Title :
One-class classification of temporal EEG patterns for K-complex extraction
Author :
Zacharaki, Evangelia I. ; Pippa, Evangelia ; Koupparis, Andreas ; Kokkinos, Vasileios ; Kostopoulos, George K. ; Megalooikonomou, Vasileios
Author_Institution :
Dept. of Comput. Eng. & Inf., Univ. of Patras, Patras, Greece
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.
Keywords :
biomedical electrodes; brain; electroencephalography; feature extraction; medical signal detection; pattern clustering; signal classification; sleep; waveform analysis; K-complex extraction; KC detection; KC wave; brain waveform; cross-validation; electroencephalography; frequency content; fundamental morphological feature; graph partitioning; information processing; memory consolidation; multiple EEG electrode; one-class classification; pattern cluster; signal content; singular generalized KC; sleep protection; spectral clustering; temporal EEG pattern; temporal similarity; training set; two-step methodology; visual criteria; visual scoring; whole-night sleep activity; Electrodes; Electroencephalography; Probability; Sensitivity; Sleep; Training; Visualization;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6610870