Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

Rossini, Paolo Maria; Miraglia, Francesca; Vecchio, Fabrizio

doi:10.1002/alz.12645

Cited by 40 publications

(30 citation statements)

References 79 publications

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“…The EEG signals were preprocessed on the MATLAB version R2021a environment software (https://kr.mathworks.com/) using the EEGlab version 2010 toolbox (https://sccn.ucsd. edu/eeglab/index.php) by applying FFT to obtain qEEG time-frequency (TF) images with a dimension of 875×656 for ECR with sub-bands (delta [1-4 Hz], theta [4][5][6][7][8], 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][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]) from each EEG channel and TF images colormap was normalized in the range of [− 20 20] dB. EEGlab is an interactive MATLAB toolkit for analyzing continuous and event-related EEG signals as well as other electrophysiological data.…”

Section: Eeg Recordings and Preprocessingmentioning

confidence: 99%

“…The branch of AI known as machine learning (ML) has been successfully implemented in medical research and used to predict the conversion of MCI-to-AD, 26 , 27 as with most studies for early diagnosis of MCI and other types of dementia only, EEG was used as a biomarker focus on a group study. 28 The goal of ML algorithms is to obtain elements of the data that are not visible using traditional statistical analysis techniques. This mimics the learning process of the human brain.…”

Section: Introductionmentioning

confidence: 99%

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CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

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Purpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods:The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). Results: The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively. Conclusion:The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

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Section: Eeg Recordings and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Simfukwe¹,

Youn²,

Kim³

et al. 2023

NDT

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“…57,58 AD-RAI outperformed plasma NfL in predicting syndromal conversion among preclinical and prodromal AD subjects. Combination of clinical features, plasma p-tau 181 , APOE ε4 genotype, and AD-RAI provides the best model in identifying early AD subjects with high risk of conversion to next syndromal stage.…”

mentioning

confidence: 94%

Comparing machine learning‐derived MRI‐based and blood‐based neurodegeneration biomarkers in predicting syndromal conversion in early AD

Yuan

Fan

Zhao

et al. 2023

Alzheimer's & Dementia

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IntroductionWe compared the machine learning‐derived, MRI‐based Alzheimer's disease (AD) resemblance atrophy index (AD‐RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects.MethodsWe recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p‐tau181, NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p‐tau181), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p‐tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years.ResultsOf 589 participants, 96 (16.3%) were A+T+C+. AD‐RAI performed better than plasma NfL when added on top of clinical features, plasma p‐tau181, and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+.DiscussionAD‐RAI outperformed plasma NfL in predicting syndromal conversion of early AD.Highlights AD‐RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD‐RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p‐tau181 and apolipoprotein E (APOE) with AD‐RAI is the best model for predicting syndromal conversion.

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“…• We have collected a resting-state EEG dataset by recruiting 187 tinnitus patients and 80 healthy subjects from the department of Otolaryngology, Sun Yat-sen Memorial hospital, Sun Yat-sen University. Compared with the previous studies [14], [26], the dataset has higher spatial precision (more electrodes) and a larger number of subjects, which eases the high individual variability of EEG data when applying deep learning methods and makes it possible for us to carry out a larger range of subject-independent experiments to uncover macroscopic EEG differences between the chronic tinnitus patients and the healthy individuals. The dataset can be accessed in drive.google.com/drive/folders/ 1Su1IWGyZlED-lINUTVGcY9dIL_1k1ip_.…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

Wang

Zhu

Zhou

et al. 2023

IEEE J. Biomed. Health Inform.

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Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

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Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

Cited by 40 publications

References 79 publications

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Comparing machine learning‐derived MRI‐based and blood‐based neurodegeneration biomarkers in predicting syndromal conversion in early AD

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

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