A study on fuzzy C-means clustering-based systems in automatic spike detection
| Autor: | Mehmet Kuntalp, Z. Hilal İnan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Artificial neural network
Computer science business.industry Correlation clustering Contrast (statistics) Health Informatics Pattern recognition Electroencephalography computer.software_genre Fuzzy logic Sensitivity and Specificity Computer Science Applications Fuzzy Logic Cluster Analysis Humans Spike (software development) Artificial intelligence Data mining business Cluster analysis computer Algorithms |
| Popis: | In this study, different systems based on the fuzzy C-means (FCM) clustering algorithm are utilized for the detection of epileptic spikes in electroencephalogram (EEG) records. The systems are constructed as either single or two-stages. In contrast to single-stage systems, the two-stage system comprises a pre-classifier stage realized by a neural network. The FCM based two-stage system is also compared to a similar system implemented using the K-means clustering algorithm. The results imply that an FCM based two-stage system should be preferred as the spike detection System. (c) 2006 Elsevier Ltd. All rights reserved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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