Classification using distance-based segmentation-application to the analysis of EEG signals

Authors:
H. Escola;E. Poiseau;M. Jobert;P. Gaillard
Affiliations:
Dept. of Clin. Neurophysiology, F. U., Spandauerdamm 130, 1000 Berlin 19, Germany and U.A. 817, University of Compiègne, 60206 Compiègne Cedex, France;-;-;-
Venue:
Pattern Recognition Letters
Year:
1991

Citing 3
Cited 0

An algorithm for non-supervised sequential of signals

Pattern Recognition Letters
Algorithms for clustering data

Algorithms for clustering data
Nonsupervised sequential classification and recognition of patterns

IEEE Transactions on Information Theory

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Abstract

The algorithm described in this paper first analyses the trajectory of observations in the space to distinguish observations corresponding to classes from observations corresponding to transition sequences between classes. This first step is called 'segmentation' since it arranges data into segments: class segments and transition segments. Segments are then merged using a hierarchical clustering method until classes and transition sequences are interlaced. This algorithm has been applied to classify sleep EEG samples.