dc.contributor.author | Puri, Digambar V. | |
dc.contributor.author | Kachare, Pramod H. | |
dc.contributor.author | Sangle, Sandeep B. | |
dc.contributor.author | Kirner, Raimund | |
dc.contributor.author | Jabbari, Abdoh | |
dc.contributor.author | Al-Shourbaji, Ibrahim | |
dc.contributor.author | Abdalraheem, Mohammed | |
dc.contributor.author | Alameen, Abdalla | |
dc.date.accessioned | 2024-09-06T11:00:01Z | |
dc.date.available | 2024-09-06T11:00:01Z | |
dc.date.issued | 2024-07-30 | |
dc.identifier.citation | Puri , D V , Kachare , P H , Sangle , S B , Kirner , R , Jabbari , A , Al-Shourbaji , I , Abdalraheem , M & Alameen , A 2024 , ' LEADNet: Detection of Alzheimer’s Disease using Spatiotemporal EEG Analysis and Low-Complexity CNN ' , IEEE Access , vol. 12 , pp. 113888-113897 . https://doi.org/10.1109/access.2024.3435768 | |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | http://hdl.handle.net/2299/28134 | |
dc.description | © 2024 The Author(s). This is an open access article under the Creative Commons Attribution-Non Commercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.description.abstract | Clinical methods for dementia detection are expensive and prone to human errors. Despite various computer-aided methods using electroencephalography (EEG) signals and artificial intelligence, a reliable detection of Alzheimer’s disease (AD) remains a challenge. The existing EEG-based machine learning models have limited performance or high computation complexity. Hence, there is a need for an optimal deep learning model for the detection of AD. This paper proposes a low-complexity EEG-based AD detection CNN called LEADNet to generate disease-specific features. LEADNet employs spatiotemporal EEG signals as input, two convolution layers for feature generation, a max-pooling layer for asymmetric spatiotemporal redundancy reduction, two fully-connected layers for nonlinear feature transformation and selection, and a softmax layer for disease probability prediction. Different quantitative measures are calculated using an open-source AD dataset to compare LEADNet and four pre-trained CNN models. The results show that the lightweight architecture of LEADNet has at least a 150-fold reduction in network parameters and the highest testing accuracy of 99.24% compared to pre-trained models. The investigation of individual layers of LEADNet showed successive improvements in feature transformation and selection for detecting AD subjects. A comparison with the state-of-the-art AD detection models showed that the highest accuracy, sensitivity, and specificity were achieved by the LEADNet model. | en |
dc.format.extent | 10 | |
dc.format.extent | 1642218 | |
dc.language.iso | eng | |
dc.relation.ispartof | IEEE Access | |
dc.subject | electroencephalogram | |
dc.subject | pre-trained models | |
dc.subject | Alzheimer’s disease | |
dc.subject | convolutional neural network | |
dc.subject | Artificial Intelligence | |
dc.subject | Health Informatics | |
dc.title | LEADNet: Detection of Alzheimer’s Disease using Spatiotemporal EEG Analysis and Low-Complexity CNN | en |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Computer Science | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Cybersecurity and Computing Systems | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | 10.1109/access.2024.3435768 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |