Analysis Electroencephalogram Signals Using ANFIS and Periodogram Techniques

In this paper the applications the Adaptative Neuro Fuzzy Inference Systems (ANFIS), the Empirical Mode Decomposition (EMD) and Discrete Wavelet Distribution (DWT) are used.  An electroencephalogram (EEG) is a diagnostic test which measures the electrical activity of the brain using highly sensitive recording equipment attached to the scalp by fine electrodes. An EEG recording is often affected with noises. These noises strongly affect the visual analysis of EEG. To overcome this problem the denoising techniques as ANFIS, EMD and DWT are applied.  The efficiency of the ANFIS, EMD and DWT to remove the noises was evaluated by several standard metrics between filter EEG output and clean original signal.  The results obtained show that the ANFIS outperformed other denoising techniques in terms of localization of the components of the abnormal EEG signal. Due to non-stationary nature of the EEG signal, the uses of time-frequency techniques are inevitable. The parametric time-frequency technique used is Periodogram (PE). The EEG signals used are normal and abnormal; the abnormal signals are obtained from the patient that has the sleep-disordered breathing (SDB) and the patient that has the sleep movement disorders (periodic leg movements or PLM).  The PE technique shows its higher performance at the level of resolution and deleting any interference-terms over other non-parametric time-frequency techniques given in the scientific literature. This study demonstrates that the combination of ANFIS and the PE techniques are a good issue in the in biomedicine. For experimental study we have used the MIT/BIH arrhythmia database. Simulations were carried out in MATLAB environment.