Person: SAATÇI, ESRA
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- PublicationMetadata onlyMultifractality 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.
- PublicationOpen AccessAkciğer 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.
- PublicationOpen AccessDetermination of Respiratory Parameters by Means of Hurst Exponents of the Respiratory Sounds and Stochastic Processing Methods(IEEE-Institute of Electrical and Electronics Engineers Inc., 2021) SAATÇI, ESRA; SAATÇI, ERTUĞRULObjectives: System approach to the human respiratory system and input/output signals which characterize the system properties were not explored in detail in the literature. The aim of this study is to propose a combination of methods to investigate the indirect relationship between the fractal properties of Respiratory Signals (RS) and Respiratory Sound Signals (RSS) and the clinically measured respiratory parameters. Methods: We used Hurst exponent to reveal the fractal properties of RS and RSS and to estimate the pressures in the respiratory system. The combination of well-known statistical signal processing methods and optimization were applied to the experimentally acquired 23 records. Pearson correlation coefficient and Bland-Altman analysis were the chosen validation methods. Results: Considerable amounts of Hurst exponent values of RSS were found to be between 0.5 and 1, which means increasing trend or decreasing trend can be seen in RSS with fractional Gaussian process properties. Results of the pressure estimator revealed that internal pressure due to tissue viscoelasticity is higher than the pressure due to static elasticity. Feature power and skewness also provided distinctive results for all recordings. Conclusion: Hurst exponent values of the RSS are fruitful representation of the signals which bring the underlaying system characteristics into the surface. We illustrated that required number of sensors can be reduced in the feature calculation to ease implementation effort on the hardware of the handheld devices. Significance: Bland-Altman plots were very successful to demonstrate the connection between the sets of measured respiratory parameters and calculated features.
- PublicationOpen AccessMultifractal Behaviour of Respiratory Signals(AVES Yayıncılık, İstanbul Üniversitesi-Cerrahpaşa, 2020) SAATÇI, ERTUĞRUL; SAATÇI, ESRAIn this study, to analyze the biomedical signals emerging from fractal structures in the human body, fractal analysis was used. Respiratory signals, such as airflow, mouth pressure, and lung volume, comprise a complex relationship that has not been inspected to date. Furthermore, the mechanism for which it is linked to the lung’s fractal structure has not been scrutinized to date. Thus, using a well-known method, known as multifractal detrended fluctuation analysis (MF-DFA), this study aims to determine both mono- and multi-fractal property of respiratory signals ,. The real signals were analyzed using the MF-DFA algorithm. Moreover, for different scales, generalized Hurst exponent values were calculated. The results demonstrated that respiratory signals are fractional Brown motion-type signals, whereas fractal properties demonstrate less intersubject change. Moreover, in addition to both airflow and lung volume, respiratory signals and sounds are multifractal signals. In conclusion, the presence of the lung’s long-memory property is the primary reason of multifractality.