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A Knowledge-Driven Expert System for Robust Cardiac Event Detection Using Multi-Scale Temporal Transformers
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AI
DOI：10.1111/exsy.70171.png)
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Abstract
En 中文
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.
Keywords:
artificial intelligence
expert systems
explanation facility
knowledge representation
wearable healthcare systems
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