This academic curriculum module delivers an analytical, biophysical, and computational exposition of biomedical signal processing, physiological sensing, digital filter design, spectral estimation, and machine learning-driven clinical decision support.
Key Technical Topics & Curricular Areas Covered:1. Physiological Signal Acquisition & Transduction: Biophysical characteristics and acquisition pipelines for Electrocardiograms (ECG), Electroencephalograms (EEG), Electromyograms (EMG), Photoplethysmograms (PPG), and Electrooculograms (EOG).2. Sampling Theory & Digital Filter Design: Nyquist-Shannon sampling theorem, oversampling considerations for analog antialiasing filters, linear-phase Finite Impulse Response (FIR) group delay latency (tau_g = M / (2*f_s)), and high-Q Infinite Impulse Response (IIR) notch filters for 50/60 Hz power-line interference rejection.3. Frequency & Time-Frequency Analysis: Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) bin resolution (Delta_f = f_s / N), Short-Time Fourier Transform (STFT), and Continuous Wavelet Transforms (CWT) for non-stationary biosignal decomposition.4. Signal-to-Noise Ratio (SNR) Optimization: Quantitative decibel formulations (SNR_dB = 20*log10(V_signal / V_noise)), baseline wander elimination, and digital artifact suppression.5. Modern AI-Era Computational Paradigms: Wavelet neural operators for multiresolution electrogram denoising; real-time Invariant Extended Kalman Filtering (IEKF) for optical motion artifact subtraction in wearable PPG; and neuromorphic Spiking Neural Networks (SNNs) for ultra-low-power on-chip edge classification in implantable devices.6. Engineering Systems Trade-offs: Linear phase FIR versus minimum-phase IIR latency, on-device edge microcontroller inference versus cloud telemetry bandwidth, and ADC quantization resolution (LSB = V_span / 2^n) versus dynamic range.7. Pedagogical Features: An interactive web-based ECG digital filtering simulator, comprehensive multi-tier conceptual and scenario review questions, and step-by-step worked quantitative engineering calculations with explicit physical units.
Format: Open Educational Resource (OER) prepared for persistent archiving on Zenodo and indexing in MERLOT.
Permanent Webpage URL: https://prep4uni.online/stem/physical-technologies/biomedical-engineering/biomedical-signal-processing/
