Yazar "Gunes, Salih" seçeneğine göre listele

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    Analysis and design of a transimpedance amplifier based front-end circuit for capacitance measurements
    (Springer Int Publ Ag, 2020) Demirtas, Mehmet; Erismis, Mehmet Akif; Gunes, Salih
    In this study, transimpedance amplifier based front-end circuits which can be employed to measure small capacitances were designed, analyzed and simulated using analog electronic circuit simulator. The front-end circuit converts the current flowing through the measured capacitance into a modulated voltage value which contains information regarding the desired capacitance. The frequency-domain, time-domain, stability and noise analyzes were carried out numerically and in simulation environment using a circuit simulator. The analytical, numerical and simulation results can be used to design optimized, precise and stable transimpedance amplifiers with low-noise value. The measured capacitance value was 10 pF which is low enough to simulate various real-world applications. Three commercially available, off-the-shelf operational amplifiers with different peripheral passive components were employed for computer based analysis. The designed transimpedance amplifiers are suitable to connect with capacitance extraction circuits which use analog or digital demodulation techniques.
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    Automated elimination of EOG artifacts in sleep EEG using regression method
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Dursun, Mehmet; Ozsen, Seral; Gunes, Salih; Akdemir, Bayram; Yosunkaya, Sebnem
    Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%-1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.
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    EFFECT OF SOME POWER SPECTRAL DENSITY ESTIMATION METHODS ON AUTOMATIC SLEEP STAGE SCORING USING ARTIFICIAL NEURAL NETWORKS
    (Iadis, 2013) Yucelbas, Cuneyt; Ozsen, Seral; Gunes, Salih; Yosunkaya, Sebnem
    Sleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring systems get popularity. In this study, we obtained EEG, EMG and EOG signals of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transform (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others. According to the results, the maximum test classification accuracy was reported as 79.72% by using of FFT method for subject1. Also, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,58 and 70.34% with use of FFT, Welch and AR, respectively.
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    A Lossy Capacitance Measurement Circuit Based on Analog Lock-in Detection
    (Kaunas Univ Technology, 2020) Demirtas, Mehmet; Erismis, Mehmet Akif; Gunes, Salih
    This paper presents a lossy capacitance measuring circuit which is based on analog lock-in detection technique. Lossy capacitance can be modelled as a pure capacitor connected in parallel with a resistor. The measurement circuit mechanism consists of an excitation signal to drive the lossy capacitance, a transimpedance amplifier to produce a voltage, and a lock-in detection circuit to extract lossy values of capacitance. The lock-in detector multiplies its input with a square wave using switches and filters out high frequencies to give a DC output that is actually in proportional to the measured values. A field programmable gate array is employed to generate direct digital synthesis based sinusoidal excitation signal to generate reference signals required for demodulation and to measure the output of lock-in detection. The phase shift between the excitation signal and reference signals is controlled accurately in digital domain. Thus, due to the phase mismatch, errors are properly reduced. Also, analog phase shifter and analog switch-driving circuits are no longer required. Three different lossy capacitors realized using discrete components are simulated and tested. The maximum relative error is 1.62 % for the resistance measurement and 6.38 % for the capacitance measurement.