Design of Butterworth, Chebyshev Type I/II, and Elliptic Filters with ECG Low-Pass Processing

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In our research, we implemented four types of digital filters: Butterworth, Chebyshev Type I, Chebyshev Type II, and elliptic filters. These filters were applied for low-pass processing of ECG signals to eliminate high-frequency noise and extract meaningful physiological information. The implementation involved specifying critical parameters including passband/stopband frequencies, ripple coefficients, and attenuation requirements using filter design functions like butter(), cheby1(), cheby2(), and ellip() in signal processing environments.

Furthermore, we conducted comprehensive experiments and data analysis to validate the effectiveness and performance of each filter design. By comparing output signals across different filter types through metrics like signal-to-noise ratio (SNR) and frequency response analysis, we concluded that elliptic filters provided optimal roll-off characteristics for ECG applications, though with higher computational complexity. This comparative study led to proposed optimization strategies for real-time implementation constraints.

Overall, our research contributes significant advancements in biomedical signal processing, offering practical methodologies and technical frameworks for enhanced ECG signal purification and analysis.