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LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection.
Kachare, Pramod; Puri, Digambar; Sangle, Sandeep B; Al-Shourbaji, Ibrahim; Jabbari, Abdoh; Kirner, Raimund; Alameen, Abdalla; Migdady, Hazem; Abualigah, Laith.
Afiliación
  • Kachare P; Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India.
  • Puri D; Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India.
  • Sangle SB; Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India.
  • Al-Shourbaji I; Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia.
  • Jabbari A; Department of Computer Science, University of Hertfordshire, Hatfield, UK.
  • Kirner R; Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia.
  • Alameen A; Department of Computer Science, University of Hertfordshire, Hatfield, UK.
  • Migdady H; Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir, 11991, Saudi Arabia.
  • Abualigah L; CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman.
Phys Eng Sci Med ; 47(3): 1037-1050, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38862778
ABSTRACT
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Electroencefalografía / Enfermedad de Alzheimer Límite: Humans Idioma: En Revista: Phys Eng Sci Med Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Electroencefalografía / Enfermedad de Alzheimer Límite: Humans Idioma: En Revista: Phys Eng Sci Med Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza