Chagas disease (ChD) is a chronic parasitic condition that can lead to severe cardiac complications. The use of electrocardiographic (ECG) analysis has emerged as a promising tool for early, non-invasive detection. This work, developed by the EPBandoleroLab team for the PhysioNet Challenge 2025, presents a deep learning approach for ChD classification using the CODE-15, SaMi-Trop, and PTB-XL databases. Our methodology explores the effectiveness of different signal representations, comparing the standard 12-lead ECG with the derived Vectorcardiogram (VCG). Furthermore, we address the significant class imbalance through a controlled sampling strategy. Our findings indicate that the model performs best when trained on the full 12-lead ECG representation with a moderately imbalanced dataset. This configuration achieved a Challenge Score of 0.259 in the official phase, placing our team in the top half of all competitors.