Abstract
Alzheimer’s disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer’s patients have been acquired and processed from which AD-related information or “signals” can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.
Highlights
Alzheimer’s disease (AD), which was first recognized by Alois Alzheimer in 1906, is the most common cause of dementia in older adults [1,2]
Algorithm for Multiple Unknown Signal Extraction (AMUSE) was used y Vialatte et al, to process data for input for sparse-bump modeling, which was fed into a neural network geared towards the classification of mild cognitive impairment (MCI) cases, achieving a 93% classifier rate [150], an improvement from the aforementioned linear discriminant analysis (LDA) with an 80% implemented by the team [4]
As disease-modifying therapies are likely to be most efficacious at much earlier stages of the disease, it is important to develop markers for early disease detection in individuals who are at risk for AD
Summary
Alzheimer’s disease (AD), which was first recognized by Alois Alzheimer in 1906, is the most common cause of dementia in older adults [1,2]. Improving the accuracy of diagnosis of AD at its early stage is critical to finding a successful treatment. Critical to the early treatment of AD is the ability to discriminate between older individuals who will and will not develop the disease during this preclinical stage. With the advent of biomedical engineering techniques, more and more brain signals have been acquired and processed for AD diagnosis. These brain signals come from electroencephalography (EEG) [5], magnetoencephalography (MEG) [6], computerized tomography (CT) [7], single photon emission computed tomography (SPECT) [8], positron. We will focus on applications of ICA/BSS/in processing of brain signals for potential AD diagnosis
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