International Research Journal of Education and Technology

Heart rate variability (HRV) is a critical indicator of autonomic nervous system function and overall cardiovascular health. Traditional methods for HRV analysis often rely on time-domain or basic frequency-domain techniques, which may not fully capture the complexity of heart rate dynamics. This study employs spectral and fractal analysis methods to provide a more comprehensive assessment of HRV. By using tools like the Fast Fourier Transform (FFT) and Detrended Fluctuation Analysis (DFA), this approach reveals deeper insights into the physiological mechanisms regulating heart rhythms. Implemented in a Jupyter Notebook environment, this project leverages Python libraries to process and visualize HRV data, offering a user-friendly and reproducible analysis workflow. Key findings demonstrate the robustness of combining spectral and fractal methods for detailed HRV characterization, potentially enhancing diagnostic accuracy and therapeutic monitoring.

Key Words: Heart Rate Variability, Spectral Analysis, Fractal Analysis, Fast Fourier Transform, Detrended Fluctuation Analysis, Jupyter Notebook, Python.