Time series labeling algorithms based on the K-nearest neighbors' frequencies

In the current paper, time series labeling task is analyzed and some solution algorithms are presented. In these algorithms, fuzzy c-means clustering, which is one of the unsupervised learning methods, is used to obtain the labels of the time series. Then K-nearest neighborhood (KNN) rule is performed on the labels to obtain more relevant smooth intervals. As an application, the handled labeling algorithms are performed on bispectral index (BIS) data, which are time series measures of brain activity. Finally, smoothing process is found useful in the estimation of sedation stage labels. (C) 2010 Elsevier Ltd. All rights reserved.