| Autor: |
Jose Luis Rodríguez-Sotelo, Alejandro Osorio-Forero, Alejandro Jiménez-Rodríguez, David Cuesta-Frau, Eva Cirugeda-Roldán, Diego Peluffo |
| Jazyk: |
angličtina |
| Rok vydání: |
2014 |
| Předmět: |
|
| Zdroj: |
Entropy, Vol 16, Iss 12, Pp 6573-6589 (2014) |
| Druh dokumentu: |
article |
| ISSN: |
1099-4300 |
| DOI: |
10.3390/e16126573 |
| Popis: |
Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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