Atrial　Fibrillation　(AF)　is　the　most　common　chronic　arrhythmia.　Effective　detection　of　the　AF　would　avoid　serious　consequences　like　stroke.　Conventional　AF　detection　methods　need　heuristic　or　hand-craft　feature　extraction.　In　this　paper,　A　deep　neural　network　named　multi-scale　convolutional　neural　networks　(MCNN)　based　AF　detector　is　proposed.　Instant　heart　rate　sequence　is　extracted　from　ECG　signal,　then　an　end-to-end　MCNN　detects　AF　with　the　instant　heart　rate　sequence　as　input　and　detection　result　as　output.　The　algorithm　was　tested　on　both　public　and　private　datasets.　On　the　public　dataset,　with　the　sensitivity　achieved　being　0.9822,　the　corresponding　specificity　is　0.9811,　and　the　overall　accuracy　is　0.9818.　The　area　under　an　ROC　curve　is　as　high　as　0.9962,　compared　to　the　AUC　of　the　best　conventional　method　is　0.9947.　Comparison　shows　that　the　MCNN　based　AF　detector　give　superior　accuracy　than　conventional　methods.　Test　on　private　dataset　also　shows　significant　improvement.　©　2017　International　Society　of　Information　Fusion　(ISIF).