## 4 Discussion of Results  
A probabilistic-based framework was developed in this study for automated apnea detection using single channel data from oronasal airflow record (FlowTH). The proposed framework leverages AASM recommendations to define apnea based on relative changes with respect to respiratory airflow baseline. to overcome the absence of a precise mathematical definition for airflow baseline, a data-driven method is developed to represent it based on two features: The breath amplitudes and inter-breath intervals. The apnea is then characterized based on relative changes in these features between two sliding windows: The baseline window which represents the current respiratory baseline and the detection window where an apneic event is to be detected. For automatic detection of apneic events, we considered classification based on a probabilistic view using a GMM-based modeling framework. The proposed framework showed a significantly improved performance in detecting apnea compared to a rule-based classifier that uses the same input features as well as two previously published algorithms that respectively use threshold-based classification and neural networks, applied on time domain features from the same respiratory signal. Using relative changes in respiratory features to define apnea enabled a dynamic approach that accounts for continuous changes in the respiratory baseline making AICPVwTH and AICPVwGMM algorithms significantly more capable to detect apneic events than previous studies that considered absolute changes in classification features overlooking relative ones. Comparing the proposed model AICPVwGMM with AICPVwTH that uses a rule-based classifier showed a significantly improved performance for the GMM model characterized by achieving high \(TPR\) with a significantly improved overall \(PPV\) for all types of apnea detections. The proposed model also allows much better performance over different apnea severity levels compared to the rule-based classifier which showed best performance over severe apnea patients only with significantly degraded performance over less severe disease classes. In recent years, many studies considered oronasal thermal airflow signal for automated apnea detection [19; 20; 21; 39; 42; 43; 44; 45; 56]. Nevertheless, patients with severe and moderate OSA conditions have been a major focus of many of these studies while not giving sufficient attention to less severe patient populations or other types of apnea events. The present results highlight the importance of evaluating apnea models on patients of varying severity conditions as well as on different apnea types. This also agrees with previous literature which demonstrated that high performance accuracies achieved with patients with high severity levels may not be generalizable to other groups of patients [57; 58]. The detailed analysis presented in this paper using patients of varying apnea severity conditions and different apnea types provides the basis for a more comprehensive understanding of the performance of apnea detection systems. Comparative analysis between the performance of proposed modeling framework and previously published research highlights some of the previously reported limitations for single-respiration channel based apnea detection methods. In particular, previous studies have reported significant fall in the diagnostic accuracy of automated sleep systems that measure two or fewer physiological parameters as opposed to those that measure three or more physiologic variables [38; 57]. Although many automated sleep systems have proved effectiveness assisting lab PSGs and at home, they still cannot completely replace dedicated centers for sleep studies [59]. The present study leverages AASM recommendations for apnea detection in many aspects. We considered the AASM recommended sensor for scoring apneic events in PSG diagnostic studies which is the oronasal thermal airflow sensor.