Abstract
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
Highlights
Alzheimer’s disease (AD) is a chronic neuro-degenerative disorder that has recently been ranked as the third most expensive disease and the sixth leading cause of death in the United States (Leifer, 2003; Alzheimer Association, 2013)
Careful analysis of the Tables suggests that for all three tasks, the wavelet enhanced independent component analysis (ICA) (wICA) artifact removal (AAR) algorithm combined with the top 24 features selected from the “All-feature” set resulted in the best classification performance
This paper has evaluated the effects of different state-of-the-art AAR algorithms on diagnosis performance; AAR algorithms were tested both alone and in tandem
Summary
Alzheimer’s disease (AD) is a chronic neuro-degenerative disorder that has recently been ranked as the third most expensive disease and the sixth leading cause of death in the United States (Leifer, 2003; Alzheimer Association, 2013). In 2012, the World Health Organization (WHO) stated that between 60–70% of dementia cases around the world were due to AD, making it the most common form of dementia As such, it called for improved (early) diagnosis, as well as better care and support for patients, their families, and caregivers (WHO and Alzheimer’s Disease International, 2012). It called for improved (early) diagnosis, as well as better care and support for patients, their families, and caregivers (WHO and Alzheimer’s Disease International, 2012) With regards to the former, today diagnosis is commonly carried out using laboratory tests, medical history, mental status examinations, and more recently, neuroimaging tools such as functional magnetic resonance imaging (fMRI). In medium- and low-income countries, as well as in rural and remote regions (e.g., the Canadian Arctic), these limitations are further exacerbated, hindering the effectiveness of very early disease diagnosis (Sarazin et al, 2012)
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