The　Electrocardiogram　(ECG)　is　commonly　used　to　detect　arrhythmias.　Traditionally,　a　single　ECG　observation　is　used　for　diagnosis,　making　it　difficult　to　detect　irregular　arrhythmias.　Recent　technology　developments,　however,　have　made　it　cost-effective　to　collect　large　amounts　of　raw　ECG　data　over　time.　This　promises　to　improve　diagnosis　accuracy,　but　the　large　data　volume　presents　new　challenges　for　cardiologists.　This　paper　introduces　ECGLens,　an　interactive　system　for　arrhythmia　detection　and　analysis　using　large-scale　ECG　data.　Our　system　integrates　an　automatic　heartbeat　classification　algorithm　based　on　convolutional　neural　network,　an　outlier　detection　algorithm,　and　a　set　of　rich　interaction　techniques.　We　also　introduce　A-glyph,　a　novel　glyph　designed　to　improve　the　readability　and　comparison　of　ECG　signals.　We　report　results　from　a　comprehensive　user　study　showing　that　A-glyph　improves　the　efficiency　in　arrhythmia　detection,　and　demonstrate　the　effectiveness　of　ECGLens　in　arrhythmia　detection　through　two　expert　interviews.