Self-attention models have emerged as powerful tools in both computer vision and Natural Language Processing (NLP) domains. However, their application in time-domain Electrocardiogram (ECG) signal analysis has been limited, primarily due to the lesser need for global receptive fields. In this study, we present a novel approach utilizing local self-attention to address multi-class classification tasks using the PhysioNet/Computing in Cardiology Challenge 2021 dataset, encompassing 26 distinct classes across six different datasets. We introduce an innovative concept called 'local lead-attention' to capture features within a single lead and across multiple configurable leads. The proposed architecture achieves an F1 score of 0.521 on the challenge's validation set, marking a 5.67% improvement over the winning solution. Remarkably, our model accomplishes this performance boost with only one-third of the total parameter size, amounting to 2.4 million parameters.