  Cardiovascular diseases are a pervasive global health concern, contributing
significantly to morbidity and mortality rates worldwide. Among these
conditions, arrhythmia, characterized by irregular heart rhythms, presents
formidable diagnostic challenges. This study introduces an innovative approach
utilizing deep learning techniques, specifically Convolutional Neural Networks
(CNNs), to address the complexities of arrhythmia classification. Leveraging
multi-lead Electrocardiogram (ECG) data, our CNN model, comprising six layers
with a residual block, demonstrates promising outcomes in identifying five
distinct heartbeat types: Left Bundle Branch Block (LBBB), Right Bundle Branch
Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular
Contraction (PVC), and Normal Beat. Through rigorous experimentation, we
highlight the transformative potential of our methodology in enhancing
diagnostic accuracy for cardiovascular arrhythmias. Arrhythmia diagnosis
remains a critical challenge in cardiovascular care, often relying on manual
interpretation of ECG signals, which can be time-consuming and prone to
subjectivity. To address these limitations, we propose a novel approach that
leverages deep learning algorithms to automate arrhythmia classification. By
employing advanced CNN architectures and multi-lead ECG data, our methodology
offers a robust solution for precise and efficient arrhythmia detection.
Through comprehensive evaluation, we demonstrate the effectiveness of our
approach in facilitating more accurate clinical decision-making, thereby
improving patient outcomes in managing cardiovascular arrhythmias.
