Abstract
Alzheimer's disease is the most common cause of dementia which causes a progressive and irreversible impairment of several cognitive functions. The aging population has been increasing significantly in recent decades and this disease affects mainly the elderly. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. Despite the progress in better understanding the disease there remains no prospect of cure at least in the near future. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to improve the scalp localization and the detection of brain anomalies (EEG temporal events) sources associated with AD by using the EEG.
TopIntroduction
In the last decades there have been deep changes in the age structure of world population, characterized by a progressive decrease in young population and inversely, by an increasing proportion of elderly people (Ballard, et al., 2011; Machado, 2002). The decrease of mortality and the simultaneous decrease of fertility levels have contributed to the phenomenon called overall aging of population. The aging population has become a fact of scientific interest around the world because the elderly are most vulnerable to the onset of certain degenerative diseases (Jonker, Launer, Hooijer, & Lindeboom, 1996). Alzheimer’s disease (AD) is one of the most debilitating diseases of developed societies. AD is an incurable illness, a chronic progressive and irreversible neurodegenerative brain disorder and the most common cause of dementia in the elderly (Ballard, et al., 2011). AD represents 60% of elderly diseases. Gradually, over time, most parts of the brain will suffer damage and symptoms will severely increase (Blennow, Leon, & Zetterberg, 2006) In 2001, there were more than 24.3 million of live people that have AD and according to estimates of Delphi in 2040 there will be 81.1 million AD patients (Ballard, et al., 2011). Experts do not know yet why the brain cells deteriorate. The cause of AD is not yet known. So far, no one single factor has been identified as being responsible to cause AD. It seems that a combination of factors, such as: age, stress, genetic inheritance, environment, lifestyle, lower educational qualifications, obesity, diabetes, hypertension, cholesterol, tobacco, alcohol, Down's syndrome and head injury, may be responsible for this disease (Cummings, 2004; Mayeux, 2003; Román, 2002). Ageing is considered the main risk factor to acquire AD; after the age of 65, the risk of developing the disease doubles every five years. Another factor is family history; people who have a close relative who developed Alzheimer's disease have a slightly higher risk of eventually developing the disease themselves (Blennow, 2005). Gender is considered another factor; a higher percentage of women develop AD than men. Heart disease, like high cholesterol, hypertension or poorly controlled diabetes are considered risk factors to develop AD (Blennow, Leon, & Zetterberg, 2006; Lahiri, Farlow, Greig, & Sambamurti, 2002).
Key Terms in this Chapter
Scalp: The skin covering the top of the human head.
Shift-to-the-Left: AD EEG slowing down relatively to the control subjects EEG. It can be seen notably in Alpha band peak of Power Spectral Density.
Unsupervised Classification: Type of machine learning algorithm used to draw extrapolations from datasets without labeled responses.
Alzheimer's Disease: Is an incurable disease that affects the brain. It is the most common cause of dementia among older people that causes problems with memory, thinking and behavior. There is no cure for the disease. Symptoms usually develop slowly and get worse over time, becoming severe enough to lead to death.
EEG Event: An Alzheimer's disease event is composed by a set of small EEG signal segments of 50 ms, characterized by a common and single dynamic energy.
Model: Model that expresses the functional dynamics of an AD patient brain.
Electroencephalogram: Physiological technique that record’s the brain’s electrical activity originated by the neurons with high resolution, widely used in clinical practice.
Signal Processing: Operation or analysis of analog/digitized signals and represents time-varying or spatially varying physical quantities.
Features: Patterns that characterize a signal.
Sources: Brain localization of the anomaly origin.