Abstract
According to the United Nations, the world elderly population is growing at its fastest rate ever. It is expected that by 2050, more than 2 billion people will be aged over 60, with 70% of them living in low or middle-income countries. For many individuals, ageing causes cognitive decline and limited functional capacity, thus placing a burden on the healthcare system. A clear example of this is the prevalence of Alzheimer's disease (AD), which is now the third most expensive disease in the United States, after cardiovascular disease and cancer. Recent projections suggest that by 2038, AD will affect 2.8% of all Canadians, incurring a cumulative economic burden (CEB) of $872 billion, and increasing by 10-fold the demand for long-term care. A similar picture is projected for Brazil, where despite a lack of current statistics on the disease, life expectancy has increased due to development and governmental investment in the health, medical technology, and educational sectors. Early detection of the disease is critically important as it allows treatment that can significantly retard disease progression to be initiated. The projected impacts of delaying AD progression by two years, for example, can include 34% fewer Canadians in long-term care and a reduction in CEB of $218 billion. Today, very early detection of AD is difficult as it involves time consuming behavioral tests or expensive diagnostic tests based on magnetic resonance imaging (MRI) technology. Such hurdles are magnified numerous times in low- or middle-income countries, as well as in rural and remote regions of developed countries, where access to MRI machines is practically non-existent. This project aims to overcome these shortcomings by developing a portable low-cost AD diagnostic system based on innovative biosensor technologies available in the market today. More specifically, a system will be developed based on a portable electroencephalogram (EEG) device, which monitors the electrical activity produced by voltage fluctuations resulting from ionic current flows within the neurons of the brain. The device streams the recorded neurological signals to a portable mobile device via Bluetooth technology. Such mobile devices are ubiquitous even in low-income countries today, thus the proposed project constitutes a viable diagnostic solution. To achieve this goal, next-generation signal processing and machine learning algorithms will be developed by research groups from Canada (at INRS-EMT, Montreal) and Brazil (UFABC, São Paulo) in order to extract useful information from noisy EEG signals, which will be automatically classified as AD or normal. This system can perform an early and low-cost diagnosis of AD, leading to improvements in care for the elderly not only in Canada and Brazil, but worldwide. (AU)
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