Cardiovascular diseases (CVD) are the leading cause of death in the world, representing almost 31% of global deaths. Therefore, continuous research on early diagnosis methods has been made to achieve more effective treatments of CVDs. Electrocardiogram (ECG) stands as the most common technique used to monitor cardiac variations. In ECG, five key points (a.k.a. fiducial points) correspond to the wave peaks P, Q, R, S, and T, whereby anomalies in the relative positions of these fiducial points indicate a potential CVD. Thus, a real-time decision support system capable of detecting ECG fiducial points is of paramount importance to early treatment. This paper proposes a real-time method to identify fiducial points in ECGs leveraging for this moving averages and heuristics. The performance comparison between the proposed method applied to the MIT-BIH Arrhythmia Database over other relevant methods found in the literature has been carried out. Results show that the proposed method outperforms the other techniques concerning both accuracy and computational cost impact.