Multitaper ve Topluluk Öğrenme Yöntemlerinin Kullanılarak Elektroensefalografi (EEG) Sinyallerinden Alzheimer Hastalığının Tespiti

Alzheimer hastalığı karmaşık bir beyin hastalığıdır, aynı zamanda sosyal ve entelektüel yeteneklerde bozulmaya yol açan demansın en yaygın şeklidir. Hastalık sadece basit bir unutkanlıkla kendini gösterir, hastalık ilerledikçe hasta son olayları unutur, ailesini ve yakın çevresini tanıyamaz, son aşamada bakıma muhtaç hale gelir. Bu nedenle erken teşhis, beyin hasarını azaltmak ve günlük işleyişi daha uzun süre korumak için tıbbi müdahalede önemli bir rol oynamaktadır. Bu çalışmada, multitaper ve topluluk öğrenme yöntemleri kullanılarak, EEG sinyallerinden Alzheimer hastalığının tespitinin yapılması amaçlanmıştır. Veriseti 24 sağlıklı bireyden ve 24 Alzheimer hastasından kaydedilen EEG sinyallerinden oluşmaktadır. EEG sinyallerinin 1-49 Hz arasındaki frekanslarının güç spektral yoğunluğu (PSD) multitaper yöntemi kullanılarak hesaplanarak, 49 öznitelik çıkarıldı. Daha sonra AdaboostM1, Total Boost, Gentle Boost, Logit Boost, Robust Boost ve Bagging topluluk öğrenme algoritmalarının performansları karşılaştırıldı. Deneyler sonucunda, Logit Boost algoritması en yüksek performansa sahipti. Algoritma, %93,04 doğruluk, %93,09 f1-skor, %92,75 duyarlılık, %93,43 kesinlik ve %93,33 özgüllük ile umut verici bir performans elde etti.

Anahtar Kelimeler:

Topluluk öğrenme, Sinyal işleme, EEG, Multitaper, Alzheimer hastalığı

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 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.

Keywords:

Ensemble learning, Signal processing, EEG, Multitaper, Alzheimer's disease,

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