DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS
Alzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize...
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| Format: | Article |
| Language: | English |
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Bursa Uludag University
2023-04-01
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| Series: | Uludağ University Journal of The Faculty of Engineering |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/2532518 |
| _version_ | 1797777613723271168 |
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| author | Hanife Göker |
| author_facet | Hanife Göker |
| author_sort | Hanife Göker |
| collection | DOAJ |
| description | Alzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize his family members and close environment, and becomes in need of care in the last stage. Early detection is therefore crucial for medical intervention to prevent brain injury and prolong everyday functioning. In this study is aimed to detection of Alzheimer’s disease from EEG signals using the multitaper and ensemble learning methods. The dataset comprises of 24 healthy people and 24 Alzheimer's patients' EEG signals. 49 features were extracted by calculating the power spectral density (PSD) of the frequencies of the EEG signals between 1-49 Hz using the multitaper method. Then, the performances of AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost, and Bagging ensemble learning algorithms were compared. As a result of experiments, the Logit Boost algorithm has the highest performance. The algorithm has achieved a promising performance of 93.04% accuracy, 93.09% f1-score, 92.75% sensitivity, 93.43% precision, and 93.33% specificity. |
| first_indexed | 2024-03-12T23:06:21Z |
| format | Article |
| id | doaj.art-2088500f50d04edd892e37f6a2417c75 |
| institution | Directory Open Access Journal |
| issn | 2148-4155 |
| language | English |
| last_indexed | 2024-03-12T23:06:21Z |
| publishDate | 2023-04-01 |
| publisher | Bursa Uludag University |
| record_format | Article |
| series | Uludağ University Journal of The Faculty of Engineering |
| spelling | doaj.art-2088500f50d04edd892e37f6a2417c752023-07-18T15:47:11ZengBursa Uludag UniversityUludağ University Journal of The Faculty of Engineering2148-41552023-04-0128114115210.17482/uumfd.11423451779DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODSHanife Göker0KÜTAHYA DUMLUPINAR ÜNİVERSİTESİAlzheimer's disease is a complex brain disease and is also the most common form of dementia that leads to impaired social and intellectual abilities. The disease only manifests itself with a simple forgetfulness, as the disease progresses, the patient forgets the recent events, cannot recognize his family members and close environment, and becomes in need of care in the last stage. Early detection is therefore crucial for medical intervention to prevent brain injury and prolong everyday functioning. In this study is aimed to detection of Alzheimer’s disease from EEG signals using the multitaper and ensemble learning methods. The dataset comprises of 24 healthy people and 24 Alzheimer's patients' EEG signals. 49 features were extracted by calculating the power spectral density (PSD) of the frequencies of the EEG signals between 1-49 Hz using the multitaper method. Then, the performances of AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost, and Bagging ensemble learning algorithms were compared. As a result of experiments, the Logit Boost algorithm has the highest performance. The algorithm has achieved a promising performance of 93.04% accuracy, 93.09% f1-score, 92.75% sensitivity, 93.43% precision, and 93.33% specificity.https://dergipark.org.tr/tr/download/article-file/2532518ensemble learningsignal processingeegmultitaperalzheimer's diseasetopluluk öğrenmesinyal işlemeeegmultitaperalzheimer hastalığı |
| spellingShingle | Hanife Göker DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS Uludağ University Journal of The Faculty of Engineering ensemble learning signal processing eeg multitaper alzheimer's disease topluluk öğrenme sinyal işleme eeg multitaper alzheimer hastalığı |
| title | DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS |
| title_full | DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS |
| title_fullStr | DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS |
| title_full_unstemmed | DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS |
| title_short | DETECTION OF ALZHEIMER'S DISEASE FROM ELECTROENCEPHALOGRAPHY (EEG) SIGNALS USING MULTITAPER AND ENSEMBLE LEARNING METHODS |
| title_sort | detection of alzheimer s disease from electroencephalography eeg signals using multitaper and ensemble learning methods |
| topic | ensemble learning signal processing eeg multitaper alzheimer's disease topluluk öğrenme sinyal işleme eeg multitaper alzheimer hastalığı |
| url | https://dergipark.org.tr/tr/download/article-file/2532518 |
| work_keys_str_mv | AT hanifegoker detectionofalzheimersdiseasefromelectroencephalographyeegsignalsusingmultitaperandensemblelearningmethods |