Detection of K-complexes in sleep EEG with support vector machines
| Autor: | Tugce Kantar, Aykut Erdamar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Brain activity and meditation Computer science business.industry Speech recognition 0206 medical engineering Pattern recognition 010103 numerical & computational mathematics 02 engineering and technology Electroencephalography 020601 biomedical engineering 01 natural sciences Support vector machine Sleep electroencephalography medicine Night sleep Sleep (system call) Artificial intelligence Sensitivity (control systems) 0101 mathematics business Sleep eeg |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2017.7960311 |
| Popis: | Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83 % and 85.29%, respectively. |
| Databáze: | OpenAIRE |
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