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　non-stationary　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　semi-supervised　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　real-world　and　synthetic　data-sets　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.　©　2019　IEEE.