Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes
Expert Systems with Applications: An International Journal
Comparative clustering analysis of bispectral index series of brain activity
Expert Systems with Applications: An International Journal
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Parallel Algorithm to Analyze the Brain Signals: Application on Epileptic Spikes
Journal of Medical Systems
DFAspike: A new computational proposition for efficient recognition of epileptic spike in EEG
Computers in Biology and Medicine
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
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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.