  The classification of the electrocardiogram (ECG) signal has a vital impact
on identifying heart-related diseases. This can ensure the premature finding of
heart disease and the proper selection of the patient's customized treatment.
However, the detection of arrhythmia is a challenging task to perform manually.
This justifies the necessity of a technique for automatic detection of abnormal
heart signals. Therefore, our work is based on the classification of five
classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset.
Artificial Neural Networks (ANN) have demonstrated significant success in ECG
signal classification. Our proposed model is a Convolutional Neural Network
(CNN) customized to categorize the ECG signals. Our result testifies that the
planned CNN model can successfully categorize arrhythmia with an overall
accuracy of 95.2%. The average precision and recall of the proposed model are
95.2% and 95.4%, respectively. This model can effectively be used to detect
irregularities of heart rhythm at an early stage.
