Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography

被引：0
作者：
Li, Xilin [1 ]
Al-Ani, Ahmed [1 ]
Ling, Sai Ho [1 ]
机构：
[1] Univ Technol Sydney UTS, Fac Engn & Informat Technol FEIT, Sch Biomed Engn, Sydney, NSW 2007, Australia
来源：
2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2018年
关键词：
OXYGEN-SATURATION; DIAGNOSIS; CLASSIFICATION;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Patients with sleep apnea (SA) are at increased risk of stroke and cardiovascular disease. Diagnosis of sleep apnea depends on the standard overnight polysomnography (PSG). In this study, the DREAM Apnea Database was used to evaluate the importance of the various features proposed in the literature for the analysis of sleep apnea. Various time and frequency- domain features that include wavelet and power spectral density were extracted from ECG, EMG, EEG, airflow, Sa02, abdominal and thoracic recordings. Evaluation measures of one-way analysis of variance (ANOVA) and Rank-Sum test were used to test the performance of different features. The selected feature subset indicated that frequency-domain features outperform time-domain ones. This study will help in enhancing the detection accuracy of sleep apnea for the various polysomnography signals.
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收藏
页码：1444 / 1447
页数：4
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