基于经验模态分解的原始颅内压信号伪迹去除方法。
Empirical Mode Decomposition-Based Method for Artefact Removal in Raw Intracranial Pressure Signals.
作者信息
Martinez-Tejada Isabel, Wilhjelm Jens E, Juhler Marianne, Andresen Morten
机构信息
Clinic of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Denmark.
Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
出版信息
Acta Neurochir Suppl. 2021;131:201-205. doi: 10.1007/978-3-030-59436-7_39.
Intracranial pressure (ICP) signals are often contaminated by artefacts and segments of missing values. Some of these artefacts can be observed as very high and short spikes with a physiologically impossible high slope. The presence of these spikes reduces the accuracy of pattern recognition techniques. Thus, we propose a modified empirical mode decomposition (EMD) method for spike removal in raw ICP signals. The EMD breaks down the signal into 16 intrinsic mode functions (IMFs), combines the first 4 to localize spikes using adaptive thresholding, and then either removes or imputes the identified ICP spikes.
颅内压（ICP）信号常常受到伪迹和缺失值片段的干扰。其中一些伪迹表现为非常高且短暂的尖峰，其斜率在生理上是不可能达到的。这些尖峰的存在降低了模式识别技术的准确性。因此，我们提出一种改进的经验模态分解（EMD）方法，用于去除原始ICP信号中的尖峰。EMD将信号分解为16个固有模态函数（IMF），合并前4个IMF以使用自适应阈值定位尖峰，然后去除或插补识别出的ICP尖峰。
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