Geoffrey Green's Home Page

Department of Systems and Computer Engineering

Geoffrey Green, B.Eng, M.A.Sc., Ph.D candidate

Office: 6070 Minto
Phone: (613) 520-2600 ext 1074
Email: geoffgreen AT ieee.org

PhD Supervisors: Dr. Adrian Chan , Dr. Rafik Goubran

PhD Thesis (in progress) - "Biomedical Applications of Electronic Nose Technology"

An electronic nose (e-nose) is an instrument intended to mimic the human sense of smell (olfaction). It consists of a sample handling system, an array of gas sensors (with overlapping specificities), and an associated pattern recognition system. The headspace above a test substance contains various odorant molecules, or volatile organic compounds (VOCs), that are introduced to the sensor array by the sample handling system. Sensor technologies include those based on conducting polymers, metal oxides, mass spectrometry and surface acoustic waves. The sensor outputs (after signal processing and feature extraction) provide a unique "smellprint" for that substance which can be used to classify, measure concentration, or verify quality.

During the last decade, e-nose technology has been deployed for quality control and process monitoring in the food, beverage and petroleum industries. The possibility of using e-nose for applications in the medical field (specifically for disease diagnostics) has garnered increased research attention as of late. Several studies indicate that when people are afflicted with ailments such as diabetes, lung cancer, and urinary tract infections (among others), biological samples collected from them (e.g. breath, urine, sputum) produce a discernable pattern of volatile organic compounds (VOCs). This forms, in essence, a "smell signature" for that disease that can be used to diagnose the presence (or potentially determine the progression of) the condition with reasonable accuracy. Though not yet commonly deployed in a clinical setting, the potential advantages are numerous:

faster diagnosis of medical conditions from a primary care provider (once trained, an e-nose system can provide results within minutes)
reduced cost to the health care system (more expensive diagnostic tests can be avoided)
reduction of inappropriate treatment (e.g. since antibiotics are commonly prescribed before the true nature of the disease is known from microbiology labs)

The challenges that must be overcome in order to deploy such systems in a medical setting are many and varied. Among the ideas that I am investigating include:

detection of discrimination of bacteria. We have a research partner - the Canadian Food Inspection Agency (CFIA) - who are interested in the potential viability of using e-nose for early detection of bacteria to enhance food safety. At present, we are investigating two nonpathogenic bacteria - Listeria innocua and E. coli DH5 - grown in nutrient media and we hope to learn some important parameters such as the detection limit (in terms of bacteria concentration)).

Laboratory

At the Biomedical Signals and Sensors Lab (Department of Systems and Computer Engineering, Carleton University), we have access to three electronic nose instruments:

AlphaMOS FOX (a benchtop instrument consisting of 12 metal oxide conductivity sensors)
AlphaMOS Kronos (a benchtop instrument consisting of a quadrupole mass spectrometer)
Cyranose 320 (a handheld instrument consisting of 32 conducting polymer sensors)

Resume

Software

Wavelet-based denoising software developed during M.A.Sc. (Matlab) - includes PET data files, wavelet transform (forward and inverse), denoising routines, and a GUI (with user's manual)

Publications

Papers in Refereed Conference Proceedings

G.C. Green, A.D.C. Chan, R.A. Goubran, B.S. Luo, M. Lin, "A rapid and reliable method of discriminating between Listeria species based on Raman spectroscopy", in Proc. 2008 IEEE Instr. and Meas. Tech. Conf., Victoria, Canada, pp. 1046-1050.

G. Green, A.D.C. Chan, and R.A. Goubran. “Dimensionality Reduction Methods of Electronic Nose Data for Bacteria Discrimination", Proceedings of the Canadian Medical and Biological Engineering Conference, June 2007.

A.D.C. Chan and G. Green. "Myoelectric Control Development Toolbox", Proceedings of the Canadian Medical and Biological Engineering Conference, June 2007.

G. Green, A.D.C. Chan, and R.A. Goubran. “An Investigation into the Suitability of using Three Electronic Nose Instruments for the Detection and Discrimination of Bacteria Types ", Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, August 2006.

G. Green, A. Cuhadar, and R.A. deKemp. “Wavelet-based Denoising for ⁸²Rb Cardiac PET Imaging ", Proceedings of the Society of Nuclear Medicine Annual Meeting, June 2005.

G. Green, A. Cuhadar, and R.A. deKemp. “Spatially Adaptive Wavelet-based Thresholding of Cardiac PET Data", Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, September 2004.

Thesis

G. Green (2005) Wavelet-based Denoising of Cardiac PET Data. M.A.Sc thesis, Department of Systems and Computer Engineering, Carleton University (Thesis defence presentation)

EMCP 2006/EMCP 2007

In the spring, I teach a week-long course entitled "Signals in Action!" to a group of high school students (grades 8-11). This was done as part of the Carleton University Enrichment Mini-course Program (EMCP). Slides for the latest course offering are available here.