Title :
EEG features extraction using neuro-fuzzy systems and shift-invariant wavelet transforms for epileptic seizures diagnosing
Author :
Akhbardeh, A. ; Farrokhi, M. ; Tehrani, A. Vahabian
Author_Institution :
Dept. of Electr. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran
Abstract :
Electro-encephalogram spikes classification and latency computing is one of the important tools in epilepsy diagnosing. However, overlapped spikes cause complexity in problem solving. We use neuro-fuzzy systems and shift-invariant wavelet transforms to solve this problem. It has been shown that our suggested procedures have high-resolution and are able to classify and perform latency computing of overlapped spikes.
Keywords :
diseases; electroencephalography; feature extraction; fuzzy neural nets; medical signal processing; signal classification; signal resolution; wavelet transforms; EEG features extraction; electroencephalogram spikes classification; epileptic seizures diagnosing; high signal resolution; latency computing; neuro-fuzzy systems; shift-invariant wavelet transforms; Artificial neural networks; Data mining; Delay; Electroencephalography; Epilepsy; Feature extraction; Fuzzy neural networks; Fuzzy systems; Neurons; Wavelet transforms; Classification; EEG Spike; Latency; Neuro-Fuzzy Systems; Shift-Invariant Wavelet Transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
DOI :
10.1109/IEMBS.2004.1403203
