Title :
A hierarchical classification system for sleep stage scoring via forehead EEG signals
Author :
Chih-Sheng Huang ; Chun-Ling Lin ; Li-Wei Ko ; Sheng-Yi Liu ; Tung-Ping Sua ; Chin-Teng Lin
Author_Institution :
Brain Res. Center, Univ. Syst. of Taiwan, Taiwan
Abstract :
The study adopts the structure of hierarchical classification to develop an automatic sleep stage classification system using forehead (Fpl and Fp2) EEG signals. The hierarchical classification consists of a preliminary wake detection rule, a novel feature extraction method based on American Academy of Sleep Medicine (AASM) scoring manual, feature selection methods and SVM. After estimating the preliminary sleep stages, two adaptive adjustment schemes are applied to adjust the preliminary sleep stages and provide the final estimation of sleep stages. Clinical testing reveals that the proposed automatic sleep stage classification system is about 77% accuracy and 67% kappa for individual 10 normal subjects. This system could provide the possibility of long term sleep monitoring at home and provide a preliminary result of sleep stages so that doctor could decide if a patient needs to have a detailed diagnosis using Polysomnography (PSG) system in a sleep laboratory of hospital.
Keywords :
electroencephalography; hospitals; medical signal processing; signal classification; sleep; AASM; American academy of sleep medicine scoring manual; PSG; Polysomnography system; SVM; automatic sleep stage classification system; clinical testing; feature selection methods; forehead EEG signals; hierarchical classification system; hospital; long term sleep monitoring; sleep laboratory; sleep stage scoring; Accuracy; Electroencephalography; Feature extraction; Forehead; Manuals; Sleep; Support vector machines; hierarchical classification; polysomnography;
Conference_Titel :
Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2013 IEEE Symposium on
Conference_Location :
Singapore
DOI :
10.1109/CCMB.2013.6609157