Estimating ECG Signal Frequency Variation for Cardiac Analysis

This prototype program for estimating ECG signal frequency variations and distinguishing between normal and abnormal ECG signals serves as a critical diagnostic tool. By analyzing frequency domain characteristics through Fast Fourier Transform (FFT) or Wavelet decomposition algorithms, the system enables comprehensive assessment of cardiac function and physiological conditions. The implementation typically involves pre-processing stages (noise filtering using bandpass filters), feature extraction (dominant frequency detection, spectral power analysis), and machine learning classification (SVM or neural networks) to identify pathological patterns. This program assists clinicians and researchers in interpreting cardiac pathologies more effectively by providing key insights into heart rhythm regularity and cardiovascular health indicators. Through automated anomaly detection algorithms with threshold-based alerts, the system enhances early identification of aberrant ECG patterns, facilitating timely intervention for cardiac abnormalities. The integration of signal processing techniques with clinical validation makes this prototype invaluable for cardiac disease diagnosis and treatment planning.