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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Fiscon, Giulia Weitschek, Emanuel Cialini, Alessio Felici, Giovanni Bertolazzi, Paola De Salvo, Simona Bramanti, Alessia Bramanti, Placido De Cola, Maria Cristina |
| Abstract | Background Alzheimer’s Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. Methods In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods. Results By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. Conclusions Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia. |
| Related Links | https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-018-0613-y.pdf |
| Ending Page | 10 |
| Page Count | 10 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726947 |
| DOI | 10.1186/s12911-018-0613-y |
| Journal | BMC Medical Informatics and Decision Making |
| Issue Number | 1 |
| Volume Number | 18 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2018-05-31 |
| Access Restriction | Open |
| Subject Keyword | Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Alzheimer’s disease Feature extraction Electroencephalography signals Classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications Health Policy |
| Journal Impact Factor | 3.3/2023 |
| 5-Year Journal Impact Factor | 3.9/2023 |
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