Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6818
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Browsing Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering by Author "Akan, Aydın"
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Publication Analysis of brain connectivity changes after propofol injection by generalized partial directed coherence(Academic Press Inc Elsevier Science, 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa, 2014) Akan, Aydın; Özkan Seyhan, Tülay; GÜRKAN, GÜRAY; 113297; 2918; 25235In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG alpha frequency band (8-14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between. awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central. to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia. (C) 2013 Elsevier Inc. All rights reserved.Publication Analysis of linear lung models based on state-space models(Elsevier Ireland Ltd., 2020-01) Saatçi, Ertuğrul; Akan, Aydın; SAATÇI, ESRABackground and Objectives: Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. Methods: We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. Results: Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. Conclusion: We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models. (C) 2019 Elsevier B.V. All rights reserved.Publication Application of Stockwell Coherence To Prediction Of Alertness Under Anaesthesia(Brno Univ Technology Vut Press, Purkynova 118, Brno 61200, Czech Republic, 2010) Akan, Aydın; Demiralp, Tamer; GÜRKAN, GÜRAY; TR113297; TR2918; TR6916In this paper, we represent an application of Stockwell Coherence to scalp EEG signals under anesthesia. By the analysis of overlapping, stationary data segments, we obtained the time-varying Stockwell coherence, which reveals the time evolution of coherence changes between any selected channel pair. After the injection and release of the anesthetic agent, time-varying Stockwell coherence analyses demonstrate similar characteristics for different patients.Publication Application of stockwell coherence to prediction of alertness under anaesthesia(2010) Akan, Aydın; Demiralp, Tamer; GÜRKAN, GÜRAY; 113297; 2918In this paper, we represent an application of Stockwell Coherence to scalp EEG signals under anesthesia. By the analysis of overlapping, stationary data segments, we obtained the time-varying Stockwell coherence, which reveals the time evolution of coherence changes between any selected channel pair. After the injection and release of the anesthetic agent, time-varying Stockwell coherence analyses demonstrate similar characteristics for different patients.Publication Bandpass sampling of multiband signals by using geometric approach(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2017) Saatçı, Ertuğrul; Akan, Aydın; SAATÇI, ESRA; 112197; 10488; 2918This paper presents a simple and fast approach to f nd a minimum sampling frequency for multi-band signals. Instead of neighbor and boundary conditions, constraints on the sampling frequency were derived by using the geometric approach. Reformulation of the minimum sampling determination problem by using geometric approach enables to represent the problem as a basic inequality problem. Recursive algorithm was proposed to solve the constraints on the minimum sampling frequency. The proposed method was verif ed through numerical simulations in terms of the minimum sampling frequency and the computational eff ciency by using 2-band and 3-band signals. Although the results illustrated the valid minimum sampling frequencies for the multi-band signals, due to the increase in the number of iterations, optimization approaches were recommended in the solution of the constraints on the minimum sampling frequency.Publication Connectivity Analysis of EEG Signals Under Propofol Anesthesia with Time varying Generalized Partial Directed Coherence(2010) Akan, Aydın; Demiralp, Tamer; GÜRKAN, GÜRAY; 113297; 2918Publication Doku Analiz Yaklaşımına Dayanan Yeni Bir İris Tanıma Yöntemi(IEEE, 2005) Akan, Aydın; GÜRKAN, GÜRAY; 113297; 2918Bu çalışmada, iris yardımı ile kişi tanıma konusunda doku analizine dayalı yeni bir yöntem sunulmaktadır. İris görüntüleri bir ön işlemeden geçirildikten sonra doku analizine dayanan öznitelik vektörleri çıkarılmaktadır. Normalize edilen iris görüntüsü dikey logaritmik olarak örneklenir ve dairesel simetrik Gabor süzgeçlerinden geçirilir. Süzgeç çıkışları üzerinde kayan bir pencere altındaki piksellerin ortalama mutlak sapma değerleri öznitelik olarak elde edilir. Önerilen yöntem, iris ile kişi tanıma sisteminde aranan önemli niteliklerden ölçek ve dönüş ile değişmezlik özelliklerine