Real-time and accurate analysis of electrocardiogram (ECG) signals is a core requirement for clinical applications such as ambulatory monitoring. However, existing ECG denoising methods struggle to balance efficiency and fidelity. Traditional approaches, such as filtering and methods based on deep encoders, often compromise the fidelity of critical waveforms, especially in the presence of strong noise interference. Although methods based on Denoising Diffusion Probabilistic Models (DDPMs) have achieved significant progress in signal reconstruction, their high iterative inference cost and architectural limitations in modeling long-range dependencies severely restrict their clinical application potential. To address these challenges, this paper proposes a Conditional Accelerated Sampling Denoising (CASD) framework. This framework formulates the denoising task as an efficient, deterministic sampling process, capable of achieving high-fidelity signal reconstruction in as few as 10 sampling steps. At the core of CASD is a Global Feature Enhancement (GFE) module, which explicitly captures the long-range dependencies of the signal via a self-attention mechanism, thereby effectively suppressing baseline wander noise.