Person: SAATÇI, ESRA
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SAATÇI
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Publication Multifractality Analysis of Respiratory Signals(IEEE, 2020) SAATÇI, ESRA; SAATÇI, ERTUĞRULFractal analysis was used to analyze the biomedical signals which are emerged from the fractal structures in the human body. Respiratory signals, such as airflow, mouth pressure, lung volume comprise the complex relationship which has not been inspected and how it is linked to the fractal structure of the lung has not been scrutinized. Thus the aim of this study is to determine the mono or multifractal property of the respiratory signals by using well known method, Multifractal Detrended Fluctuation Analysis (MF-DFA). Real signals were analyzed by utilizing already proposed MF-DFA algorithm and generalized Hurst exponent values were shown for different scales. In the results, it was shown that respiratory signals are fractional Brown motion type signals and fractal properties exhibit less intersubject change. Finally, it was proved that apart from the airflow and lung volume, respiratory sounds and signals are multifractal signals. It appears that the presence of long-memory property of the lung is the primary reason of the multifractality.Publication Respiratory Parameter Estimation In Non-İnvasive Ventilation Based On Generalized Gaussian Noise Models(Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands, 2010-02) Akan, Aydın; SAATÇI, ESRA; TR112197; TR2918Modeling of respiratory system under non-invasive ventilation by using measured respiratory signals is of great interest in respiratory mechanics research area. Statistical processing techniques in the time-domain may be utilized as an alternative to the commonly used frequency-domain analysis to estimate model parameters. In this work, we propose using a generalized Gaussian distribution (GGD) to model the measurement noise in the respiratory system identification problem. The parameters of the GGD (i.e. the mean, the variance and the shape) are estimated by maximum likelihood (ML) and moment based estimators. However, the estimation error should also be taken into account which is in fact investigated as measurement innovations together with the measurement noise. Thus the Kalman iterations are applied with the help of the score function to compute the measurement innovations. Finally, the complete picture of the measurement noise and innovation analysis of the respiratory models is obtained which helped us to evaluate the non-Gaussian noise extension in the respiratory system analysis. (C) 2009 Elsevier B.V. All rights reserved.Publication State-space analysis of fractional-order respiratory system models(2020-03) Saatçi, Ertuğrul; SAATÇI, ESRAFractional Order Models (FOM) of the respiratory system 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 analyzed in terms of interactions between parameters, time-varying dynamics and measurable signals. In this study we purpose to use state-space analysis to yield the time-varying nature of the system incorporated by the parameters, states and output. We tested the models for controllability, observability and stability characteristics while using the parameters found in the literature. Sufficient asymptotic stability bounds were driven by using stability theory of the discrete time-delay system. Results revealed that FOMs with estimated parameters offer systems with different characteristics. Thus, careful consideration must be given when interpreting estimated parameters in FOMs during respiratory tests. © 2019 Elsevier LtdPublication Determination of theminimum sampling frequency in bandpass sampling by geometric approach and geometric programming(Springer, 2018-10) Saatçı, Ertuğrul; SAATÇI, ESRA; 112197; 10488This paper presents a simple and fast approach to find 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 to the bandpass sampling theorem. Reformulation of the constraints on the minimum sampling frequency enabled to represent the problem as an optimization problem which was structured by the geometric programming and mixed-integer nonlinear programming methods. The convex optimization problem was then solved by the proposed algorithm applying interior point approach in the line search framework. It was demonstrated that this unified structure directly linked the geometric approach of the bandpass sampling theorem to the optimization problem. The proposed method was verified through numerical simulations in terms of the minimum sampling frequency and the computational efficiency. Results illustrated the feasibility of the geometric approach and the proposed algorithm in the determination of the minimum sampling frequency by providing the savings in the number of iterations and the decrease in the valid minimum sampling frequency.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 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 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 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 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 Akciğer Basınçlarının İnvasiv Olmayan Yöntemler ile Kestirilmesi Amacıyla Akciğer Basınçları Ve Akciğer Sesleri Arasındaki İlişkinin Modellenmesi(TÜBİTAK EEEAG Proje, 2020) SAATÇI, ESRA; Öztürk, Ayşe Bilge; SAATÇI, ERTUĞRUL; Akan, aydınSolunum fonksiyon testleri solunum hastalıklarının teshis ve tedavisinin izlenmesinde kullanılırlar. Hastane ortamında yapılan bu testler pahalı cihazlara ve hastalar tarafından yapılan çesitli solunum manevralarına ihtiyaç duyarlar. Bu projenin amacı klinikte kullanılan solunum fonksiyon testlerinin yerine basit yöntemler ile solunum parametrelerinin bulunmasıdır. Bu amacı gerçeklestirmek için akciger basınçlarının girisimsel olmayan yöntemler ile kestirilmesi gerekmektedir. Basit mikrofonlar ile ölçülen akciger seslerinin ve havayolu gaz akıs hızı, sıcaklıgı ve nemi gibi çesitli solunum sinyallerinin istatistiksel ve fraktal sinyal isleme yöntemleri ile islenmesi bu projede önerilen temel yöntemdir. Solunum parametrelerinin kestiriminde bazı sinyal isleme yaklasımları önerilmis olsa bile solunum sesleriyle beraber istatistiksel ve fraktal sinyal isleme yöntemleri kombinasyonunun kullanılması bu projenin yenilikçi kısmıdır. Yapılan analizler sonucunda derin ve normal solunumların birlikte kullanıldıgı bronsial solunum sesinden elde edilen Hurst üstelinin agız içi basıncının kestiriminde en basarılı sonuçları verdigi görülmüstür. Ayrıca viskoelastik modelin yardımıyla kestirilen akciger basınçlarının gücü en iyi spirometrik testlerde FEV1 ve FVC parametreleriyle IOS testinde R5 parametresi ile ilişkilidir.