Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering.

A new approach to visual evaluation of long-term EEG recordings is proposed. The method is based on multichannel adaptive segmentation, subsequent feature extraction, automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm), and on the distinguishing of thus identified EEG segments by colour directly in the EEG record. The black and white variant of the described automatic system is presented. The method was evaluated by applying it to simulated artificial data and to real EEG recordings; some of the illustrative results are shown. In addition, the performance of this system is evaluated and the first experience with its application to routine EEG recordings is discussed.

Use of discrete Hilbert transformation for automatic spike mapping: a methodological investigation.

On the basis of discrete Hilbert transform (DHT) realised by fast Fourier transform (FFT), a new strategy for automatic spike mapping is introduced. The further computation of the EEG time series after DHT results in the time series of the momentary power and the momentary frequency. Both are used for the solution of the main requirements of automatic spike mapping. The spike-mapping concept introduced meets the requirements of efficient automatic spike detection and also has an insensitivity with regard to EMG interference and transient signal components, a frequent cause of false positive detections. Additionally, there are advantages if the momentary power of the spike is mapped instead of the spike potential. The use of momentary power makes a combination of power spectral mapping and spike-mapping strategies possible.