Electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiovascular diseases, yet traditional approaches often struggle with noise, missing leads, and limited training data. In this work, we present a novel supervised learning approach for ECG classification that leverages masked modeling techniques. Our method randomly masks 2-6 leads during training, forcing the model to learn robust representations from incomplete data. We implement this approach using a modified EfficientNetV2 architecture adapted for 1D signal processing, incorporating Squeeze-and-Excitation modules and both Fused and standard Mobile Bottleneck Convolution blocks to efficiently capture temporal dependencies in ECG signals. Our team ‘DTU_HealthTech' finished 7th in the unofficial phase of the PhysioNet Challenge 2025, having a Challenge score of 0.789. Experimental results in internal testing show that our masked modeling approach not only improves F1 scores by 8.2% compared to baseline methods but also maintains robust performance when tested on real-world clinical data with varying signal quality. This work demonstrates that supervised masked modeling provides an effective framework for ECG analysis, potentially reducing the need for perfect multi-lead recordings in clinical settings while maintaining diagnostic accuracy.