M. Kemal Kiymik

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In this study, in order to analyze the EEG signal, the conventional and modern spectral methods were investigated. Interpretation and performance of these methods were detected for clinical applications. For this purpose EEG data obtained from different persons were processed by PC computer using periodogram and AR model algorithms. Periodogram and AR(More)
Real time sonogram outputs of autoregressive (AR) and Fast Fourier Transform (FFT) spectral analysis of 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. Data obtained from coronary, renal, iliac, digital and mesenteric arteries were processed using AR- and FFT-based spectral analysis techniques and interpretable sonograms were constructed. In(More)
We 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(More)
Electroencephalography (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(More)
The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time–frequency methods and(More)
Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial(More)
In 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(More)
Tricuspid and mitral valve flow area was determined from an apical four-chamber view. Doppler signals were recorded from normal subjects and patients with tricuspid and mitral valve stenosis by using a pulsed Doppler unit. The location of sample volume was chosen at the ventricular side of the valve orifice and within the right ventricular tract. This was(More)
The sonogram outputs of autoregressive (AR) based spectral analysis of a 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. The data obtained from coronary and iliac arteries were processed using AR-based spectral analysis technique, and then the interpretable sonograms by the surgeons were constructed. When the sonogram outputs were compared(More)
Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the(More)