Detection of apneic events from single channel nasal airflow using 2nd derivative method

• Published in: Computer methods and programs in biomedicine Vol. 91; no. 3; pp. 199 - 207
• Main Authors: Han, Jonghee, Shin, Hong-Beom, Jeong, Do-Un, Park, Kwang Suk
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.09.2008
• Subjects: Algorithms; Apnea; Diagnosis, Computer-Assisted - methods; Female; Humans; Internal Medicine; Male; Middle Aged; Nasal airflow; Other; Pattern Recognition, Automated - methods; Polysomnography - methods; Reproducibility of Results; Second derivative; Sensitivity and Specificity; Sleep; Sleep Apnea, Obstructive - diagnosis; Second derivative; Sleep; Apnea; Nasal airflow
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2008.04.012

Abstract Detection of sleep apnea is one of the major tasks in sleep studies. Several methods, analyzing the various features of bio-signals, have been applied for automatic detection of sleep apnea, but it is still required to detect apneic events efficiently and robustly from a single nasal airflow signal under varying situations. This study introduces a new algorithm that analyzes the nasal airflow (NAF) for the detection of obstructive apneic events. It is based on mean magnitude of the second derivatives (MMSD) of NAF, which can detect respiration strength robustly under offset or baseline drift. Normal breathing epochs are extracted automatically by examining the stability of SaO2 and NAF regularity for each subject. The standard MMSD and period of NAF, which are regarded as the values at the normal respiration level, are determined from the normal breathing epochs. In this study, 24 Polysomnography (PSG) recordings diagnosed as obstructive sleep apnea (OSA) syndrome were analyzed. By analyzing the mean performance of the algorithm in a training set consisting of three PSG recordings, apnea threshold is determined to be 13% of the normal MMSD of NAF. NAF signal was divided into 1-s segments for analysis. Each segment is compared with the apnea threshold and classified into apnea events if the segment is included in a group of apnea segments and the group satisfies the time limitation. The suggested algorithm was applied to a test set consisting of the other 21 PSG recordings. Performance of the algorithm was evaluated by comparing the results with the sleep specialist's manual scoring on the same record. The overall agreement rate between the two was 92.0% ( κ = 0.78). Considering its simplicity and lower computational load, the suggested algorithm is found to be robust and useful. It is expected to assist sleep specialists to read PSG more quickly and will be useful for ambulatory monitoring of apneas using airflow signals.