In　this　paper　we　proposed　a　wearable　electrocardiogram　(ECG)　telemonitoring　system　for　atrial　fibrillation　(AF)　detection　based　on　a　smartphone　and　cloud　computing.　A　wearable　ECG　patch　was　designed　to　collect　ECG　signals　and　send　the　signals　to　an　Android　smartphone　via　Bluetooth.　An　Android　APP　was　developed　to　display　the　ECG　waveforms　in　real　time　and　transmit　every　30　s　ECG　data　to　a　remote　cloud　server.　A　machine　learning　(CatBoost)-based　ECG　classification　method　was　proposed　to　detect　AF　in　the　cloud　server.　In　case　of　detected　AF,　the　cloud　server　pushed　the　ECG　data　and　classification　results　to　the　web　browser　of　a　doctor.　Finally,　the　Android　APP　displayed　the　doctor＇s　diagnosis　for　the　ECG　signals.　Experimental　results　showed　the　proposed　CatBoost　classifier　trained　with　17　selected　features　achieved　an　overall　F-1　score　of　0.92　on　the　test　set　(n　=　7270).　The　proposed　wearable　ECG　monitoring　system　may　potentially　be　useful　for　long-term　ECG　telemonitoring　for　AF　detection.