Şen, Sena YağmurCura, O.K.Akan, Aydın2023-12-262023-12-2620239798350306590https://doi.org/10.1109/ASYU58738.2023.10296777https://hdl.handle.net/20.500.14365/50332023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- 194153Dementia is a prevalent neurological disorder that impairs cognitive functions and significantly diminishes the quality of life. In this research, a deep learning method is introduced for detecting and monitoring Alzheimer's Dementia (AD) by analyzing Electroencephalography (EEG) signals. To accomplish this, a signal decomposition technique known as Intrinsic Time Scale Decomposition (ITD) is employed to classify EEG segments obtained from both AD patients and control subjects. The analysis specifically concentrates on 5-second EEG segments, utilizing ITD to extract Proper Rotation Components (PRCs) from these segments. The PRCs are subsequently transformed into Time-Frequency (TF) images using the Short-Time Fourier Transform (STFT) spectrogram. These TF images serve as training data for a 2-Dimensional Convolutional Neural Network (2D CNN). The proposed approach is compared with the classification of the spectrogram of 5-second EEG segments using the same CNN architecture. The experimental results conclusively demonstrate the superior classification performance of the ITD-based approach when compared to the utilization of raw EEG signals. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessAlzehimer's Dementia (AD)ClassificationCNNsDeep LearningElectroencephalography (EEG)Intrinsic Time Scale Decomposition (ITD)Short-Time Fourier Transform (STFT)SpectrogramBiomedical signal processingConvolutional neural networksDeep learningElectrophysiologyImage classificationLearning systemsNeurodegenerative diseasesNeurophysiologySpectrographsAlzehimer dementiaDeep learningElectroencephalographyIntrinsic time scale decompositionIntrinsic time-scale decompositionsRotation componentsShort time Fourier transformsShort-time fourier transformSpectrogramsTime-frequency imagesElectroencephalographyConference Object10.1109/ASYU58738.2023.102967772-s2.0-85178310455