Safety-Oriented Interpretable ECG Denoising with Regression Tsetlin Machines

This paper proposes a safety-oriented and interpretable hybrid framework for ECG denoising that decouples artifact intensity estimation from waveform restoration. Three Regression Tsetlin Machines, trained with frequency-isolated features and hard negative mining, estimate normalized intensities of baseline wander, muscle artifact, and power line interference. Calibrated estimates modulate deterministic wavelet-based attenuation and adaptive notch filtering, enabling adaptive yet bounded suppression without direct waveform reconstruction. Evaluated on 1000 windows from 10 unseen patients under realistic mixed-noise conditions, the framework achieves a mean SNR gain of +8.50 dB (95% CI: [+7.90, +9.10]) and satisfies IEC 60601-2-25 amplitude tolerances in four of five noise categories. Integer-only inference requires 56 ms per 4-second window (71× real-time), while clause-level transparency supports feature auditing and physiological validation, enabling predictable behavior and suitability for safety-critical and embedded deployment.