A Separable Bi-Pyramidal Feature Attention Network to Detect Alzheimer's Using Electroencephalographic Signals

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 15
• Main Authors: Kalambe, Sandesh, Karnati, Mohan, Seal, Ayan, Penhaker, Marek, Krejcar, Ondrej
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Alzheimer's disease; Alzheimer’s disease (AD) classification; Brain modeling; computer-aided diagnosis of AD; Datasets; Deep learning; Discrete wavelet transforms; electroencephalogram (EEG) signal measurement and classification; Electroencephalography; electroencephalography signal; Feature extraction; Feature maps; Industrial applications; Instruments; medical instrumentation for EEG analysis; Nearest neighbor methods; Neurological diseases; Signal processing; Support vector machines
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3565100

Signal categorization is crucial in many clinical areas, including the diagnosis of Alzheimer's disease (AD), a common neurological disorder marked by symptoms such as memory loss and speech difficulties. This study focuses on how to distinguish between Alzheimer's patients and healthy persons using electroencephalogram (EEG) signals, a noninvasive, low-cost diagnostic approach. We describe a novel separable bi-pyramidal feature attentive network (SBPFAN) that extracts multiscale deep attributes from 2-D images of 8-s EEG segments using separable and dilated convolutions (DCs). A feature attention block (FAB) is incorporated at each pyramid level to emphasize notable AD-related characteristics. After concatenating and processing the FAB feature maps through several dense layers, a softmax layer is employed for classification. Two datasets are used in three different experimental setups-subject-dependent, subject-independent, and cross-dataset-to estimate SBPFAN's performance. Experimental results demonstrate that SBPFAN is effective and holds significant potential for medical and industrial applications in AD detection.