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　real-world　data　is　extremely　promising　as　we　find　its　accuracy,　sensitivity,　and　specificity　to　be　90.2%,　87.6%,　and　94.1%,　respectively.　©　2019　IEEE.