Praetorius H M, Bodenstein G, Creutzfeldt O D
Electroencephalogr Clin Neurophysiol. 1977 Jan;42(1):84-94. doi: 10.1016/0013-4694(77)90153-5.
The first step in a procedure for automatic EEG analysis is to compress the incoming data into a manageable format while preserving the essential diagnostic information. In our approach we mimic the visual procedure of looking through the record for segments and events of particular interest. We assume that the EEG is composed of roughly stationary segments of variable length, possibly superposed by sharp transients. By using an autoregressive model we have developed a procedure to detect the segment boundaries and locate transients, and to represent the information in the segments in terms of a set of parameters specifying their power spectra. In this way, the time structure as well as the frequency content of the signal is preserved. Examples of segmentation and transient detection are shown for several EEG signals, and the quality of the representation is demonstrated by simulating the original signal from the parameters. Possible applications to practical EEG analysis are discussed.
自动脑电图分析程序的第一步是将输入数据压缩成可管理的格式，同时保留基本的诊断信息。在我们的方法中，我们模仿了在记录中查找特定感兴趣的片段和事件的视觉程序。我们假设脑电图由长度可变的大致平稳的片段组成，可能叠加有尖锐的瞬变信号。通过使用自回归模型，我们开发了一种程序来检测片段边界并定位瞬变信号，并根据一组指定其功率谱的参数来表示片段中的信息。通过这种方式，信号的时间结构以及频率内容得以保留。展示了几个脑电图信号的分割和瞬变检测示例，并通过从参数模拟原始信号来证明表示的质量。讨论了其在实际脑电图分析中的可能应用。
