Accurate interpretation of 12-lead electrocardiograms (ECGs) is critical for early detection of cardiac abnormalities, yet manual reading is error-prone and existing CNN-based classifiers struggle to choose receptive-field sizes that generalize to the long sequences typical of ECGs. Omni-Scale CNN (OS-CNN) addresses this by enumerating prime-sized kernels inspired by Goldbach’s conjecture to cover every scale, but its exhaustive design explodes computational cost and blocks deeper, wider models. We present E fficient C onvolutional O mni-Scale Net work (EcoScale-Net), a hierarchical variant that retains full receptive-field coverage while eliminating redundancy. At each stage, the maximum kernel length is capped to the scale still required after down-sampling, and \(1 \times 1\) bottleneck convolutions inserted before and after every Omni-Scale block curtail channel growth and fuse multi-scale features. On the large-scale CODE-15% ECG dataset, EcoScale-Net reduces parameters by 90% and FLOPs by 99% compared with OS-CNN, while raising macro-averaged F1-score by 2.4%. These results demonstrate that EcoScale-Net delivers state-of-the-art accuracy for long-sequence ECG classification at a fraction of the computational cost, enabling real-time deployment on commodity hardware. Our EcoScale-Net code is available in GitHub Link .