Browsing by Author "Alkan, Ahmet"

Now showing 1 - 6 of 6

Citation - WoS: 3
Citation - Scopus: 4
A novel mobile epilepsy warning system
(SPRINGER-VERLAG BERLIN, 2006) Ahmet Alkan; Yasar Guneri Sahin; Bekir Karlik; Alkan, Ahmet; Karlik, Bekir; Sahin, Yasar Guneri; A Sattar; BH Kang
This paper presents a new design of mobile epilepsy warning system for medical application in telemedical environment. Mobile Epilepsy Warning System (MEWS) consists of a wig with a cap equipped with sensors to get Electroencephalogram (EEG) signals a collector which is used for converting signals to data Global Positioning System (GPS) a Personal Digital Assistant (PDA) which has Global System for Mobile (GSM) module and execute Artificial Neural Network (ANN) software to test current patient EEG data with pre-learned data and a calling center for patient assistance or support. The system works as individual sensors obtain EEG signals from patient who has epilepsy and establishes a communication between the patient and Calling Center (CC) in case of an epileptic attack. MEWS learning process has artificial neural network classifier which consists of Multi Layered Perceptron (MLP) neural networks structure and back-propagation training algorithm.
Citation - WoS: 47
Citation - Scopus: 57
Applications of parametric spectral estimation methods on detection of power system harmonics
(Elsevier Science SA, 2008) Ahmet Serdar Yilmaz; Ahmet Alkan; Musa Hakan Asyali; Alkan, Ahmet; Asyali, Musa H.; Yilmaz, Ahmet S.
Harmonics are the major power quality problems in industrial and commercial power systems. Several methods for detection of power system harmonics have been investigated by engineers due to increasing harmonic pollution. Since the non-integer multiple harmonics (inter and sub-harmonics) become wide spread the importance of harmonic detection has increased for sensitive filtration. This paper suggests parametric spectral estimation methods for the detection of harmonics inter-harmonics and sub-harmonics. Yule Walker Burg Covariance and Modified Covariance methods are applied to generate cases. Not only integer multiple harmonics but also non-integer multiple harmonics are successfully determined in the computer simulations. Further performances of proposed methods are compared with each other in terms of frequency resolution. © 2007 Elsevier B.V. All rights reserved. © 2008 Elsevier B.V. All rights reserved.
Citation - WoS: 45
Citation - Scopus: 50
Classification of EEG recordings by using fast independent component analysis and artificial neural network
(Springer, 2008) Yücel Koçyig̃it; Ahmet Alkan; Halil Erol; Alkan, Ahmet; Kocyigit, Yucel; Erol, Halil
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. © 2007 Springer Science+Business Media LLC. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.
Citation - Scopus: 71
Comparison of AR and Welch methods in epileptic seizure detection
(2006) 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.
Citation - WoS: 44
Citation - Scopus: 62
Frequency domain analysis of power system transients using Welch and Yule-Walker AR methods
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ahmet Alkan; Ahmet S. Yimaz; Alkan, Ahmet; Yimaz, Ahmet S.; Yilmaz, Ahmet S.
In this study power quality (PQ) signals are analyzed by using Welch (non-parametric) and autoregressive (parametric) spectral estimation methods. The parameters of the autoregressive (AR) model were estimated by using the Yule-Walker method. PQ spectra were then used to compare the applied spectral estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the obtained power spectra were examined in order to detect power system transients. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the AR method with that of the Welch technique are given. The results demonstrate superior performance of the AR method over the Welch method. (c) 2007 Elsevier Ltd. All rights reserved.
Citation - Scopus: 45
Long term energy consumption forecasting using genetic programming
(Association for Scientific Research, 2008) Ahmet S. YILMAZ; Ahmet Alkan; KORHAN KARABULUT; Alkan, Ahmet; Yilmaz, Ahmet S.; Karabulut, Korhan
Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information such as climate and previous load demand data. In this paper a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. The empirical results demonstrate successful load forecast with a low error rate.