Electrocardiogram (ECG) is the foundation of the analysis of cardiac disease. In the hospital clinical ECG diagnostic scenarios, when doctors analyze ECG signals or when an ECG intelligent diagnostic system is used, there might be strong noises like baseline wander or muscle artifact in the ECG signals due to the unstable state of the subjects, and such interferences are usually difficult to be filtered out by traditional filters, which can lead to serious errors in the subsequent signal analysis. To solve this problem, we propose a novel network which integrates hybrid transformer and multi-receptive feature extraction mechanism into score-based diffusion model. We used score-based diffusion model to reconstruct the clean ECG signals from noisy ones. The experiment was conducted on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of our method. Baseline methods are used for comparison. The evaluation results show that our method can achieve an outstanding performance on four distance-based evaluation metrics by at least 26% overall improvement in the comparison with the best baseline method. The study demonstrates that the signal denoising and reconstruction method based on the self-designed score-based diffusion model can effectively remove the interferences in the ECG signals, thereby facilitating the subsequent diagnosis in real-world situation. It also has huge potential for establishing the ECG intelligent analysis system.