Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices

Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Cecchi, Marco; Moore, Dennis K.; Anghinah, Renato

doi:10.1016/j.bspc.2016.12.009

Cited by 56 publications

(33 citation statements)

References 37 publications

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“…EEG systems with 32 or more channels are cumbersome and its electrode placement/adjustment can take around one hour or even longer. Long pretest procedures may provoke drowsiness, fatigue, stress, and/or alternate mental states that may alter EEG patterns and, consequently, study outcomes [ 85 ]. Another point to be considered, detailed in subsequent Section 3.4.7 , is the minimal density required in source location analysis, as greater numbers of channels increase precision [ 182 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nine papers reported the use of semiautomated methods based on the ICA (independent component analysis) method, which also require human intervention to label components as artifactual. Lastly, 18 articles made use of automated artifact removal (AAR) methods, such as FASTER and wavelet-enhanced independent component analysis (wICA), which are able to substitute the human intervention in the artifactual component selection [ 85 , 124 ], or of linear regression on electromyographic electrodes or of a notch filter tuned to the blink frequency [ 138 ]. In [ 84 ], different AAR algorithms were compared to evaluate their impact in the AD classification performance compared to raw and manually selected EEG signals and wICA was found to give the best results.…”

Section: Resultsmentioning

confidence: 99%

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Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

et al. 2018

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Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

et al. 2018

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“…EEG signals were recorded with a digital high-resolution 128-channel device (Brain Vision) using the International 10-10 system. 17 Sampling frequency was 10000 Hz and impedance of all electrodes was maintained below 10 kΩ. The recordings were performed at resting state, with participants comfortably seated in a reclined chair for approximately 25 min.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

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Fraga

Ferreira

et al. 2017

Dement. neuropsychol.

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Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms.ObjectiveTo understand the electroencephalographic differences in functional mechanisms between AD and DAI groups.MethodsThe study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis.ResultsThe eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas.ConclusionThe results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.

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“…scenarios where artifacts due to physical activity are present. Among these methods, the wavelet-enhanced ICA method, (Castellanos and Makarov, 2006), allows automated artifact removal, and has been proven effective in different scenarios where EEG was acquired with low-density wearable devices (e.g., Cassani et al, 2017;Rosanne et al, 2019). The parameter used for the wICA method in our experiments relied in a threshold K = 1 set empirically.…”

Section: Signal Processing and Feature Calculationmentioning

confidence: 99%

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research

et al. 2020

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With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

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Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices

Cited by 56 publications

References 37 publications

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research

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