A novel sleep apnea detection system in electroencephalogram using frequency variation

Obstructive sleep apnea syndrome is the most common respiratory disturbance in humans. Many new diagnosis and treatment methods are constantly being proposed. Electroencephalogram analysis has become one of the most important items in the diagnosis of obstructive sleep apnea syndrome. This paper proposes a novel sleep apnea detection system in electroencephalogram using frequency variation. The system utilizes a band-pass filter to filter out components with extreme low and high frequencies from the electroencephalogram. It also utilizes baseline correction to eliminate components with pseudo-interference frequency. Moreover, it extracts frequency elements from Hilbert spectrum by Hilbert–Huang transformation. The system then detects duration of obstructive sleep apnea from the variation of Hilbert spectrum frequency. The main contribution of the system is to preserve time information in the electroencephalogram by Hilbert–Huang transformation mechanism as well as find frequency variation information. The system also allows free adjustment of time scale to establish a flexible detection system with fast response so it is capable of real time detection of obstructive sleep apnea.