A Knowledge‐Driven Expert System for Robust Cardiac Event Detection Using Multi‐Scale Temporal Transformers

ABSTRACT Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for expert systems that enable continuous and interpretable cardiac monitoring. We present ArmFormer, a knowledge‐driven expert system that leverages Transformer‐based reasoning for robust cardiac event detection from wearable armband electrocardiogram signals. The model integrates domain‐guided multi‐scale patch encoding to capture waveform morphology and rhythm dependencies, while local gated Transformer blocks enhance temporal continuity and suppress noise‐induced variability. A lead‐wise attention mechanism coupled with gradient‐based visualisation provides interpretability by highlighting clinically relevant regions such as QRS complexes, P waves, and ST segments. On an in‐house cohort of 99 subjects comprising 6211 normal and 10,030 abnormal 10‐s segments, ArmFormer achieved 91.66% accuracy, 91.57% F1‐score, 91.41% sensitivity, and 97.36% AUC under a subject‐exclusive protocol that prevents patient‐level information leakage. Compared with convolutional and residual baselines, AUC improved by up to 5%, while floating‐point operations and parameters were reduced by 29‐fold and 47‐fold, respectively, achieving 2.58 ms inference latency per segment. External validation on CPSC2018 and Chapman showed accuracies of 83.65% and 94.69%, with AUCs of 96.69% and 99.38%, respectively. By combining domain‐guided encoding, noise‐robust temporal reasoning, and interpretable attention, ArmFormer provides a practical and reliable framework for expert‐level cardiac event detection in wearable monitoring scenarios.