ECG Signal Acquisition, Processing with Spectral Analysis and Feature Extraction

ECG signal acquisition, processing with spectral analysis and feature extraction constitutes a critical research domain in biomedical engineering. ECG signal acquisition involves converting human cardiac electrical signals into digital format through sensors for recording and computational analysis, typically implemented using analog-to-digital converters (ADC) with sampling rates between 100-1000 Hz. Processing encompasses spectral analysis through Fast Fourier Transform (FFT) algorithms to examine frequency domain characteristics, and feature extraction employing mathematical and statistical methods such as wavelet transforms or Pan-Tompkins algorithms for detecting QRS complexes, P-waves, and T-waves. These computational techniques enable identification of key cardiac patterns and abnormalities, which are vital for ECG-based diagnosis and continuous patient monitoring. The implementation typically involves digital filters for noise reduction (e.g., bandpass filtering 0.5-40 Hz), peak detection algorithms for heart rate calculation, and machine learning classifiers for arrhythmia detection, forming the foundation for advanced medical research and clinical applications in cardiology.