Abstract:

BACKGROUND: Sleep apnea syndrome (SAS) is monitored and examined clinically with polysomnography. However, it is expensive and complex to operate, which significantly affects the natural sleep of human.
OBJECTIVE: To evaluate the value of heart rate variability (HRV) in diagnosing SAS, and propose a new method for SAS classification based on fuzzy support vector machine (FSVM).
METHODS: Detrended fluctuation analysis and autoregressive model spectrum estimation were used to analyze R-R interval sequence of 38 healthy subjects and 28 SAS subjects during various sleep stages. Scaling exponents of age, gender and HRV at each sleep stage, as well as low/high frequency were selected as SAS characteristic parameters. FSVM was used to classify SAS.
RESULTS AND CONCLUSION: Results indicate that the proposed method can diagnose SAS effectively and the classification accuracy rate of SAS is 93.94%. Compared with current SAS diagnosis methods, this method is more simple and accurate.