<p>Electrocardiogram (ECG) signal analysis is a fundamental tool in the diagnosis and monitoring of cardiovascular diseases. However, traditional evaluation of ECG modeling and analysis techniques is often hindered by noise, signal artifacts, and limited access to real patient data due to privacy constraints. To address these challenges, this paper introduces a novel parametric quartic interpolation spline (PQIS) method for ECG signal modeling, based on fourth-degree polynomial interpolation of physiologically significant control points. The PQIS method offers a low-complexity, control-point-driven framework that accurately preserves key morphological and temporal features of the ECG waveform. Quantitative validation demonstrates strong alignment between the real and modeled signals, with a Pearson correlation coefficient (PCC) of 0.996, a dynamic time warping (DTW) distance of 0.008, and a deviation of less than 2% in critical clinical parameters such as QRS duration and RR interval. Power spectrum analysis confirms that the synthetic signal closely mimics the frequency composition of the original, while time-domain cross-correlation (0.974 with a 0.000-s lag) highlights precise alignment. These results confirm that PQIS preserves diagnostic fidelity while enabling flexible modeling and waveform synthesis. The method’s parametric nature enables the generation of diverse ECG morphologies by adjusting control points, facilitating research and simulation without reliance on extensive real-world data. A preliminary analysis of photoplethysmogram (PPG) signals further suggests its potential adaptability to other biomedical signals, positioning PQIS as a versatile tool for advancing biomedical signal processing and synthetic data generation.</p>