The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

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.

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

“…Many attempts have been made to reject or mitigate eye movement artifacts, to reduce interobserver variability, and to improve efficiency in visible inspection of the data (Croft et al , 2000, Cassani et al , 2014. Blind source separation, such as independent component analysis, is increasingly used for the detection and removal of ocular artifacts (LeVan et al , 2006, Gao et al , 2010, although it is unknown to what extent these artifact reduction methods influence functional connectivity and network metrics.…”

Electroencephalogram based diagnosis of Alzheimer Disease

“…It is the novel feature for AD diagnosis witch quantitatively monitors EEG amplitude modulation [22] [23]. The feature is termed as 'EEG spectro-temporal modulation energy'.…”

“…The following are the different steps involved in its computation. Firstly, the full-band EEG signal is decomposed into five wellknown sub-bands: delta (0.1 -4 Hz), theta (4 -8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30 -100 Hz). The Temporal envelope of each sub-band signal is computed by means of a Hilbert transform.…”

Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

“…1), the raw EEG is further processed by a state-of-the-art AAR algorithm. Motived by our recent findings [27], we used the wavelet-enhanced independent component analysis (wICA) algorithm [28].…”