Biomedical Signals Processing EQUP 4336

Course description:

The purpose of this course is for students to learn about biomedical signal processing techniques as applicable to medical instrument design. The course will begin with and overview of analog and discrete-time signals properties, Fourier analysis, and time-frequency analysis. Then study discrete-time filter, the Z-transform, and sampling. The convolution theorem for the Fourier and z-transform will be presented. Next, Various estimation, detection and filtering methods are developed and demonstrated on biomedical signals. The methods include harmonic analysis, autoregressive model, Wiener and Matched filters, linear discriminants, and independent components. All methods will be developed to answer concrete question on specific data sets in modalities such as ECG, EEG, MEG, Ultrasound. The lectures will be accompanied by data analysis assignments using MATLAB.

Course Aims:

Demonstrate the role of analog and digital signal processing in the several Bio-medical applications.
Apply the processes of sampling, quantisation and filtering of clinical biomedical signals.
Apply adaptive filtering mechanisms as applied to removing mains noise from clinical ECG data.
Apply fundamentals of the Artificial Neural Network to predict and classify biomedical signals.
Analyze the physiological data with the particular focus of detecting events in bio-medical signals.
Demonstrate the need for digital signal processing in medical instrument design and clinical practice.

Course outcomes:

By the end of this course, the students should be able to:

  • demonstrate an advanced understanding of the principles of analog and digital signal processing.
  • know the fundamental tools that are used to describe, analyze and process biomedical signals.
  • systematically apply methods to extract relevant information from biomedical signal measurements.
  • Aware about the fundamental principles in the analysis and design of filters, power spectral density estimation and non-stationary signal processing techniques with applications to biomedical signals.