Abstract: Hypnograms are a schematic representation of sleep,
depicting its different stages through the night. This work
presents a new strategy for classifying these data, based on CCCBiclustering
for time series. By exploring local sleep patterns, one
might be able to find significant ones which are characteristic to
a given group of patients. The developed method is applied on
a dataset consisting of hypnograms for six different pathologic
populations and a control group. Although the preliminary
results are not so satisfactory, future work is discussed, showing
promising potentialities.