Aims & Objectives
This course will provide introductory knowledge of the nature of typical biomedical signals, providing their origins, characteristics, typical interferences and noise sources. Clinical and biomedical problems are presented giving the most popular tools to analyse and evaluate current signal processing solutions, proposing also alternative designs. Practical experience of applying signal processing methods to patient data improve students ability to critically analyse different signal processing approaches, identifying their strengths and limitations.
Learning outcomes & Competences
On successful completion the students will have the knowledge and understanding the nature of the most important biomedical signals and the mathematical principles of continuous, digital and stochastic signal processing. Understand how to apply specific mathematical techniques to solve problems in the areas of biomedical signals.
Basics of signal theory
One dimensional digital signal processing
- Nature of Biomedical Signals Acquisition. The Cardiovascular System, Electrocardiogram (ECG), Phonocardiogram (PCG). The Nervous System, Electromyogram (EMG), Electroencephalogram (EEG). The Respiration system, Breath, Speech, Snoring, Cough. Transducers, Noise and artefacts. The multi-source, multi-sensor problem.
- Basic Signal Processing. Analog filters. Digital filters, FIR,IIR, Notch. Design of Digital filters. Adaptive filtering. Adaptive interference cancellation. Filtering of ECG, PCG, EEG signals using MATLAB.
- Statistical Processing of Biomedical signals. Random Variables & Processes. Intra- and inter-subject variability. Autocorrelation, Cross-correlation. Autoregressive models. Linear prediction. Spectrum Estimation. Parameter Estimation & Modeling. Noise cancellation. Parameters estimation and noise cancellation in ECG, EEG signals using MATLAB.
- Space-Time signal processing. Beamforming: Delay-and-sum and frequency-domain beamforming. MUSIC and ESPRIT algorithm. Signal separation. Principal Component Analysis (PCA). Independent Component Analysis (ICA). Array processing in Biomedical signals. EEG signal separation. Separation Of Maternal And Fetal ECG Signals. EEG/MEG Cordical Source Localization. Hearing Aid Arrays.
- Signal analysis. Integral Transforms, Fourier, Wavelets, Curvelets. Short-time signal analysis & Features extraction. Examples in Biomedical signals. Detecting QRS complexes in the ECG. Blood pressure measurements. Signal processing in pulse oximetry.
Recommended reading / Bibliography
Leif Sornmo, Pablo Laguna, “Bioelectrical Signal Processing in Cardiac and Neurological Applications”, Academic Press (2005)
Akay Metin (eds.), “Time Frequency and Wavelets in Biomedical Signal Processing” John Wiley & Sons (1998)
Bruce, Eugene N. “Biomedical Signal Processing and Signal Modeling”, John Wiley & Sons (2001)
Sergio Cerutti, Carlo Marchesi “Advanced Methods of Biomedical Signal Processing“, Wiley-IEEE Press (2011)
Rasha Bassam, “Phonocardiography Signal Processing”, Morgan and Claypool Publishers (2009)
Charles Lessard, John Enderle, “Signal processing of random physiological signals”, Morgan & Claypool Publishers (2006)
Teaching and learning methods
Final (Written) Exam (50%), Project Assignment (50%)