A Review of Automated Techniques for Assisting the Early Detection of Alzheimer’s Disease with a Focus on EEG

Pérez-Valero, Eduardo; López-Gordo, M. A.; Morillas, Christian A.; Pelayo, Francisco J.; Vaquero-Blasco, Miguel A.

doi:10.3233/jad-201455

Cited by 21 publications

(8 citation statements)

References 91 publications

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“…Furthermore, AD patients have the least difficulty and the most consistency. The complexity of EEG signals declines with disease progression, as predicted, particularly for comparing HC issues to Alzheimer's disease patients [91].…”

Section: The Signal Complexity Analysis In Eegmentioning

confidence: 88%

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: The Signal Complexity Analysis In Eegmentioning

confidence: 88%

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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“…In recent years, there has been a substantial increase in the adoption of non-invasive devices for measuring brain activity, such as electroencephalography (EEG) ( Minguillon et al, 2017 ; He et al, 2023 ). The non-invasiveness and high temporal resolution make it a convenient and essential tool for research and clinical diagnosis of neurological diseases ( Perez-Valero et al, 2021 ). EEG is measured by placing electrodes on the scalp and it provides indispensable insights into the synchronous activity of populations of cortical neurons ( David et al, 2002 ).…”

Section: Introductionmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

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Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

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“…Among modern neuroimaging techniques, EEG has advantages such as inexpensive, non-invasive, portability, relative simplicity, less recording [1]. Because of these advantages, automated methods based on EEG signal analysis combined with deep learning and machine learning have become an important research topic to support clinicians in the difficult process of early AD diagnosis [14]. Reduced complexity in EEG signals has been described as a hallmark of AD progression.…”

Section: Introductionmentioning

confidence: 99%

Welch Spectral Analysis and Deep Learning Approach for Diagnosing Alzheimer's Disease from Resting-State EEG Recordings

Göker¹

2023

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Alzheimer's disease (AD) is a serious and progressive neuronal disease that damages brain cells, resulting in loss of cognitive function and memory. Early diagnosis is crucial for medical intervention to prevent brain damage and preserve daily functioning for longer. In this study, a deep learning approach was proposed for early diagnosis of AD from electroencephalography (EEG) recordings at resting-state. The dataset contains EEG recordings of 24 healthy individuals and 24 Alzheimer's patients. To extract the features from the EEG recordings, the power spectral densities of the frequencies between 1-49 Hz were calculated using the welch spectral analysis method. Using extracted features, the performances of random forest (RF), k-nearest neighbor (kNN), support vector machine (SVM), and bidirectional long-short term memory algorithms were compared. In addition, under different resting state conditions (open eyes; closed eyes; open eyes and closed eyes), the effectiveness of EEG signals was analyzed. As a result of the experiments, the bidirectional long-short term memory algorithm had the highest performance. The algorithm achieved promising performance with 98.85% accuracy, 0.986 recall, 0.990 precision, 0.990 specificity, 0.988 f1-score, and 0.977 Matthews correlation coefficient. The combination of the welch spectral analysis and the bidirectional long-short term memory deep learning approach can be used to accurately and effectively distinguish AD and HC groups from resting-state EEG recordings. More accuracy was achieved in this study compared to investigations using cutting-edge technology.

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A Review of Automated Techniques for Assisting the Early Detection of Alzheimer’s Disease with a Focus on EEG

Cited by 21 publications

References 91 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

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Welch Spectral Analysis and Deep Learning Approach for Diagnosing Alzheimer's Disease from Resting-State EEG Recordings

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