A Study on the Detection of Obstructive Sleep Apnea Using ECG

ECG를 이용한 수면 무호흡 검출에 관한 연구

Obstructive Sleep Apnea(OSA) is a representative symptom of sleep disorder which is caused by airway obstruction. OSA is usually diagnosed through the laboratory based Polysomnography(PSG) which is uncomfortable and expensive. In this paper, the detection method for OSA events, using ECG, has been developed. The proposed method uses the ECG data sets provided from Physionet. The features for OSA events detection are the average and standard deviation of 1 minute R-R interval, power spectrum of R-R interval and S-pulse amplitude from data sets. These features are applied to the input of Neural Network. To evaluate the method, we used the another ECG data sets. And we achieved sensitivity of 89.66％, specificity of 95.25％. So, we can know that the features proposed in this paper are important to detect OSA.