Advanced Physiological Signal Processing
BMG 510 delves into the theoretical foundations and practical applications of advanced signal processing for biological measurements. The course covers essential concepts such as digital filter design (FIR, IIR), time-frequency analysis (Fourier transforms, wavelet transforms), and advanced techniques for identifying and mitigating common artifacts inherent in physiological recordings. Emphasis is placed on developing robust algorithms for extracting meaningful information from complex biomedical signals, including heart rate variability from ECG and brain state analysis from EEG. Through a combination of lectures, problem-solving, and hands-on laboratory exercises, students will gain the expertise required to analyze and interpret high-dimensional physiological datasets, preparing them for research and development roles in biomedical engineering and related fields. The course culminates in a capstone project applying learned techniques to a real-world physiological signal processing challenge.
Course outline
Lectures, virtual labs, and graded assignments — completed in your browser.
Syllabus
["Week 1: Lesson 1: Review of Digital Signal Processing Fundamentals","Week 2: Lesson 2: Advanced Digital Filter Design for Biomedical Signals","Week 3: Lesson 3: Adaptive Filtering and Noise Reduction Techniques","Week 4: Hands-on Lab: Implementing Digital Filters and Noise Reduction","Week 5: Lesson 5: Time-Domain Analysis of Physiological Signals","Week 6: Lesson 6: Frequency-Domain and Time-Frequency Analysis","Week 7: Lesson 7: Introduction to Wavelet Transforms and Their Applications","Week 8: Hands-on Lab: Spectral and Time-Frequency Analysis of ECG/EEG","Week 9: Lesson 9: Artifact Detection and Removal Strategies for ECG Signals","Week 10: Lesson 10: Advanced ECG Feature Extraction and Analysis Algorithms","Week 11: Hands-on Lab: ECG Algorithm Development and Artifact Mitigation","Week 12: Lesson 12: EEG Signal Characteristics and Brain State Analysis","Week 13: Lesson 13: Advanced EEG Processing Techniques and Interpretations","Week 14: Capstone Service Project: Physiological Signal Processing Challenge"]