The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings
Abstract
Abstract A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing noninvasive scalp EEG recordings. The methodology here proposed consists of evaluating the power spectral density (PSD) of the 19-channels EEG traces and representing the related spectral profiles into 2-d gray scale images (PSD-images). A customized Convolutional Neural Network with one processing module of convolution, Rectified Linear Units (ReLu) and pooling layer (CNN1) is designed to extract from PSD-images some suitable features and to perform the corresponding two and three-ways classification tasks. The resulting CNN is shown to provide better classification performance when compared to more conventional learning machines; indeed, it achieves an average accuracy of 89.8% in binary classification and of 83.3% in three-ways classification. These results encourage the use of deep processing systems (here, an engineered first stage, namely the PSD-image extraction, and a second or multiple CNN stage) in challenging clinical frameworks.
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Cited by
A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images
Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., Maqsood, M., & Yaqub, M. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.01.002 · Cited by 292
Deep learning for Alzheimer's disease diagnosis: A survey
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Huggins, C. J., Escudero, J., Parra, M. A., Scally, B., Anghinah, R., Vitória Lacerda De Araújo, A., Basile, L. F., & Abasolo, D. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac05d8 · Cited by 105
Data leakage in deep learning studies of translational EEG
Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1373515 · Cited by 103
EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions
Tzimourta, K. D., Giannakeas, N., Tzallas, A. T., Astrakas, L. G., Afrantou, T., Ioannidis, P., Grigoriadis, N., Angelidis, P., Tsalikakis, D. G., & Tsipouras, M. G. (2019) · Brain Sciences · doi:10.3390/brainsci9040081 · Cited by 79
Metadata
- Authors
- Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., & Morabito, F. C.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 323
- Pages
- 96-107
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2894895904
- Cited by
- 294
- References
- 38
- FWCI
- 14.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., & Morabito, F. C. (2019). A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.09.071
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