Detection of Hypopnea Using Respiratory Signals

Abstract

Hypopnea is a respiratory disorder that affects people's sleep quality and reduces their standard of living. Detection and treatment of sleep disorders are costly. It requires time and effort. Because patients have to spend their time with special systems in which their physiological signals are recorded and specialist personnel in their sleep laboratories. Polysomnograms should be analyzed by medical doctors every night. Reliable sleep stage scoring is done manually by experts. This means that each morning, a specialist visually analyzes the 960 period of an eight-hour polysomnogram to create a hypnogram. This requires a long time. In this study, a method for automatic detection of hypopnea by eliminating the effect of the doctor is proposed. In this method, epoxes were scored by using air flow, thorax and abdominal amplitude information obtained from the person. A training data was created using hypopnea and normal epochs and grading was performed using the determined attributes. Quadratic Support Vector Machines (SVM) gave the highest accuracy when determining the presence of hypopnea. The linear DVM method was trained in 90.6% accuracy and the system was then tested. It was found that hioped epochs can be detected with 90% sensitivity.