Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal

[摘要] Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K -nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

[学科分类] 肠胃与肝脏病学

[关键词] medical signal processing;sleep;medical disorders;electroencephalography;feature extraction;medical signal detection;nearest neighbour methods;signal classification;automatic detection;sleep apnoea events;multiband EEG signal;electroencephalography signal analysis;subject-specific classification;nonapnoea events;sleep disorder;apnoea patient;interband energy ratio features;K-nearest neighbourhood classifier