Role of EEG as biomarker in the early detection and classification of dementia
The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these degenerative disorders in the early...
| Main Authors: | , , , , , |
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| Format: | Article |
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Hindawi Publishing Corporation
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35292/ |
| _version_ | 1848848013951238144 |
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| author | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Chellappan, Kalaivani Islam, Md. Shabiul Escudero, Javier |
| author_facet | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Chellappan, Kalaivani Islam, Md. Shabiul Escudero, Javier |
| author_sort | Al-Qazzaz, Noor Kamal |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these degenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. |
| first_indexed | 2025-11-15T09:27:45Z |
| format | Article |
| id | upm-35292 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:27:45Z |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-352922015-12-31T04:59:41Z http://psasir.upm.edu.my/id/eprint/35292/ Role of EEG as biomarker in the early detection and classification of dementia Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Chellappan, Kalaivani Islam, Md. Shabiul Escudero, Javier The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these degenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. Hindawi Publishing Corporation 2014 Article PeerReviewed Al-Qazzaz, Noor Kamal and Md. Ali, Sawal Hamid and Ahmad, Siti Anom and Chellappan, Kalaivani and Islam, Md. Shabiul and Escudero, Javier (2014) Role of EEG as biomarker in the early detection and classification of dementia. The Scientific World Journal, 2014. art. no. 906038. pp. 1-16. ISSN 2356-6140; ESSN: 1537-744X http://www.hindawi.com/journals/tswj/2014/906038/abs/ 10.1155/2014/906038 |
| spellingShingle | Al-Qazzaz, Noor Kamal Md. Ali, Sawal Hamid Ahmad, Siti Anom Chellappan, Kalaivani Islam, Md. Shabiul Escudero, Javier Role of EEG as biomarker in the early detection and classification of dementia |
| title | Role of EEG as biomarker in the early detection and classification of dementia |
| title_full | Role of EEG as biomarker in the early detection and classification of dementia |
| title_fullStr | Role of EEG as biomarker in the early detection and classification of dementia |
| title_full_unstemmed | Role of EEG as biomarker in the early detection and classification of dementia |
| title_short | Role of EEG as biomarker in the early detection and classification of dementia |
| title_sort | role of eeg as biomarker in the early detection and classification of dementia |
| url | http://psasir.upm.edu.my/id/eprint/35292/ http://psasir.upm.edu.my/id/eprint/35292/ http://psasir.upm.edu.my/id/eprint/35292/ |