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

被引:18
作者
Saha, Suvasish [1 ]
Bhattacharjee, Arnab [1 ]
Fattah, Shaikh Anowarul [1 ]
机构
[1] Bangladesh Univ Engn & Technol, Dept Elect & Elect Engn, Dhaka, Bangladesh
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;
D O I
10.1049/htl.2018.5101
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
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.
引用
收藏
页码:82 / 86
页数:5
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