SAATÇI, ESRASAATÇI, ERTUĞRUL2022-11-072022-11-072020Saatçi, E., & Saatçi, E. (2020, October). Multifractality Analysis of Respiratory Signals. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.978-1-7281-7206-42165-0608https://hdl.handle.net/11413/7916Fractal 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.eninfo:eu-repo/semantics/restrictedAccessRespiratory SignalSmono Fractalmulti FractalGeneralized Hurst ExponentconferenceObject000653136100315