Tran Trung, Huong Ha Thi Thanh, Duy Pham The, Binh Nguyen Thanh
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引用次数: 0

摘要

近年来,单通道生理记录因其便捷性在便携式健康设备和研究环境中越来越受欢迎。然而,电图(EOG)伪像的存在会大大降低记录数据的质量,影响基本信号特征的准确性。因此,去除生理信号中的伪影是信号处理管道中的关键步骤。目前的技术通常采用独立分量分析(ICA)来有效分离多通道记录中的信号源和伪像源。然而,在处理最小仪器或便携式设备中的单通道或少数通道测量时会出现限制,从而限制了 ICA 的实用性。为了应对这一挑战,本文介绍了一种创新的人工痕迹去除算法,利用增强的经验模式分解来提取本征模式函数(IMF)。随后,该算法通过在这些 IMFs 中隔离与 EOG 相关的片段,从而去除这些片段。我们将所提出的方法与现有的单通道脑电图伪像去除算法进行了比较,结果显示该方法性能优越。研究结果表明,我们的方法能有效地分离伪像成分,重建的信号具有很强的相关性,功率谱与地面真实脑电信号非常相似。这在去除伪像方面优于现有方法。此外,所提出的算法大大缩短了执行时间,实现了实时在线分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Single-Channel EOG removal based on Empirical Mode Decomposition
In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.
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CiteScore
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发文量
15
审稿时长
10 weeks
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