sahiptir. Sistemin yazılım kısmı, Delphi derleyicisi ile Windows ortamında gerçeklenmiştir.Publication Dual Kalman Filter based State-Parameter Estimation in Linear Lung Models(Springer, 233 Spring Street, New York, Ny 10013, United States, 2009) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918Time-domain approach to inverse modeling of respiratory system requires estimation of the parameters from the noisy observation. In this work, states and parameters of the linear lung models are estimated simultaneously by dual Kalman filter where the algorithm use two-observation forms. We also employ Kalman smoother for fine tuning the parameters. It is found that the state estimates are more robust to initial filter parameters than the model parameter convergences. Both viscoelastic and the Mead models yielded encouraging results and compatible estimator variances.Publication EEG Sinyalinin Anestezi Sırasındaki Topografik ve Zamansal İzge Analizi(IEEE, 2018) Uslu, Atilla; Cebeci, Bora; Erdoğan, Ezgi T.; Kaşıkçı, Itır; Seyhan, Tülay Ö.; Akan, Aydın; Demiralp, Tamer; GÜRKAN, GÜRAY; 113297; 221283; 141798; 167802; 2918; 6916Bu çalışmada EEG sinyalinin anestezi altındaki izgesel değişimi, zamansal ve uzaysal olarak incelenmiştir. Bu amaçla kullanılan yöntemler arasında, günümüz anestezi derinliği takip cihazlarının da kullandığı bilinen SEF-90, α-β bandı güç oranları ve izgesel entropi yer almaktadır. Ayrıca doğrusal olmayan sistemlerin analizinde kullanılan Higuchi fraktal boyutu da karşılaştırma olarak incelenmiştir. Uzaysal analiz sonucunda, normal bireylerde oksipital bölgede görülen baskın alfa aktivitesinin artan anestezi derinliği ile frontal bölgeye kaydığı doğrulanmış ve ilgili topografik gösterimler sunulmuştur.Publication Estimation Of The Respiratory System Parameters(European Assoc Signal Speech & Image Processing-Eurasip, Po Box 74251, Kessariani, 151 10, Greece, 2011) Sert, Görkem; Akan, Aydın; GÜRKAN, GÜRAY; SAATÇI, ESRA; TR112197; TR113297; TR2918In clinical respiratory studies, resistance and the lung compliance are two important respiratory parameters that are often measured by physicians. In this work, Respiratory signals (mask pressure, airway flow, and lung volume) are measured by using artificial lung simulator and mannequin head and respiratory parameters set on the simulator are estimated by the best linear unbiased estimator (BLUE). However, prior to the estimation, muscular pressure signals that symbolize the effect of the respiratory parameters on the respiratory signals are computed by using least mean square (LMS) based adaptive noise canceler (ANC). It is found that LMS filter length considerably effects the filter output and in turn the estimation results. Thus, it is suggested to use mis-adjustement criterion in LMS-ANC filter to select the filter order by processing the signals that have only one respiratory parameter variation. In conclusion, respiratory parameters are successfully estimated from the muscular pressure signals that are filtered out with appropriate filter lengths.Publication Frequency estimation for monophonical music by using a modified VMD method(IEEE, 345 E 47Th St, New York, Ny 10017 USA, 2017) Öztürk Şimşek, Berrak; Akan, Aydın; 107113; 2918In this paper, a new Variational Mode Decomposition (VMD) is introduced, and applied to the fundamental frequency estimation of monophonical Turkish maqam music. VMD is a method to decompose an input signal into an ensemble of sub-signals (modes) which is entirely non-recursive. It determines the relevant bands adaptively, and estimates the corresponding modes concurrently. In order to optimally decompose a given signal, VMD seeks an ensemble of modes with narrow-band properties corresponding to the Intrinsic Mode Function (IMF) definition used in Empirical Mode Decomposition (EMD). In our proposed modified VMD approach, in order to obtain the bandwidth of a mode, each mode is shifted to baseband by mixing an exponential that is adjusted to the respective center frequency. The bandwidth is estimated through elastic net method that linearly combines penalties of the Lasso and Ridge Regression methods. Simulation results on fundamental frequency estimation of real music and synthetic test data show better performance compared to classical VMD based approach, and other common methods used for music signals, such as YIN and MELODIA based methods.Publication Fundamental Frequency Estimation for Monophonical Turkish Music by using VMD(IEEE, 345 E 47Th St, New York, Ny 10017 USA, 2015-05-16) Öztürk Şimşek, Berrak; Akan, Aydın; Bozkurt, Barış; 107113; 2918In this study, a new method is presented for the