Received: September 11, 2008
Accepted: November 01, 2008
Ref: Hang LW, Wang JF, Yen CW, Lin CL. EEG arousal prediction via hypoxemia indicator in patients with obstructive sleep apnea syndrome. Internet Journal of Medical Update. 2009 July;4(2):24-28.
EEG AROUSAL PREDICTION VIA HYPOXEMIA INDICATOR IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA SYNDROME
Dr. Liang-Wen Hang†‡ MD, Mr. Jen-Feng WangØ, Dr. Chen-Wen YenØ PhD and Dr. Chen-Liang Lin¶ PhD
†Sleep Medicine Center, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
‡Division of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
ØDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
¶Medical Electronics and Device Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
(Corresponding Author: Dr. Chen-Liang Lin, Medical Electronics and Device Technology Center, Industrial Technology Research Institute, Rm. 815, Bldg. 53, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu, Taiwan 31040. Email: firstname.lastname@example.org)
Obstructive sleep apnea syndrome (OSAS) is a sleep breathing disorder characterized by recurrent airflow obstruction caused by a total or partial collapse of the upper airway. OSAS is a common affliction suffered by millions. The arousal index (ArI) is the best predictor of daytime somnolence for patients with OSAS, however, the polysomnography (PSG) examination in the sleep lab is expensive, time consuming and labor intensive. The objective of this study is to evaluate the ability and reliability of arousal prediction via the hypoxemia indicator in patients with OSAS. Patients with a diagnosis of OSAS by standard polysomnography were recruited from China Medical University Hospital Centre. There were 248 patients in the learning set and 255 patients in the validation set. The presence of OSAS was defined as an Apnea Hypopnea Index (AHI) >5/h. We used the hypoxemia indicator to predict ArI in patients with OSAS by linear regression and evaluated the prediction performance in different clinical characteristics subsets. The standard error of estimate of ArI prediction was 12.9 in the learning set. For predicting the severity of ArI, for ArI exceeding 15/h or 30/h, the sensitivity was 53.4% and 75.7%, respectively, with corresponding specificity of 96.6%, and 77.4%, respectively. We analyzed the hypoxemia indicator for predicting the severity of sleep fragmentation. The result demonstrated it is possible to predict ArI via the hypoxemia indicator, especially in severe patients.
KEY WORDS: Pulse oximetry; Obstructive Sleep Apnea Syndrome; Polysomnography; Arousal index