A SVMBased Algorithm to Diagnose Sleep Apnea Obstructive sleep apnea syndrome (OSAS) is a breathing disorder presenting during sleep. Although polysomnography (PSG) is the gold standard to diagnose OSAS, it is an expensive method that is quite complicated to use. Worse, it takes a long time between testing and getting a diagnosis from PSG. Thus, we have designed an algorithm aimed at diagnosing OSAS in a more efficient manner. First, blood oxygen saturation (SpO2) data are processed to obtain statistical features, which are then trained to establish a classification model based on a support vector machine (SVM) strategy; the resulting SVM model performs the diagnosis of OSAS. Furthermore, in order to allow remote diagnosis, we combine our algorithm with a monitoring system. To achieve this, physiological data are collected from a smart phone and then uploaded to the SVM model in the cloud. Once processed, a diagnosis report is returned to the smart phone. A preliminary evaluation of our algorithm based on realworld data is extremely promising as we find its accuracy, sensitivity, and specificity to be 90.2%, 87.6%, and 94.1%, respectively.

Sleep Disorder Data Stream Classification Based on Classifiers Ensemble and Active Learning Polysomnography (PSG) screening for obstructive sleep apnea (OSA) is time consuming. The OSA classification is very important for medical scientists and machine learning researchers. In the current work, we developed a classification method for electrocardiogram (ECG) data. The data set has two labels: sleep disorders or not. As a result, Active Learning is used as a classification technique. Data stream classification in a nonstationary environment is attaining more attention recently. It is a highly challenging task, since the concept drift and limited labeled data. Therefore, a classification model is needed to be struggling with concept drift detection and the need of labeled data. To solve these issues, we propose an efficient semisupervised method in this paper which uses Active Learning to detect concept drift in an unsupervised way and Classifiers Ensemble to keep higher predictions combined with weighted majority voting. Experiments results on realworld and synthetic datasets show the effectiveness of the proposed approach. For the initial experiment, we use an existing data set. The data set includes data for every 10 seconds, up to 6000 seconds, and 35 patients. We have used 80% of the data for training purposes and 20% of the data for testing purposes. Active Learning results show that our method can effectively detect OSA. The accuracy of the predicted result is 71%. Future research in this area will be to obtain data from hospitals and use our developed algorithms for OSA classification and prediction.