Classification of sleep apnea using wavelet-based spectral analysis of heart rate variability

Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of 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 signal processing based technique is needed.

The main purpose of this work is to predict the severity of sleep apnea using an efficient wavelet-based spectral analysis method of the heart rate variability (HRV) to classify sleep apnea into three different levels (mild, moderate, and severe) according to its severity and to distinguish them from normal subjects.

The standard FFT spectrum analysis method and the soft-decision wavelet-based technique are to be used in this work in order to rank patients to full polysomnography. Data of 20 normal subjects and 20 patients with mild apnea and 20 patients with moderate apnea and 20 patients of severe apnea are used in this study. The data is obtained from the sleep laboratory of Sultan Qaboos University hospital in Oman. Four different classification versions have been used in this work.

Accuracy result of 90% was obtained between severe and normal subjects and 85% between mild and normal and 75% between severe and moderate and 83.75% between normal and patients.

The VLF/LF power spectral ratio of the wavelet-based soft-decision analysis of the RRI data after a high-pass filter resulted in the best accuracy of classification in all versions.