Feature extraction and classification of EEG sleep recordings in newborns

Visual evaluation of long-term EEG recordings is very difficult, time consuming and subjective process. This paper aims to present the research and development of a comprehensive scheme for computer-assisted recognition of behavioral states of sleep in newborns. In clinical practice, the ratio of behavioral states (wakefulness, quiet and active sleep) is used as an important indicator of the brain maturation. Analysis was performed offline, on real clinical data, with the assumption that each EEG channel in recording was independent from others and equally important for analysis and classification. The proposed solution comprises several consecutive steps of signal preprocessing and processing, with focus on segmentation, feature extraction and selection, and classification. Performed classification was based on linear support vector machines and performance was evaluated through cross validation. Obtained results can be used as a reference for developing or enhancing neonatal sleep EEG/PSG classification algorithms.