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.
Krajca
V
V
Faculty Hospital Bulovka, Department of Neurology, Praha, CSFR.
Petránek
S
S
Patáková
I
I
Värri
A
A
D000465:Algorithms; D016000:Cluster Analysis; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis; D006801:Humans; D008954:Models, Biological; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D012984:Software