Bedside monitors are vital in medical settings such as Intensive Care Units (ICU) and operating rooms, enabling continuous real-time monitoring of critical patient parameters like heart rate, blood pressure, and oxygen saturation. Among these, the Electrocardiogram (ECG) signal is particularly crucial for diagnosing cardiovascular diseases (CAD) by depicting the heart’s electrical activity. However, the accuracy of ECG signals is often compromised by noise and interference from sources such as electrical disturbances and body movements. This research aims to enhance ECG signal clarity by employing Finite Impulse Response (FIR) digital filters to minimize noise. The research contributes to the field by providing a comparative analysis of filtering methods, with the Kaiser window method emerging as the most effective for reducing ECG signal noise. The data used is obtained from human body signals in the Lead II position. The data will then be processed and analyzed offline using digital filters. Three digital filtering techniques—Kaiser window, rectangular, and Tukey—were implemented and evaluated based on their Signal-to-Noise Ratio (SNR). The Kaiser window method demonstrated superior performance, achieving the highest SNR value of 8.49 dB, resulting in a cleaner, interference-free ECG signal display. The study’s findings underscore the importance of advanced filtering techniques in improving the accuracy of ECG monitoring, which could lead to more reliable diagnoses and better patient outcomes. This has significant implications for clinical practices, as it enhances the reliability and clarity of critical patient data, ultimately supporting better clinical decision-making.