Detection of Sleep Stages in Temporal Profiles in Neonatal EEG—k-NN versus k-Means Approach: A Feasibility Study

The aim of this feasibility study is to experimentally verify the detection of changes of sleep stages in neonates with our proposed semi-automated approach using k-NN classification in comparison with a fully automated approach using simple k-means cluster analysis for classification (instead of k-NN). Our semi-automatic approach uses the k-NN classifier trained on etalons (prototypes) created by semi-automated etalons extraction (k-means for etalons suggestion and expert-in-the-loop for verification). Both methods are compared to labelling of sleep stages made by an experienced physician Dr. K. Paul. An EEG recording of full-term neonate is chosen from group of EEG recordings: full-term and preterm neonates recorded from eight electrodes positioned in standard conditions. The EEG recording is digitally preprocessed by mean-removal filter (no other filters are applied) and segmented adaptively. For each segment, 24 features are extracted and send to two classification processes: k-means and k-NN. Classified segments are plotted in temporal profiles (class membership in time) that are analysed for sleep stages using our method of creating a single detection curve from all channels and a threshold is applied on this detection curve to detect sleep stages.