fundamental frequency estimation of Turkish makam music recordings by using Variational Mode Decomposition (VMD). VMD is a method to decompose an input signal into an ensemble of sub-signals (modes) which is entirely non-recursive and determines the relevant bands adaptively and estimates the corresponding modes concurrently. In order to decompose a given signal optimally, actuated by the narrow-band properties corresponding to the Intrinsic Mode Function (IMF) definition used in Emprical Mode Decomposition (EMD), and we seek an ensemble of modes. Simulation results on fundamental frequency estimation of real music and synthetic test data show better performance compared to other common decomposition methods for music signals such as spectrogram, YIN, MELODIA and EMD based methods.Publication Generalized Gauss Distribution Noise Model for Respiratory Parameter Estimation(Ieee, 345 E 47Th St, New York, Ny 10017 Usa, 2009) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918In this studs, measurement noise is modelled as a Generalized Gauss Distribution and a new method is presented to estimate the model parameters. The estimator algorithm consists of the Kurtosis method, Kalman iterarions and the Maximum likelihood method. The proposed method is successfully applied to linear lung model parameter estimation problem.Publication Heart sound localization and reduction in tracheal sounds by gabor time-frequency masking(Istanbul Unıv, Fac Engineering, Elektrik-Elektronik Mühendisliği Bölümü, Avcılar Kampüsü, İstanbul, 34320, Turkey, 2017) Akan, Aydın; SAATÇI, ESRA; 112197; 2918Respiratory sounds, i.e. tracheal and lung sounds, have been of great interest due to their diagnostic values as well as the potential of their use in the estimation of the respiratory dynamics (mainly airflow). Thus the aim of the study is to present a new method to filter the heart sound interference from the tracheal sounds. Tracheal sounds and airflow signals were collected by using an accelerometer from 10 healthy subjects. Tracheal sounds were then pre-processed by Recursive Least Square - Adaptive Noise Cancellation (RLS-ANC) filter to remove background noise. Gabor time-frequency expansion was used for both heart sound localization and reduction problem. In the first step of filtering, RLSANC successfully filtered out the broad - band ambient noise. Reconstruction of tracheal sound was achieved from modified Gabor coefficients without heart sound noise. Visual inspection and quantitative analysis demonstrated that Gabor time-frequency masking with RLS-ANC filters provides successful tracheal sound signal separation.Publication Inverse Modeling of Respiratory System during Noninvasive Ventilation by Maximum Likelihood Estimation(Hindawi Publishing Corporation, 410 Park Avenue, 15Th Floor, #287 Pmb, New York, Ny 10022 Usa, 2010) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918We propose a procedure to estimate the model parameters of presented nonlinear Resistance-Capacitance (RC) and the widely used linear Resistance-Inductance-Capacitance (RIC) models of the respiratory system by Maximum Likelihood Estimator (MLE). The measurement noise is assumed to be Generalized Gaussian Distributed (GGD), and the variance and the shape factor of the measurement noise are estimated by MLE and Kurtosis method, respectively. The performance of the MLE algorithm is also demonstrated by the Cramer-Rao Lower Bound (CRLB) with artificially produced respiratory signals. Airway flow, mask pressure, and lung volume are measured from patients with Chronic Obstructive Pulmonary Disease (COPD) under the noninvasive ventilation and from healthy subjects. Simulations show that respiratory signals from healthy subjects are better represented by the RIC model compared to the nonlinear RC model. On the other hand, the Patient group respiratory signals are fitted to the nonlinear RC model with lower measurement noise variance, better converged measurement noise shape factor, and model parameter tracks. Also, it is observed that for the Patient group the shape factor of the measurement noise converges to values between 1 and 2 whereas for the Control group shape factor values are estimated in the super-Gaussian area.Publication Lung model parameter estimation by unscented Kalman filter(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2007) Akan, Aydın; SAATÇI, ESRA; TR2918; TR112197Dynamic nonlinear models are the best choice to analyze respiratory systems and to describe system mechanics. In this work, Unscented Kalman Filtering (UKF) was used to estimate the dynamic nonlinear model parameters of the lung model by using the