Biomedical Signal Processing

Upon completion of the subject, students should be able to: 1. extract useful information from a biomedical signal; 2. demonstrate an understanding of signal representation and processing across a range of biomedical devices; 3. apply advanced knowledge in biomedical image processing to develop and implement biomedical algorithms for processing biomedical images and critically interpret their success; 4. appreciate the mathematical principles of continuous and digital signal processing; 5. apply specific mathematical techniques to solve problems in the area of biomedical signals; and 6. describe the origin, properties, and suitable models of important biological signals such as ECG, EEG.

Signals: What is a signal? History; categories; application fields. Biomedical Signals: nature, sources, types and examples. Signal Processing: Definition, Stochastic and deterministic signals, Discrete signals, Linear time invariant systems, Duality of time and frequency domain, Hypotheses testing. Biomedical signal Processing: Brain signals-local field potentials (LFP), electrocorticogram, (ECG), electroencephalogram (EEG), and magnetoencephalogram (MEG); Heart signals - Electrocardiogram, Heart rate variability, Fetal ECG; Electromyogram; Gastro- intestinal signals; Acoustic signals. Modeling Biomedical Systems.