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Öğe AR spectral analysis of EEG signals by using maximum likelihood estimation(Pergamon-Elsevier Science Ltd, 2001) Güler, I; Kiymik, MK; Akin, M; Alkan, AIn this study, EEG signals were analyzed using autoregressive (AR) method. Parameters in AR method were realized by using maximum likelihood estimation (MLE). Results were compared with fast Fourier transform (FFT) method. It is observed that AR method gives better results in the analysis of EEG signals. On the other hand, the results have also showed that AR method can also be used for some other researches and diagnosis of diseases. (C) 2001 Elsevier Science Ltd. All rights reserved.Öğe Automatic recognition of alertness level by using wavelet transform and artificial neural network(Elsevier, 2004) Kiymik, MK; Akin, M; Subasi, AWe propose a novel method for automatic recognition of alertness level from full spectrum electroencephalogram (EEG) recordings. This procedure uses power spectral density (PSD) of discrete wavelet transform (DWT) of full spectrum EEG as an input to an artificial neural network (ANN) with three discrete outputs: alert, drowsy and sleep. The error back propagation neural network is selected as a classifier to discriminate the alertness level of a subject. EEG signals were obtained from 30 healthy subjects. The group consisted of 14 females and 16 males with ages ranging from 18 to 65 years and a mean age of 33.5 years, and a body mass index (BMI) of 32.4 +/- 7.3 kg/m(2). Alertness level and classification properties of ANN were tested using the data recorded in 12 healthy subjects, whereby the EEG recordings were not used been used to train the ANN. The statistics were used as a measure of potential applicability of the ANN. The accuracy of the ANN was 96 3% alert, 95 +/- 4% drowsy and 94 +/- 5% sleep. The results suggest that the automatic recognition algorithm is applicable for distinguishing between alert, drowsy and sleep state in recordings that have not been used for the training. (C) 2004 Elsevier B.V. All rights reserved.Öğe Automatic recognition of vigilance state by using a wavelet-based artificial neural network(Springer London Ltd, 2005) Subasi, A; Kiymik, MK; Akin, M; Erogul, OIn this study, 5-s long sequences of full-spectrum electroencephalogram (EEG) recordings were used for classifying alert versus drowsy states in an arbitrary subject. EEG signals were obtained from 30 healthy subjects and the results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron (MLP), was used for the classification of EEG signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to discriminate the alertness level of the subject. In order to determine the MLPNN inputs, spectral analysis of EEG signals was performed using the discrete wavelet transform (DWT) technique. The MLPNN was trained, cross-validated, and tested with training, cross-validation, and testing sets, respectively. The correct classification rate was 93.3% alert, 96.6% drowsy, and 90% sleep. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective for discriminating the vigilance state of the subject.Öğe Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application(Pergamon-Elsevier Science Ltd, 2005) Kiymik, MK; Güler, I; Dizibüyük, A; Akin, MElectroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings. (c) 2004 Elsevier Ltd. All rights reserved.Öğe A new approach for diagnosing epilepsy by using wavelet transform and neural networks(Ieee, 2001) Akin, M; Arserim, MA; Kiymik, MK; Turkoglu, IToday, epilepsy keeps its importance as a major brain disorder. However, although some devices such as magnetic resonance (MR), brain tomography (BT) are used to diagnose the structural disorders of brain, for observing some special illnesses especially such as epilepsy, EEG is routinely used for observing the epileptic seizures, in neurology clinics. In our study, we aimed to classify the EEG signals and diagnose the epileptic seizures directly by using wavelet transform and an artificial neural network model. EEG signals are separated into delta, theta, alpha, and beta spectral components by using wavelet transform. These spectral components are applied to the inputs of the neural network. Then, neural network is trained to give three outputs to signify the health situation of the patients Keywords: wavelet, neural network, epilepsy, EEG.