measured airway flow, mask pressure and integrated lung volume. Artificially generated data and the data from Chronic Obstructive Pulmonary Diseased (COPD) patients were analyzed by the proposed model and the proposed UKF algorithm. Simulation results for both cases demonstrated that UKF is a promising estimation method for the respiratory system analysis.Publication Lung Sound Noise Reduction Using Gabor Time-Frequency Masking(SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, 2007) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918The Gabor expansion is a mathematical tool, which provides a joint time-frequency representation of a given signal by decomposing it into time-frequency elementary signals called Gabor atoms. It has been used in a variety of signal processing applications, including biomedical signal processing. In this paper we present a time-frequency masking technique based on Gabor expansion for both heart sound localization and reduction problem. Gabor coefficients of lung sound segments recorded from trachea are calculated and masked to remove the distinctive heart sound effects. Reconstruction of lung sound is achieved from modified Gabor coefficients without heart sound noise.Publication Monofonik Türk Makam Müziğinde İşitsel Melodi Kestirimi(IEEE, 2020) ŞİMŞEK, BERRAK ÖZTÜRK; Akan, AydınBu çalışmada, monofonik Türk makam müziği eserlerinde baskın işitsel melodi çıkarımı için yeni bir yöntem önerilmiştir. Müzik sinyalleri, Geliştirilmiş Değişken Mod Ayrıştırma Yöntemi kullanılarak ayrıştırılır ve temel frekanslar, her moddaki merkez frekanslarının hesaplanmasıyla elde edilir. Baskın melodi hattını tahmin etmek ve seçmek için her penceredeki farklı frekans bilgilerinden bazı parametreler belirlenir. Elde edilen sonuçlar, batı müziği için kullanılan sinyal işleme algoritmaları olan YIN ve MELODIA ile karşılaştırılmıştır. Değerlendirme aşamasında uluslararası standartlara bağlı kalınarak MIREX kriterleri kullanılmaktadır. Simülasyon sonuçları, Geliştirilmiş Değişken Mod Ayrıştırma Yönteminin karşılaştırmada kullanılan diğer yöntemlere göre daha başarılı sonuçlar verdiğini göstermiştir.Publication Multiple frequency estimation by using improved variational mode decomposition(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2017) Öztürk Şimşek, Berrak; Akan, Aydın; 107113; 2918In this study, an improved Variational Mode Decomposition method is introduced and applied to the multi frequency estimation problem in heterophonical Turkish maqam music recordings. The main purpose of this method is to decompose a given signal into a predetermined, finite number of modes. Center frequencies of the fundamental components are calculated in a non-iterative and adaptive manner, while the modes are estimated simultaneously. In the traditional DKA algorithm, Tikhonov Regularization is employed to estimate the center frequencies of the modes. Instead, we propose to use ElasticNet Regression to solve the optimization problem and thus improve the fundamental frequency estimation performance. By using the proposed approach, simulations are performed on multiple frequency estimation in heterophonical Turkish maqam music recordings. Results are compared to that of the commonly used fundamental frequency estimation techniques in the literature such as Melodia and YIN, and evaluated by using MIREX2016-Multiple Fundamental Frequency Estimation and Tracking criteria. Experimental studies show that the proposed method improves the results of traditional VMD, and outperforms other methods used for comparison.Publication Posterior Cramer-Rao Lower Bounds for Dual Kalman estimation(Academic Press inc Elsevier Science, 525 B St, Ste 1900, San Diego, Ca 92101-4495 USA, 2012-01) Akan, Aydın; SAATÇI, ESRA; 112197; 2918We present the Posterior Cramer-Rao Lower Bounds (PCRLB) for the dual Kalman filter estimation where the parameters are assumed to be time-invariant and stationary random variables. Relations between the PCRLB, the states, and the parameters are established and recursions are obtained for finite observation time. As a case study, the closed-form expressions of the PCRLB for a linear lung model, called the Mead respiratory model, are derived. Distribution of the parameters is assumed to be Generalized Gaussian Distribution (GGD) which enabled an investigation of different shape factors. Simulations performed on the signals collected from the human respiratory system yielded encouraging results. It is concluded that the parameter distribution should be chosen as Gaussian to super-Gaussian in order for the PCRLB algorithm to converge. (C) 2011 Elsevier Inc. All rights reserved.