Overlapping electromyographic (EMG) interference with the ECG spectrum during acquisition remains challenging. Multi-resolution analysis techniques, including DWT, EMD, and VMD, typically rely on fixed parameters or thresholds, which limit their ability to suppress EMG noise without morphological distortion. This paper proposes a Particle Swarm Optimization (PSO)‑tuned adaptive total variation (TV) denoiser in the Tunable Q‑factor Wavelet Transform (TQWT) domain to attenuate EMG noise while preserving clinically relevant ECG morphology. The proposed method addresses the limitations of fixed settings across sub-bands containing noise with varying characteristics. Instead of applying fixed thresholds across TQWT sub-bands, adaptive TV is applied to each sub‑band, with optimal parameters selected automatically using PSO. The method was tested on an ECG database contaminated with real EMG noise. The results showed improvements of 11.07% compared with the hybrid adaptive method, 8.08% over the iterative regeneration method, and 17.99% over the Adaptive Wavelet Wiener Filter. Furthermore, in the highest noisy group, where SNRin < 4dB, the mean ± standard deviation of MSE was 0.000741±0.000632. Overall, the approach achieved efficient performance in real‑world EMG noise reduction while preserving ECG morphology.