Physiological Signal Processing
This upper-division undergraduate course provides a comprehensive examination of physiological signal processing, a critical field in biomedical engineering and instrumentation. Students will delve into the underlying physics of biopotential generation and measurement, progressing through the design and function of specialized biopotential amplifiers. A significant portion of the course covers analog and digital filtering techniques essential for noise reduction and signal enhancement. Practical applications are highlighted through detailed studies of common signal chains, including those for electrocardiography (ECG), electroencephalography (EEG), and pulse oximetry (SpO2). The course also addresses the theoretical and practical aspects of analog-to-digital conversion and digital sampling, ensuring a robust understanding of data acquisition and processing for biomedical signals.
Course outline
Lectures, virtual labs, and graded assignments — completed in your browser.
Syllabus
["Week 1: Lesson 1: Introduction to Physiological Signals and Biopotential Sources","Week 2: Lesson 2: Fundamentals of Biopotential Measurement and Electrodes","Week 3: Lesson 3: Instrumentation Amplifiers for Biopotential Acquisition","Week 4: Hands-on Lab: Biopotential Amplifier Design and Characterization","Week 5: Lesson 5: Principles of Analog Filtering for Signal Conditioning","Week 6: Lesson 6: Introduction to Digital Signal Processing and Discrete Systems","Week 7: Lesson 7: Digital Filtering Techniques and Implementation","Week 8: Hands-on Lab: Signal Filtering and Noise Reduction","Week 9: Lesson 9: Electrocardiography (ECG) Signal Chain and Analysis","Week 10: Lesson 10: Electroencephalography (EEG) Signal Acquisition and Processing","Week 11: Hands-on Lab: Physiological Signal Acquisition and Analysis","Week 12: Lesson 12: Pulse Oximetry (SpO2) and Photoplethysmography (PPG) Principles","Week 13: Lesson 13: Analog-to-Digital Conversion and Sampling Theory for Biomedical Signals","Week 14: Capstone Service Project: Design and Implementation of a Physiological Signal Processing System"]