A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment
Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe deme...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402402396X |
| _version_ | 1797267675986001920 |
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| author | Alireza Faghfouri Vahid Shalchyan Hamza Ghazanfar Toor Imran Amjad Imran Khan Niazi |
| author_facet | Alireza Faghfouri Vahid Shalchyan Hamza Ghazanfar Toor Imran Amjad Imran Khan Niazi |
| author_sort | Alireza Faghfouri |
| collection | DOAJ |
| description | Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals. |
| first_indexed | 2024-03-07T22:54:21Z |
| format | Article |
| id | doaj.art-7b48a9d37ff741cc9ef9244907c96096 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:20:22Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-7b48a9d37ff741cc9ef9244907c960962024-03-09T09:28:06ZengElsevierHeliyon2405-84402024-02-01104e26365A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairmentAlireza Faghfouri0Vahid Shalchyan1Hamza Ghazanfar Toor2Imran Amjad3Imran Khan Niazi4Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, IranNeuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran; Corresponding author.Riphah International University, Islamabad, PakistanRiphah International University, Islamabad, Pakistan; Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New ZealandCentre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand; Faculty of Health & Environmental Sciences, Health & Rehabilitation Research Institute, AUT University, Auckland, New Zealand; Department of Health Science and Technology, Aalborg University, Aalborg, DenmarkMild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.http://www.sciencedirect.com/science/article/pii/S240584402402396XMild cognitive impairment (MCI)Tensor decompositionElectroencephalogram (EEG)Alzheimer's diseaseParallel factor analysis (PARAFAC) |
| spellingShingle | Alireza Faghfouri Vahid Shalchyan Hamza Ghazanfar Toor Imran Amjad Imran Khan Niazi A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment Heliyon Mild cognitive impairment (MCI) Tensor decomposition Electroencephalogram (EEG) Alzheimer's disease Parallel factor analysis (PARAFAC) |
| title | A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment |
| title_full | A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment |
| title_fullStr | A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment |
| title_full_unstemmed | A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment |
| title_short | A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment |
| title_sort | tensor decomposition scheme for eeg based diagnosis of mild cognitive impairment |
| topic | Mild cognitive impairment (MCI) Tensor decomposition Electroencephalogram (EEG) Alzheimer's disease Parallel factor analysis (PARAFAC) |
| url | http://www.sciencedirect.com/science/article/pii/S240584402402396X |
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