Electrocardiogram　(ECG)　signals　play　a　vital　role　in　diagnosing　and　monitoring　patients　suffering　from　various　cardiovascular　diseases　(CVDs).　This　research　aims　to　develop　a　robust　algorithm　that　can　accurately　classify　the　electrocardiogram　signal　even　in　the　presence　of　environmental　noise.　A　one-dimensional　convolutional　neural　network　(CNN)　with　two　convolutional　layers,　two　down-sampling　layers,　and　a　fully　connected　layer　is　proposed　in　this　work.　The　same　1D　data　was　transformed　into　two-dimensional　(2D)　images　to　improve　the　model＇s　classification　accuracy.　Then,　we　applied　the　2D　CNN　model　consisting　of　input　and　output　layers,　three　2D-convolutional　layers,　three　down-sampling　layers,　and　a　fully　connected　layer.　The　classification　accuracy　of　97.38%　and　99.02%　is　achieved　with　the　proposed　1D　and　2D　model　when　tested　on　the　publicly　available　Massachusetts　Institute　of　Technology-Beth　Israel　Hospital　(MIT-BIH)　arrhythmia　database.　Both　proposed　1D　and　2D　CNN　models　outperformed　the　corresponding　state-of-the-art　classification　algorithms　for　the　same　data,　which　validates　the　proposed　models＇　effectiveness.