Engineering and the Environment, University of Southampton

Knowledge and understanding
Having successfully completed the module, you will be able to demonstrate knowledge and understanding of:

• The origins and characteristics of some of the most commonly used biomedical signals, including ECG, EMG, evoked potentials, and blood pressure.
• Sources and characteristics of noise and artefacts in these signals.
• Use of these signals in diagnosis, patient monitoring and physiological investigation.
• The application of signal processing techniques, including coherent averaging, digital filtering, spectral estimation, cross-correlation, input-output modelling with biomedical signals, and the interpretation of results.
• Different approaches and options in common biomedical signal processing tasks, such as the estimation of evoked potentials, automatically detecting heart-beats from the ECG, and physiological modelling.

Cognitive (thinking) skills
Having successfully completed the module, you will be able to:

• Read and interpret current research literature relating to biomedical signal processing tasks, and summarize results obtained.
• Recognise, select and critically evaluate appropriate techniques for the solution of specific problems in the processing and analysis of biomedical signals.

Practical, subject specific skills
Having successfully completed the module, you will be able to:

• Implement key biomedical signal processing tasks in Matlab.
• Select and evaluate appropriate signal processing methods for specific biomedical applications, and interpret results critically.
• Synthesize results, discuss interpretations, and propose alternative algorithms.

Key transferable skills
Having successfully completed the module, you will be able to:

• Acquire a working knowledge of new software applications and IT resources.
• Implement specified algorithms by computer programming.
• Discuss technical issues within a multidisciplinary environment.
• Communicate results, conclusions and problems to health-care professionals (such as clinicians and clinical scientists) with limited mathematical background.

The aims of this module are to:

• Provide you with an awareness of different biomedical signals.
• Provide you with an awareness of some of the specific problems and objectives of biomedical signal processing.
• Enable you to analyse and critically evaluate current signal processing solutions in clinical and biomedical problems, and to propose alternative designs.

Objectives

• To introduce you to some biomedical signals, their origins and characteristics.
• To show you how appropriate signal processing methods are being applied to these signals for clinical practice and biomedical research, and enable you to chose between alternatives.
• To give you practical experience of applying signal processing methods to patient data.
• To develop your ability to critically analyse different signal processing approaches, identify some of their strengths and limitations, and enable you to formulate alternatives.

Biomedical signals

• Physiological origin
• Main characteristics (amplitudes, frequency range, clinically relevant features)
• Main clinical uses
• Noise and artefacts
• Examples taken from ECG, EEG, EMG and blood pressure.

Biomedical signal acquisition

• Transducers
• Sources of noise and artefact
• Basic measures to reduce noise and artefact.

Biomedical signal analysis

• Outline of diagnosis, monitoring and prognosis
• Biomedical signals as random data
• Intra- and inter-subject variability
• Specificity, sensitivity and repeatability of diagnostic procedures.

Application of signal processing techniques to biomedical signals, including

• coherent averaging
• digital filtering
• spectral estimation
• cross-correlation
• input-output modelling.

Signal processing for specific biomedical applications, including

• estimating evoked potentials
• detecting QRS complexes in the ECG
• biophysical and black-box modelling
• noise reduction.

1 week of intensive lectures, demonstrations and computing practicals.

Case studies will provide further practical insight into biomedical signal processing projects.

Computing laboratory practicals will be based on the MATLAB software package, using, for the most part, data previously obtained from patients and normal volunteers.

The typical laboratory class size will be 15. Two lecturers will assist you with practical work in the exercises provided. Feedback is given by advice and assistance in the laboratory session. Students joining the module may have widely varying experience of signal processing and the software used; this is dealt with by proportionate assistance during the computing laboratory sessions.

A range of computing tasks will be issued each day, and you will be expected to complete all tasks, which will require additional work following the one-week intensive teaching to achieve a full appreciation of the topic. Additional tutorial assistance will be available in the post-module period.

Selecting and implementing different biomedical signal processing techniques for set problems, and interpreting and discussing the results. These will be provided in example sheets at the end of the 1 week course. Tutorial support, model answers, and formative feedback will be provided for the example sheets.

Completing a formal assignment, which is either a second example sheet (as above), or based on reading a set paper in the literature, implementing the technique and then replicating / testing and critically evaluating the results.

Visits to health care units (such as the Hearing and Balance Centre or the South of England Cochlear Implant Centre at the ISVR, the Biomechanics Laboratory in the School of Health Professions and Rehabilitation Science, the Medical Physics, Intensive Care, Neonatal Intensive Care, or Clinical Neurophysiology Departments at local hospitals) will be arranged to provide further practical input, and stimulate clinical interest.

You are expected to read supporting texts and search for relevant material using IT resources (e.g. internet). A booklist is also provided.

Secondary text

Nonlinear Biomedical Signal Processing, 1, 2000, M Akay, 0780360117
IEEE Press: New York

Nonlinear Biomedical Signal Processing, 2, 2000, M Akay, 0780360125
IEEE Press: New York

Medical Physics and Biomedical Engineering, 1999, B H Brown et al, 0750303689
Institute of Physics Publishing: Bristol

Biomedical Signal Processing and Signal Modeling, 2001, E N Bruce, 0471345407
Wiley: New York

Signals and Systems in Biomedical Engineering Signal Processing and Physiological Systems Modeling, 2000, S R Devasahayam, 0306463911
Kluwer Academic/Plenum Publishers: New York

Physiological Control Systems Analysis, Simulation, and Estimation, 2000, M C K Khoo, 0780334086
IEEE Press: New York

Non-invasive Instrumentation and Measurement in Medical Diagnosis, 2001, R B Northrop, 0849309611
CRC: Boca Raton, Fla

Digital Signal Processing, 1975, A V Oppenheim
R W Schafer, 0132146355
Prentice-Hall: Englewood Cliffs, N J

Digital Signal Processing Principles, Algorithms, and Applications, 2nd Edition, 1992, J G Proakis
D G Manolakis, 002396815X
Macmillan: New York

Biomedical Signal Analysis, 2002, R M Rangayyan, 0471208116
Wiley-Academy: New York

Medical Instrumentation: Application and Design, 1978, J G Webster (ed), 0395254116
Houghton Mifflin: Boston

Periodical: IEEE Transactions on Biomedical Engineering, , IEEE: New York

Periodical: Medical and Biological Engineering and Computing, , Peregrinus: Stevenage

Periodical: Medical Engineering and Physics, , Elsevier: Amsterdam

Detection and Estimation Methods for Biomedical Signals, 1996, M Akay, 0120471434
Academic Press: San Diego

Biomedical Signal Processing, 1994, M Akay, 0120471450
Academic Press: San Diego

Time Frequency and Wavelets in Biomedical Signal Processing, 1998, M Akay (ed), 0780311477
IEEE Press: Piscataway, NJ