A Reliable Algorithm Based on Combination of EMG, ECG and EEG Signals for Sleep Apnea Detection (A Reliable Algorithm for Sleep Apnea Detection)

Sleep Apnea Syndrome is one of the most common and dangerous causes of sleep disorder that the suspected patients are tested (examined) by recording various types of vital signals during sleep using polysomnography (PSG). Since human body rhythms have a chaotic and non-linear behavior, the nonlinear analysis of body parameters provides the researchers with valuable information about body behavior during the disease and its comparison with the normal state for a more accurate examination of the diseases. The purpose of this is to diagnose apnea events using linear and nonlinear analyses and combining the EMG, ECG and EEG signals in patients with Obstructive Sleep Apnea (OSA). The research data are obtained by the Physionet database including 25 subjects (21 males and 4 females). After performing the pre-processing phase to remove the noise related to EMG, ECG, EEG and artifact signals based on the corresponding algorithms, the healthy and apnea sleep ranges are separated from one another. Linear and nonlinear analyses in MATLAB environment are performed on signals and conditions which are evaluated in healthy sleep and during sleep apnea at different stages of sleep in patients with OSA by multilayer perceptron classifier. The best result of the proposed algorithm obtained by combining the signals and the specificity, sensitivity and accuracy values are 96.87 +/- 1.78, 97.14 +/- 2.24 and 98.09 +/- 2.15 respectively. The results show that the proposed algorithm can help doctors and nurses as a diagnostic tool with more accuracy than similar techniques.