Implantable cardioverter defibrillators (ICD), life‐saving devices that may prevent sudden cardiac death from cardiac arrhythmias, are implanted in people who have had cardiac arrests and increasingly in people with severe heart failure who are at risk for a primary ventricular arrhythmia. The clinical benefits of ICD implantation contrasts with evidence suggesting ICDs may also have untoward effects on recipients’ quality of life and psycho‐emotional status. Yet, the research on ICDs and patient‐reported outcomes is very limited and has produced inconclusive findings.

The goal of this project with Agfa Healthcare, a provider of diagnostic imaging and healthcare IT solutions, is to develop mathematical models for (i) assessment and (ii) improvement of compressed medical images. The rapidly increasing volume of data generated by new imaging modalities (eg CT scanner, MRI) necessitates the use of lossy compression techniques to decrease the cost of storage and improve the efficiency of transmission over networks. Increasing the degree of compression of an image, however leads to decreasing fidelity.

Normal listeners have a remarkable ability to localize sounds because the brain can analyze the slight differences between the sound waves arriving at the two ears. These different cues are less important when the listener knows where a talker is located, but they are extremely important when speech comes from an unexpected location, as often happens in everyday situations.

The project consists of research into a technology that overcomes the shortcomings and deficiencies of the existing pain detection device. This approach modifies and improves pain detecting instruments by adding more intelligence and advanced designing techniques to the existing instrument. The existing design has too much wear & tear of the probe sensor, so our aim of the project is to overcome it by providing a disposable and replaceable probe. For this various moldings techniques, flexible circuit technology would be experimented and researched.

At the NRC Institute for Biodiagnostics, a developer of non-invasive medical devices and techniques to increase prospects for prevention, earlier diagnosis, improved treatment and prognosis of diseases, a new optical imaging technology is being developed for detecting early dental decay. In collaboration with Dr. Reza Fazel’s group at the University of Manitoba, a suite of image processing and image analysis methods will be developed to extract out clinically relevant parameters from the images.

Biopeak Corp, a medical device company, is developing a non-invasive, integrated, sensing platform that would allow people to be monitored for multiple parameters continuously day and night. This presents some major challenges in terms of wearable electronics that can survive several days of use, including size, power management, ease of use and comfort. All these issues require technological innovation. The research to be performed as part of this internship work will be to focus on one of the subsystems in this product.

Wearable health monitoring devices are identified as a viable option for preventing dangerous health problems and surveillance of after-incident patients. But given the high computing needs of such battery-powered systems, a trade-off must be made between miniaturization and device lifetime. On the other hand, the human bearer is a significant source of power, both in the form of heat and movement, which can be a natural power source for wearable health monitoring devices.

This internship with Photon Control, a Vancouver-based engineering design, development, and manufacturing company is the first phase of a project to design a highly sensitive magnetometer that will act as a non-invasive interface of a human brain to a machine or computer though many other commercial applications of this device are also anticipated. Electrochemical activity in the brain can be interpreted as a “signal” which in turn can be manipulated by a computer to derive information related to thoughts or intentions.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is a powerful emerging experimental technique which provides important information about the solvent exposed area of complex molecular systems that is not readily available through existing technologies. HDX-MS can be applied to the specific cases of small molecules binding to protein systems, which is very important for the understanding, and new design, of therapeutic compounds, including chemotherapeutic cancer drugs.

The digital and bio-mechanical analysis of human motion is an attractive research field as it helps one understand and interpret pathologies affecting the body’s articular components. The goal of this project is to develop data analysis and automatic classification methods to differentiate between data from asymptomatic and pathological individuals. The data will be drawn from 3D morpho-functional assessments. Developed methods could eventually be considered for the creation of decision support and pathology diagnosis tools.