A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals. (1st September 2017)

• Record Type: Journal Article
• Authors: Huang, Wu; Guo, Bing; Shen, Yan; Tang, Xiangdong
• Is Part Of: Computers in biology and medicine. Volume 88(2017)
• Page Start: 32
• Page End: 40
• Publication Date: 2017-09-01
• Subjects: Apnea -- AHI -- Baseline amplitude -- Hypopnea -- OSAS -- Respiratory event -- SAHS; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.06.015
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Sleep apnea hypopnea syndrome (SAHS) affects people's quality of life. The apnea hypopnea index (AHI) is the key indicator for diagnosing SAHS. The determination of the AHI is based on accurate detection of apnea and hypopnea events. This paper provides a novel method to detect apnea and hypopnea events based on the respiratory nasal airflow signal and the oximetry signal. The method uses sliding window and short time slice methods to eliminate systematic and sporadic noise of the airflow signal for improving the detection precision. Using this algorithm, the sleep data of 30 subjects from the Huaxi Sleep Center of Sichuan University (HSCSU) and the Teaching Hospital of Chengdu University of Traditional Chinese Medicine (THCUTCM) were auto-analyzed for detecting the apnea and hypopnea events. The total predicted apnea and hypopnea events were 8470. By manual investigation, the sensitivity and positive predictive value (PPV) of detecting apnea and hypopnea events were 97.6% and 95.7%, respectively. The sleep data of 28 subjects form HSCSU were auto-diagnosed SAHS according to the AHI. The sensitivity and PPV were 92.3% and 92.3%, respectively. This is an effective and precise method to diagnose SAHS. It can fit the home care SAHS screener. Highlights: The method uses sliding window and fixed short time slices to eliminate the systematic and sporadic noise of the airflow. The method classifies the unknown respiratory event to an apnea or hypopnea event according to the Bayesian criterion. This is an effective and precise method to detect the sleep events and diagnose SAHS. It can fit the home care SAHS screener.