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

Healthcare Technology Letters

article

Suvasish Saha¹  Arnab Bhattacharjee¹  Shaikh Anowarul Fattah¹ 

[1] Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology

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;   

DOI  :  10.1049/htl.2018.5101

学科分类：肠胃与肝脏病学

来源: Wiley

【 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.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND