MATLAB Code Implementation for FIR Filter

FIR (Finite Impulse Response) filters are commonly used signal processing tools widely applied for eliminating noise from electrocardiogram (ECG) signals. By processing ECG signals, FIR filters effectively remove noise interference, resulting in cleaner and more accurate cardiac signal representations. The fundamental operation of this filter relies on a predetermined set of coefficients that adjust the weighting of different frequency components in the input signal, thereby achieving noise suppression. In MATLAB implementation, designers typically utilize functions like `fir1` or `firpm` (Parks-McClellan algorithm) to generate optimal filter coefficients based on specified parameters such as cutoff frequencies and filter order. The filtering process involves convolution between the input ECG signal and the filter coefficients using the `filter` or `conv` functions. Key implementation considerations include selecting appropriate window functions (e.g., Hamming, Hanning) for coefficient calculation and determining the optimal filter order to balance frequency response and computational efficiency. For ECG applications, FIR filters are particularly valuable due to their linear phase characteristics, which preserve signal morphology without introducing phase distortion. Common design approaches involve bandpass filtering (e.g., 0.5-40 Hz) to retain physiological components while eliminating baseline wander and high-frequency noise. The stability of FIR filters (guaranteed by their finite impulse response nature) makes them suitable for real-time biomedical signal processing applications. Thus, FIR filters not only enhance ECG signal quality but also improve signal reliability and usability, making them essential tools in biomedical signal processing domains.