Electrocardiogram (ECG) signals are vital for diagnosing cardiac abnormalities, but they are often corrupted by powerline interference noise, which can obscure critical features and compromise diagnostic accuracy. This work investigates various filtration techniques aimed at effectively removing powerline interference noise from ECG signals. Different approaches, including average filters and moving average filters, are explored and compared to determine their efficacy in noise reduction while preserving important signal characteristics. Experimental evaluations are conducted using synthetic ECG signals contaminated with simulated powerline interference noise, as well as real-world ECG recordings corrupted by actual powerline interference. The performance of each filtration technique is assessed based on metrics such as Root Mean Square Error (RMSE) and percentage root mean square difference (PRD). The trade-offs between noise reduction effectiveness and preservation of ECG signal fidelity are analyzed to identify the most suitable approach for clinical applications. This work provides valuable insights into the selection and implementation of filtration techniques for mitigating powerline interference noise in ECG signals. The findings contribute to the development of robust signal processing methods for improving the reliability and accuracy of ECG-based diagnostic systems.