P68-F Abnormalities of cortical neural synchronization mechanisms in patients with Alzheimer’s diseases dementia: An EEG study

Karami, Vania; Amenta, Francesco; Noce, Giuseppe; Percio, Claudio Del; Lizio, Roberta; Pascarelli, Maria Teresa; Bluma, Marina; Babiloni, Claudio

doi:10.1016/j.clinph.2019.04.517

Cited by 3 publications

(2 citation statements)

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“…Some reviews, for example, do not look at EEG as a primary diagnostic tool [93,94,98], whereas others are solely concerned with EEG signal synchronization [95,96]. Furthermore, other publications [99,100] offer a wider overview of the entire dementia continuum, not just AD. In revisions [101,102], the key function types for Alzheimer's disease detection are thoroughly explored.…”

Section: Conditions Of Eeg Recording and Symptoms Of Admentioning

confidence: 99%

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

Ouchani

Gharibzadeh

Jamshidi

et al. 2021

BioMed Research International

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This study will concentrate on recent research on EEG signals for Alzheimer’s diagnosis, identifying and comparing key steps of EEG-based Alzheimer’s disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article’s purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer’s disease, extreme Alzheimer’s disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer’s disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer’s disease science.

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Section: Conditions Of Eeg Recording and Symptoms Of Admentioning

confidence: 99%

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

Ouchani

Gharibzadeh

Jamshidi

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

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“…Neuroimaging and primarily MRI provide essential information for AD dementia classification and prediction [ 12 , 13 , 14 ]. ML models, coupled with MRI information, can provide high diagnostic accuracy of age-related cognitive decline (ARCD) in dementia subjects [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Battineni

Chintalapudi

Amenta

et al. 2020

JCM

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Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

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An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes

2024

Int. Res. J. multidiscip. Technovation

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This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings.

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P68-F Abnormalities of cortical neural synchronization mechanisms in patients with Alzheimer’s diseases dementia: An EEG study

Cited by 3 publications

References 0 publications

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes

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