Abstract
Nowadays, sleep disorders become an important issue because it can adversely affect neurocognitive, cardiovascular, respiratory diseases, which subsequently induce the behavior disorder, majority of these cases up to 85% of these cases are obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and treat OSA is becoming a critical issue from both academical and medical perspective. Polysomnography (PSG) can monitor the OSA using relatively few invasive techniques. However, sleep studies are expensive and time-consuming because they require overnight evaluation at sleep laboratories with dedicated systems and attending personnel. To improve such inconveniences of exam and high cost, it is important to develop a simplified method to diagnose the OSA. The motivation of this study is to develop an OSA detection algorithm, which uses only electrocardiogram (ECG) signal. The procedures of this algorithm include three parts. At first, ECG signals are preprocessed by discrete wavelet transform (DWT) method in order to detect the R peck by removing the base line wander and power line noise. Based on the R peak position, the 126 features were generated from the ECG-derived respiration (EDR) signal using time domain and frequency analysis. The range scaling method can be applied afterward to normalize all features in order to minimize the effect of large range variation from the value of each feature. At last, a classification method called support vector machine (SVM) is used to classify if the subject shows OSA symptom or not in each minute. The 10- fold cross-validation method is applied to select the best SVM parameter for classification. By combining all the classification results, one can determine if the subject is normal or apnea. The accuracy of 88.29%, the sensitivity of 92.90% and the specificity value of 86.48%can be seen from the Apnea-ECG database using the minute by minute performance based algorithm. Beside, the accuracy of 100%, the sensitivity of 100% and the specificity value of 100% also can be seen from the database using the subject based performance. The goal of this study to reduce both the detection time and cost is accomplished.