  Arrhythmia is just one of the many cardiovascular illnesses that have been
extensively studied throughout the years. Using multi-lead ECG data, this
research describes a deep learning (DL) pipeline technique based on
convolutional neural network (CNN) algorithms to detect cardiovascular lar
arrhythmia in patients. The suggested model architecture has hidden layers with
a residual block in addition to the input and output layers. In this study, the
classification of the ECG signals into five main groups, namely: Left Bundle
Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature
Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat
(N), are performed. Using the MIT-BIH arrhythmia dataset, we assessed the
suggested technique. The findings show that our suggested strategy classified
15,000 cases with a high accuracy of 98.2%
