EEG features extraction using neuro-fuzzy systems and shift-invariant wavelet transforms for epileptic seizures diagnosing

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.