Program Information

Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix

D Michalski*, M Huq , G Bednarz , R Lalonde , Y Yang , d heron , University of Pittsburgh Medical Center, Pittsburgh, PA

Presentations

SU-E-J-261 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:
To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover.
Methods:
20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared.
Results:
Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same is for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans.
Conclusion:
Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied.

Funding Support, Disclosures, and Conflict of Interest: Funding provided by Varian Medical Systems via Investigator Initiated Research Project Conflict of interest:None

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