EEG Sleep Staging Using Vectorial Autoregressive Models

Sleep studies require the use of several channels of EEG. The analysis of vector EEG, exhibits significant advantages over scalar analysis. Novel algorithms for segmentation, classification and compression of vector EEG are described. The statistics of the suggested measures for segmentation and classification are discussed. The algorithms were evaluated on four patients, yielding mean correct sleep staging of about 85%.