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Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection is crucial for effective treatment and management. Electrocardiogram (ECG) signals have been widely used for CVD diagnosis due to their non-invasiveness and ease of acquisition. However, the complexity of ECG signals and the presence of multiple CVDs in a single patient make the classification task challenging. Therefore, the early detection and diagnosis of cardiovascular diseases (CVDs) using electrocardiogram (ECG) signals is critical to preventing associated complications. This article proposes a novel approach for the reduced lead ECG multi-label classification task using 2D SEResnet with an attention mechanism. The proposed approach employs a 2D CNN architecture that incorporates wide SE residual networks (SEResnet) and a self-attention mechanism to extract relevant features from ECG signals …