PubMed İndeksli Yayınlar Koleksiyonu

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Classification of EEG recordings by using fast independent component analysis and artificial neural network
(SPRINGER, 2007-10-26) Yucel Kocyigit; Ahmet Alkan; Halil Erol
Since there is no definite decisive factor evaluated by the experts visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98% and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate this application brings objectivity to the evaluation of EEG signals.
Citation - Scopus: 71
Comparison of AR and Welch methods in epileptic seizure detection
(2006-11-03) Ahmet Alkan; Mahmut Kemal Kıymık; Alkan, Ahmet; Kiymik, M. Kemal
Brain is one of the most critical organs of the body. Synchronous neuronal discharges generate rhythmic potential fluctuations which can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean electrical activity measured at different sites of the head. EEG patterns correlated with normal functions and diseases of the central nervous system. In this study EEG signals were analyzed by using autoregressive (parametric) and Welch (non-parametric) spectral estimation methods. The parameters of autoregressive (AR) method were estimated by using Yule-Walker covariance and modified covariance methods. EEG spectra were then used to compare the applied estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the EEG power spectra were examined in order to epileptic seizures detection. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of Welch technique were given. The results demonstrate consistently superior performance of the covariance methods over Yule-Walker AR and Welch methods. © 2006 Springer Science+Business Media Inc. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.

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