Digital Signal Processing Biosignal Testbed
Real-time DSP hardware platform executing discrete-time IIR filtering algorithms.
Design and implement a second-order IIR notch filter (Q = 30) to suppress 60 Hz hum from corrupted ECG telemetry while preserving diagnostic ST-segment and QRS morphology. Explore interactive physiological instrumentation simulation, biomechanics, and biomedical engineering telemetry.
Ready to sample with Noise P_in = 0.5 mV².
Adjust Noise P_in, then execute biomedical trial.
Biomedical engineering telemetry and physiological states are evaluated rigorously using 64-bit IEEE 754 precision. Residuals reflect ordinary least-squares regression across successive observations. Continuous data structures support high-fidelity biological and physiological modeling.
The linear power transmission model applies across the tested linear range (P_in ≤ 1.1 mV²). Evaluated using a second-order IIR digital notch filter centered at f_0 = 60.0 Hz with sampling frequency f_s = 500 Hz and 3 dB notch bandwidth BW = 2.0 Hz (Q = 30). Input 60 Hz interference power is varied from 0.1 to 1.1 mV².
Real-time DSP hardware platform executing discrete-time IIR filtering algorithms.
Generates clean 12-lead diagnostic ECG signals at 72 BPM with standard amplitude (1.0 mV peak).
Injects calibrated 60 Hz sinusoidal noise power into the synthesized ECG signal.
Computes power spectral density before and after filtering with 0.1 Hz spectral resolution.
Displays raw corrupted ECG, filtered ECG, and residual error waveform in real time.
Calculates floating-point biquad numerator and denominator coefficients for IIR notch filter.
Design and implement a second-order IIR notch filter (Q = 30) to suppress 60 Hz hum from corrupted ECG telemetry while preserving diagnostic ST-segment and QRS morphology. Standardized parameter settings: 0.1, 0.3, 0.5, 0.7, 0.9, 1.1 mV².
Compute second-order IIR notch coefficients for f_0 = 60.0 Hz, f_s = 500 Hz, Q = 30: b = [1, -2·cos(ω_0), 1], a = [1, -2·r·cos(ω_0), r²] with r = 0.9874; load into DSP memory.
Input pure clean 1.0 mV ECG waveform without noise; confirm that filter passband causes zero visible attenuation or phase distortion of the QRS complex.
Superimpose 60.0 Hz sinusoidal hum to establish an input noise power P_in = 0.10 mV² (amplitude ~0.45 mV peak-to-peak).
Run real-time biquad filtering and compute FFT power spectral density at 60 Hz; log residual output power P_out in μV².
Increment 60 Hz interference power through 0.30, 0.50, 0.70, 0.90, and 1.10 mV², recording residual output power P_out at each step.
Plot residual output power P_out against input interference power P_in, performing linear regression to evaluate the power transmission ratio k_n.
Points are your recorded observations. The solid regression line validates ECG Powerline Notch Filter mathematical scaling.
| # | Input Interference Power P_in (mV²) | Residual Output Power P_out (μV²) | Input interference power P_in (mV²) | Output residual power P_out (μV²) | Remove |
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