Researchers introduce BEAT-Net, a supervised biomimetic framework for ECG diagnosis using deep learning. It integrates QRS-centered biological tokenization with a hierarchical architecture, achieving comparable diagnostic accuracy to CNN baselines while reducing parameters by 95%. This framework demonstrates an efficient and interpretable alternative to massive pre-training for clinical deployment.