Automatic EEG analysis during long-term monitoring in the ICU
Automatic EEG analysis during long-term monitoring in the ICU
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DOI：
10.1016/s0013-4694(98)00009-1
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发表时间：
1998-07-01
期刊：
影响因子：
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通讯作者：
Rosenblatt, B
中科院分区：
文献类型：
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作者：
Agarwal, R;Gotman, J;Rosenblatt, B
为了帮助审查延长EEG，我们已经开发了一种自动EEG分析方法，可以用来压缩延长EEG到两页。分段脑电自动分析（AAS-EEG）包括4个基本步骤：（1）分段;（2）特征提取;（3）分类;（4）表示。其思想是将EEG分解为固定的片段并提取可用于将片段分类为相似模式组的特征。最后一步涉及以压缩形式呈现处理后的数据。这是通过向EEGer提供来自每组EEG模式的代表性样本和完整EEG的压缩时间曲线来完成的。为了验证上述方法，通过AAS-EEG和常规EEG方法评估41个6 h EEG记录的正常性。通过压缩和常规EEG进行的总体评估之间的差异100%在一个异常水平内，73.6%的记录在二分之一水平内。我们证明了自动分割和聚类EEG的可行性和可靠性，从而允许减少6小时跟踪到几个代表性的部分和它们的时间序列。这将有助于在ICU监测期间审查长记录。(C)1998爱思唯尔科学爱尔兰有限公司保留所有权利。
To assist in the reviewing of prolonged EEGs, we have developed an automatic EEG analysis method that can be used to compress the prolonged EEG into two pages. The proposed approach of Automatic Analysis of Segmented-EEG (AAS-EEG) consists of 4 basic steps: (1) segmentation; (2) feature extraction; (3) classification; and (4) presentation. The idea is to break down the EEG into stationary segments and extract features that can be used to classify the segments into groups of like patterns. The final step involves the presentation of the processed data in a compressed form. This is done by providing the EEGer with a representative sample from each group of EEG patterns and a compressed time profile of the complete EEG. To verify the above approach, 41 6 h EEC records were assessed for normality via the AAS-EEG and conventional EEG approaches. The difference between the overall assessment via compressed and conventional EEG was within one abnormality level 100% of the time, and within one-half level for 73.6% of the records. We demonstrated the feasibility and reliability of automatically segmenting and clustering the EEG, thus allowing the reduction of a 6 h tracing to a few representative segments and their time sequence. This should facilitate review of long recordings during monitoring in the ICU. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.