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Biomedical signals like ECG, EEG, EMG are complex and non-stationary. Filtering aims at noise reduction, frequency enhancement, or specific frequency removal. Analog filters are cheap but sensitive to environment; digital filters are more accurate and flexible. Spectral analysis uses DFT/FFT for frequency domain representation, with considerations on windowing and zero-padding. Non-parametric methods like periodogram estimate power spectral density (PSD), while parametric methods use autocorrelation function (ACF) and Wiener-Khinchin theorem. Welch method improves Bartlett by overlapping windows to reduce edge artifacts. Cross-spectral analysis examine
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- Biological systems are complex due to interactions and high variability, often non-stationary, with signals corrupted by noise; deterministic or stochastic approaches are used for processing.
- Signal types include ECG (deterministic), EEG (stochastic stationary), EMG (stochastic non-stationary), ABP (DC-20 Hz to 150 Hz), and RESP (0.05-20 Hz); filtering aims at noise suppression, frequency enhancement, or special operations.
- Filtering methods include analog (influenced by environment) and digital filters (immune to noise, flexible in characteristics); IIR and FIR filters are discussed with their advantages and disadvantages.
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