Time-Frequency Methods for Diagnosing Alzheimer's Disease Using EEG

Bibina, V. C.; Chakraborty, Upasana; Lourde, R. Mary; Kumar, Ajit

doi:10.1145/3121138.3121183

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Specifically, 9 descriptive statistical features listed in Equation (3) are extracted from each coefficient by calling the corresponding built-in functions in Matlab. Therefore, the total number of features extracted form each segment n=19×4×9=684 according to Equation (4), where 19 denotes the number of channels, 4 indicates the number of frequency bands, and 9 means the number of descriptive statistical features.…”

Section: Results Of Spectral-temporal Feature Extractionmentioning

confidence: 99%

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An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

Yin

Cao

Siuly

et al. 2019

Int. J. Autom. Comput.

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Aiming to differentiate between MCI patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state EEG recordings. This framework firstly eliminates noise employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the Support Vector Machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-Nearest Neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision Tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delay cognitive decline and dementia onset.

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Section: Results Of Spectral-temporal Feature Extractionmentioning

confidence: 99%

“…The early detection of dementia would provide opportunities for early intervention and symptomatic treatments. Recent studies have demonstrated that AD has a pre-symptomatic phase that can last for years, known as mild cognitive impairment (MCI) [2,3,4,5] . Obviously, detecting MCI is essential and effective for potential patients.…”

Section: Introductionmentioning

confidence: 99%

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

Yin

Cao

Siuly

et al. 2019

Int. J. Autom. Comput.

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“…There are popular methods for preparing EEG data, such as frequency domain (Kulkarni & Bairagi, 2017), time domain (John et al, 2018), and time-frequency domain (Bibina et al, 2017). Method using frequency domain is the most common, and one of the most effective and standard approaches as the power spectrum represents the 'frequency value' of the signal or the distribution of signal power over frequency (Dressler et al, 2004) and this method is applied by Kulkarni and Bairagi (2017) in detecting AD.…”

Section: Electroencephalogram (Eeg) Neurofeedbackmentioning

confidence: 99%

A Game-based Psychotherapy Intervention Model for Memory Disorder: Model Validation Using EEG Neurofeedback Data

ChePa,

Laura Lim,

Yusoff

et al. 2023

Journal of Information and Communication Technology

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Game-based psychotherapy intervention is a promising alternative to non-pharmacological approaches in treating memory disorders. Nevertheless, the game-based approach is yet to be included systematically in existing intervention models for treating memorydisorders. Hence, this article discusses how a proposed gamebased psychotherapy intervention is developed and validated usingneurofeedback approach. The proposed model consists of nine exogenous and six instantaneous factors as the main components. Toensure its applicability, a validation procedure has been carried out through a series of psychotherapy experiments involving the elderly with memory disorder symptoms. Electroencephalogram (EEG) data captured from the experiments are thoroughly analysed to validate relationships among factors in the model. Experimental findings have proven that all relationships are successfully validated and supported except for the belief component with the cut-off point of 56.6%. The novelty of this study can be attributed to the integration of digital games and neurofeedback in psychotherapy for memory disorders. The model is believed to be a guideline in planning suitable cognitive training and rehabilitation for people with memory disorders towards improving the quality of the elderly life.

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“…Applications and comparisons of classification algorithms for recognition of Alzheimer's Disease by Lehmann et al [4] suggests the use of absolute and relative spectral power biomarkers calculated from recording of rested eyes closed EEG signals taken from healthy, mild and moderate cases of AD. It also suggests the use of algorithms like forest classification, SVM and neural networks for classification achieving accuracies as high as 91% for Severe AD vs Normal classifications.…”

Section: Reviewmentioning

confidence: 99%

Deep Neural Network Model for Automated Detection of Alzheimer’s Disease using EEG Signals

et al. 2022

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Our brain is our body’s control centre and is essential for proper functioning of the body. Alzheimer’s disease is a chronic neurodegenerative disease that affects the cerebral cortex of the brain and causes memory loss and loss of cognitive thinking. EEG (Electroencephalography) is a method of recording neurological electrical activity with electrodes. It was chosen as it is a simple, painless procedure. This paper suggests an automated and accurate algorithm for the detection of Alzheimer's Disease using EEG signals with a combination of Signal processing and Deep Learning Methods. Concepts like Butterworth filters, DWT, statistical parameters, Data Augmentation and CNN were used in order to achieve a classification algorithm with high accuracy. A total highest system accuracy of 97.61% was achieved.

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Time-Frequency Methods for Diagnosing Alzheimer's Disease Using EEG

Cited by 6 publications

References 21 publications

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

A Game-based Psychotherapy Intervention Model for Memory Disorder: Model Validation Using EEG Neurofeedback Data

Deep Neural Network Model for Automated Detection of Alzheimer’s Disease using EEG Signals

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