A new simple efficient classification technique for severity of sleep apnea with mathematical model and interpretation.

BACKGROUND: Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of the upper airway. Polysomnographic recording is a conventional method for detection of OSA. Although it provides reliable results, it is expensive and cumbersome. Thus, an advanced non-invasive heart rate variability (HRV) signal processing technique other than the standard spectral analysis, which also has efficiency limitations, is needed for identification of OSA and classification of apnea levels.

OBJECTIVE: The main purpose of this work was to predict the severity of sleep apnea using an efficient method based on the combination of time-domain and frequency-domain analysis of the HRV to classify sleep apnea into three different levels (mild, moderate, and severe) according to its severity and to distinguish them from normal subjects.

METHODS: The statistical signal characterization of the FFT-based spectrum of the RRI data is used in this work in order to rank patients to full polysomnography. Data of 20 normal subjects, 20 patients with mild apnea, 20 patients with moderate apnea and 20 patients with severe apnea were used in this study.

RESULTS: Accuracy result of 100% was obtained between severe and normal subjects, 100% between mild and normal subjects, and 100% between apnea (mild, moderate, severe) and normal subjects. This perfect accuracy is obtained using the parameter mean (mt). The physiological interpretation of the SSC parameters has been derived using a mathematical model system.

CONCLUSIONS: An efficient method for screening of sleep apnea with 100% efficiency in classification of sleep apnea levels, is investigated in this work.