<![CDATA[Heart rate measurement based on electrocardiogram (ECG) signals is commonly performed through traditional observation techniques, which can be less effective especially when restricted to short-range. Such limitations may hinder the detection of subtle variations in cardiac rhythm, potentially resulting in the omission of critical time-dependent physiological information. To overcome this challenge, a computational approach is introduced to enhance both the accuracy and consistency of heart rate estimation. The fast Fourier transform (FFT) is applied to convert ECG signals from the time domain to the frequency domain, allowing for accurate identification of dominant spectral components associated with cardiac activity. This transformation also enables the evaluation of long-range, which is often impractical to analyze using time-domain methods alone. A dataset consisting of ten ECG signal recordings from subjects with normal heart function was utilized. Waveform images were digitized using PlotDigitizer software and further processed in MATLAB through spectral transformation. The resulting frequency components were accurately identified, with a mean absolute error of less than 0.2% when compared to reference values. These results demonstrate the effectiveness of a frequency-based analytical approach in improving measurement precision and promoting efficiency in digital cardiac monitoring. The findings contribute to the development of advanced biomedical signal processing techniques.]]>
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