dc.contributor.author | Shoeibi, Afshin | |
dc.contributor.author | Gorriz Sáez, Juan Manuel | |
dc.date.accessioned | 2022-01-10T09:12:27Z | |
dc.date.available | 2022-01-10T09:12:27Z | |
dc.date.issued | 2021-11-25 | |
dc.identifier.citation | Shoeibi A... [et al.]. (2021) Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front. Neuroinform. 15:777977. doi: [10.3389/fninf.2021.777977] | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/72265 | |
dc.description | This work was supported by the MCIN/AEI/10. 13039/501100011033/and FEDER "Una manera de hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20, and P20-00525 projects. | es_ES |
dc.description.abstract | Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific
chemicals in the brain, the function of some brain regions is out of balance, leading to
the lack of coordination between thoughts, actions, and emotions. This study provides
various intelligent deep learning (DL)-based methods for automated SZ diagnosis via
electroencephalography (EEG) signals. The obtained results are compared with those
of conventional intelligent methods. To implement the proposed methods, the dataset
of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First,
EEG signals were divided into 25 s time frames and then were normalized by z-score
or norm L2. In the classification step, two different approaches were considered for SZ
diagnosis via EEG signals. In this step, the classification of EEG signals was first carried
out by conventional machine learning methods, e.g., support vector machine, k-nearest
neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and
bagging. Various proposed DL models, namely, long short-term memories (LSTMs),
one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in
the following. In this step, the DL models were implemented and compared with different
activation functions. Among the proposed DL models, the CNN-LSTM architecture has
had the best performance. In this architecture, the ReLU activation function with the
z-score and L2-combined normalization was used. The proposed CNN-LSTM model
has achieved an accuracy percentage of 99.25%, better than the results of most former
studies in this field. It is worth mentioning that to perform all simulations, the k-fold
cross-validation method with k = 5 has been used. | es_ES |
dc.description.sponsorship | FEDER "Una manera de hacer Europa" RTI2018-098913-B100 | es_ES |
dc.description.sponsorship | Junta de Andalucia
European Commission CV20-45250
A-TIC-080-UGR18
B-TIC-586-UGR20
P20-00525
MCIN/AEI/10. 13039/501100011033/ | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Frontiers | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Schizophrenia | es_ES |
dc.subject | Neuroimaging | es_ES |
dc.subject | EEG Signals | es_ES |
dc.subject | Diagnosis | es_ES |
dc.subject | Deep learning | es_ES |
dc.title | Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3389/fninf.2021.777977 | |
dc.type.hasVersion | VoR | es_ES |