Use of adaptive Hilbert transformation for eeg segmentation and calculation of instantaneous respiration rate in neonates

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.