Use of adaptive Hilbert transformation for eeg segmentation and calculation of instantaneous respiration rate in neonates
Use of adaptive Hilbert transformation for eeg segmentation and calculation of instantaneous respiration rate in neonates
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DOI：
10.1007/bf02025311
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发表时间：
1996-01-01
期刊：
影响因子：
--
通讯作者：
Eisel, M
中科院分区：
文献类型：
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作者：
Arnold, M;Doering, A;Eisel, M
宽带和窄带希尔伯特变换滤波器 (HTF) 被用作分析新生儿脑电图 (EEG) 和呼吸运动的预处理单元。对于这些应用，开发了基于解析滤波器设计的使窄带通滤波器的谐振频率适应实际信号特性的新算法。为了分割不连续脑电图，将谐振频率的位置嵌入到神经网络（NN）的学习算法中。在这种自动脑电图模式识别中，考虑了尖峰活动的检测。引入的尖峰检测方案使用宽带 HTF 作为基本单元。此外，应用共振频率连续控制算法来实现瞬时呼吸率计算处理单元的自适应，在此框架下，获得了一种对低信噪比（SNR）不太敏感的自适应频率估计的新在线方法。与为分析实验和临床数据而建立的处理方法进行了比较，对引入的新方法进行了测试。
Broad, as well as narrow band Hilbert transform filters (HTFs) were used as preprocessing units in the analysis of electroencephalogram (EEG) and respiratory movements in neonates. For these applications, new algorithms for the adaptation of the resonance frequency of a narrow-band-pass filter to the actual signal properties on the basis of an analytic filter design were developed.For the segmentation of the discontinuous EEG, the location of the resonance frequency was imbedded into the learning algorithm of a neural network (NN). In such automatic EEG pattern recognition, the detection of spike activity was taken into consideration. The spike detection scheme introduced uses broad-band HTFs as basis units. Additionally, the algorithm for the continuous control of the resonance frequency was applied to achieve the adaptation of the processing unit that performed the calculation of the instantaneous respiration rate, in this framework, a new on-line method for adaptive frequency estimation that is less sensitive to low signal-to-noise ratios (SNRs) was obtained.The new approaches introduced were tested in comparison with processing methods that have been established for the analysis of experimental and clinical data.