A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection

Oltu, Burcu; Akşahin, Mehmet Feyzi; Kibaroğlu, Seda

doi:10.1016/j.bspc.2020.102223

Cited by 72 publications

(33 citation statements)

References 34 publications

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“…According to World Alzheimer Report published in 2018, around 50 million people globally were living with dementia, corresponding to about 7.3% of the world's over-65-year-olds, and this number is projected to increase to 115 million by 2050 ( 1 ). Dementia affects not only individuals and their families but also the wider economy, with global costs estimated at about US$1 trillion annually, which is expected to increase to US$2 trillion by 2030 ( 2 ). Alzheimer's disease (AD) is the most common form of dementia and may account for an estimated 60–80 percent of cases ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Yamada

Shinkawa

Kobayashi

et al. 2021

Front. Digit. Health

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Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

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Section: Introductionmentioning

confidence: 99%

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Yamada

Shinkawa

Kobayashi

et al. 2021

Front. Digit. Health

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“…Numerous ML methods have been used to classify and predict AD stages with promising results (Haller, Lövblad et al 2011, Falahati, Westman et al 2014, Rathore, Habes et al 2017. While some studies have made use of a single screening modality, such as MRI (Fan, Batmanghelich et al 2008, Kloppel, Stonnington et al 2008, Cuingnet, Gerardin et al 2011, Liu, Zhang et al 2012, Tong, Wolz et al 2014, or electroencephalography (EEG) (Blinowska, Rakowski et al 2017, Farina, Emek-Savaş et al 2020, Ferri, Babiloni et al 2020, Oltu, Akşahin et al 2021, others have used a combination of multiple imaging techniques including MRI, PET, and cerebrospinal fluid (CSF) biomarkers (Zhang, Wang et al 2011, Gray, Aljabar et al 2013, Jie, Zhang et al 2013, Young, Modat et al 2013, Teipel, Kurth et al 2015, Yun, Kwak et al 2015, Samper-González, Burgos et al 2018. Although many of those studies presented interesting and promising results in AD classification, most focused on a so-called two-class problem.…”

Section: Introductionmentioning

confidence: 99%

Brain simulation augments machine-learning-based classification of dementia

Triebkorn

Stefanovski

Dhindsa

et al. 2021

Preprint

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IntroductionWhile the prevalence of neurodegenerative diseases and dementia increases, our knowledge of the underlying pathomechanisms and related diagnostic biomarkers, outcome predictors, or therapeutic targets remains limited. In this article, we show how computational multi-scale brain network modeling using The Virtual Brain (TVB) simulation platform supports revealing potential disease mechanisms and can lead to improved diagnostics.MethodsTVB allows standardized large-scale structural connectivity (SC)-based modeling and simulation of whole-brain dynamics. We combine TVB with a cause-and-effect model for amyloid-beta, and machine-learning classification with support vector machines and random forests. The amyloid-beta burden as quantified from individual AV-45 PET scans informs parameters of local excitation/inhibition balance. We use magnetic resonance imaging (MRI), positron emission tomography (PET, specifically Amyloid-beta (Abeta) binding tracer AV-45-PET, and Tau-protein (Tau) binding AV-1451-PET) from 33 participants of Alzheimer’s Disease Neuroimaging Initiative study 3 (ADNI3). The frequency compositions of simulated local field potentials (LFP) are under investigation for their potential to classify individuals between Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and healthy controls (HC) using support vector machines and random forest classifiers.ResultsThe combination of empirical features (subcortical volumetry, AV-45- and AV-1451-PET standard uptake value ratio, SUVR per region) and simulated features (mean LFP frequency per brain region) significantly outperformed the classification accuracy of empirical data alone by about 10% in the accuracy index of weighted F1-score (empirical 64.34% vs. combined 74.28%). There was no significant difference between empirical and simulated features alone. The features with the highest feature importance showed high biological plausibility with respect to the AD-typical spatial distribution of the features. This was demonstrated for all feature types, e.g., increased importance indices for the left entorhinal cortex as the most important Tau-feature, the left dorsal temporopolar cortex for Abeta, the right thalamus for LFP frequency, and the right putamen for volume.DiscussionIn summary, here we suggest a strategy and provide proof of concept for TVB-inferred mechanistic biomarkers that are direct indicators of pathogenic processes in neurodegenerative disease. We show how the cause-and-effect implementation of local hyperexcitation caused by Abeta can improve the machine-learning-driven classification of AD. This proves TVBs ability to decode information in empirical data by means of SC-based brain simulation.

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“…Several ML techniques for automated SS tasks have been applied in the articles included in this review ( Table 6 ). Ebrahimi et al [ 274 ] have identified four sleep stages, extracted features based on WT coefficients, and applied MLP with eight neurons in one hidden layer, achieving 93% accuracy. Similarly, Zoubek et al [ 275 ] have compared the performance between FFT and WT with two different classifiers: KNN and MLP.…”

Section: Discussionmentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection

Cited by 72 publications

References 34 publications

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions

Brain simulation augments machine-learning-based classification of dementia

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

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