One-class classification of temporal EEG patterns for K-complex extraction

Abstract

The purpose of this study was to detect one of the constituent brain waveforms in electroencephalography (EEG), the K-complex (KC). The role and significance of the KC include its engagement in information processing, sleep protection, and memory consolidation [1]. The method applies a two-step methodology in which first all the candidate KC waves are extracted based on fundamental morphological features imitating visual criteria. Subsequently each candidate wave is classified as KC or outlier according to its similarity to a set of different patterns (clusters) of annotated KCs. The different clusters are constructed by applying graph partitioning on the training set based on spectral clustering and exhibit temporal similarities in both signal and frequency content. The method was applied in whole-night sleep activity recorded using multiple EEG electrodes. Cross-validation was performed against visual scoring of singular generalized KCs during all sleep cycles and showed high sensitivity in KC detection.