Prof David Simpson is Professor of Biomedical Signal Processing within Engineering and Physical Sciences at the University of Southampton.
The Biomedical engineering provides a stimulating mix of 'hard' science and engineering with the excitement of understanding biology better and the motivation to help patients.
His research and teaching focus on the application of engineering techniques (primarily those for data analysis using computing methods) in a medical context.
After schooling in Austria, he graduated in Biomedical Electronics from the University of Salford (1981) and then worked as a mathematics and physics teacher in Nigeria. He obtained his PhD in Electrical Engineering from Imperial College of Science, Technology and Medicine, University of London in 1988. From 1989 to 1998 he lectured in the Biomedical Engineering Program at the Federal University of Rio de Janeiro (Brazil), before becoming a research fellow in the Medical Physics Department of Leicester Royal Infirmary. He moved to the University of Southampton (ISVR) in 2001.
Research
Publications
Teaching
Contact
Research interests
Stochastic signal processing algorithms (methods for the analysis of random signals)
The control system regulating blood pressure, heart-rate, respiration and blood flow in the brain
Auditory Evoked Potentials (the responses of the brain to auditory stimulation)
Neuromuscular control of limb movements in humans and insects (locusts)
Improving hearing aids and cochlear implants
David Simpson's research aims to improve the care of patients. To this end he develops and applies signal processing methods that extract diagnostic information from complex biomedical signals (e.g. from traces of ECG or blood pressure) and improves the understanding of physiology and hence innovative diagnostic methods.
Figure 1
Figure 1: The analysis of biomedical signals provides new insights into physiology and paves the way to new diagnostic techniques.
Occasionally people wake up during surgery, which is highly distressing. It is a particular problem when a muscle relaxant is given as people are unable to move to tell people that they are awake. We hope to develop methods that can tell if people are awake in operations using either the brain response to sound, or the patterns of connection in the brain.
It is possible to measure the electrical response of the brain to sound. However the signals are very small and can be swamped by electrical activity from rest of the body. In this project we are developing better methods to measure the tiny signals and exploring their clinical applications.
We would like to develop a set of objective test methods that can be used to compare the various features of modern hearing aids and to predict which features will best improve patient benefit from hearing aids.
Developing methods to measure the performance of advanced features in hearing aids/cochlear implants and to predict how much they will benefit patients.
How can we improve existing muscle models and use these to detect the effect of therapies, medicines, and any other external factor on muscle performances?
Anagarita-Jaimes, N. C., Dewhirst, O. P., & Simpson, D. M. (2010). Non-linear modeling of cerebral autoregulation using cascade models. In P. D. Bamidis, & N. Palllikarakis (Eds.), Proceedings of the 22nd Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010 (pp. 93-96). (IFMBE Proceedings; Vol. 29). Springer.
Simpson, D. M., & Birch, A. A. (2008). Optimizing the assessment of autoregulation from black-box models. In 4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008) (pp. 1-4). Institute of Electrical and Electronics Engineers.
Whitehead, E., Simpson, D. M., Darekar, A., & Birch, A. A. (2008). Resting Satte fMRI: New maps from BOLD. Paper presented at 6th Annual Southampton Neuroscience Group (SoNG) Seminar, .
Bell, S. L., Lv, J., Manders, A. J., & Simpson, D. M. (2007). A bootstrap approach to the detection of auditory evoked potentials. In D. Butinar (Ed.), XX IERASG Biennial Symposium: Programme and Book of Abstracts (pp. 87). Slovene Society for Clinical Neurophysiology of the Slovene Medical Association.
Madariaga Estrada, H. G., Simpson, D. M., & Boylan, G. B. (2006). Changes in heart rate associated with neonatal seizures. In Proceedings of Annual Scientific Meeting & IBEX 2006 Exhibition (pp. 15-16). Institute of Physics and Engineering in Medicine.
Bravo, F. R. N., Simpson, D. M., Nadal, J., Panerai, R. B., Marinatto, M. N. V., Lopes, J. M. A., & Evans, D. H. (2005). Use of respiratory CO2 signals for the assessment of CO2 reactivity in newborns. Paper presented at IFMBE Proceedings of the 3rd European Medical and Biological Engineering Conference, IFMBE European Conference on Biomedical Engineering, Prague, Czech Republic, 20-25 November 2005, .
Simpson, D. M. (2003). Signal processing and health-care. Paper presented at IEEE EMBS UKRI Postgraduate Conference on Biomedical Engineering and Medical Physics, .
David Simpson has focussed on cross-disciplinary learning and teaching at the interface between engineering and medicine / life-sciences. Current courses cover signal processing for audio and biomedical applications.
Prof David Simpson Engineering, University of Southampton, Highfield, Southampton. SO17 1BJ United Kingdom
