This paper presents a novel real-time adaptive sleep apnea monitoring methodology, suitable for portable devices used in home care applications. The proposed method identifies apnea/hypopnea events with the help of oronasal airflow signal and aimed to meet clinical standards in the assessment mechanism of apnea severity. It uses a strategically combined adaptive two stage classifier model to detect apnea or hypopnea events on the basis of personalized breathing patterns. For the detection of events, optimum set of time, frequency, and nonlinear measures, extracted from overlapping segments of typical 8 s were fed to support vector machine-based classifiers model to identify the possible origin of the segments, i.e., whether from normal or abnormal (apnea/hypopnea) episodes, and then the decision of the classifier model on the time sequenced successive segments have been used to detect an event. The performance of the proposed real-time algorithm is validated on clinical tests online. Average accuracies of hypopnea, apnea, and combined event detection when compared with polysomnography-based respective indices on unseen subjects during online tests were found to be 91.8%, 94.9%, and 96.5%, respectively, which are quite acceptable.