MAS509 Biomedical Signal Processing and Instrumentation (Autumn 2025)

On successful completion of the course, the student should be able to:

• understand the basis in the theory of biomedical measurement systems, including sensors, signal conditioning methods, measurement techniques, patient interfacing and instrumentation used in assistive and healthcare technology
• understand the basis in the theory of biomechanical measurement systems, including motion, force sensors and visual motion capture system
• understand the basis in the theory of machine vision for human behavior analysis, fall and irregularity detection
• understand the basis in the machine vision an image processing related to assistive and health technology
• demonstrate how signals from biomechanical sensors can be fused for physical activity assessment and feature extraction
• demonstrate how wearable sensors and systems can be used in home monitoring, telemedicine and smart home control
• write a scientific report

This course focuses on cutting-edge methods for acquiring, processing, and interpreting biomedical data. It introduces students to various imaging and signal modalities, such as biopotentials, Electrocardiography (ECG), Optical Coherence Tomography (OCT), spectroscopy, Electroencephalography (EEG), and. The curriculum emphasizes both foundational knowledge and hands-on experience, preparing students to address real-world biomedical challenges using advanced computational techniques.

This course is dedicated to introduction of biopotentials, biomedical signal acquisition, filtering, and time-frequency analysis of bio-signals. Further, integrating machine learning (ML) techniques into biomedical applications. Students will learn how ML can perform supervised and unsupervised learning for bio-image/signal classification, regression and clustering. By the end of the course, participants will have a strong understanding of the theoretical concepts, practical tools, and interdisciplinary approaches required for a career in biomedical imaging and signal processing. This module includes the following content:

• Introduction to biopotentials
• Bio -signal acquisition and preprocessing
  • Biomedical sensors and measurement methods
  • Techniques for bio-signal data acquisition, noise reduction, and filtering in biomedical signals
  • Fourier analysis, and wavelet transformation to process bio-signals

• Biomedical computer vision imaging and image modalities analysis
  • Overview and principle of OCT, spectroscopy, EEG, ECG, and other imaging techniques used in biomedical research and clinical diagnostics.
• Machine Learning for Biomedical Applications
  • Application of supervised and unsupervised ML algorithms for classification, regression, and clustering in biomedical data including OCT image, spectrometer data and other modalities
  • Transfer learning and custom AI/ML models leveraging Convolutional Neural Networks (CNNs) for image classification, and segmentation. Examples include - ML based retina health detection based on OCT image, Heart health detection based on ECG time series data etc.

Compulsory assignments must be approved in order to take the examination.

Information will be given in Canvas when the semester starts.