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VARIABILITY ANALYSIS & ITS APPLICATIONS TO PHYSIOLOGICAL TIME SERIES DATA

Kaffashi, Farhad
http://rave.ohiolink.edu/etdc/view?acc_num=case1181072302

Abstract Details

2007, Doctor of Philosophy, Case Western Reserve University, Electrical Engineering.
In this thesis, novel variability analysis techniques are developed and refinements are made to some currently available methods to enhance their use and effectiveness. These variability analysis techniques are applied to physiological time series data to study both health and disease. In particular, the addition of a new parameter, the time delay, is proposed to enhance the performance of Approximate and Sample Entropy calculations; a novel technique is developed to estimate the gradient of power law behavior based on non-parametric change-point detection; a novel technique is developed to quantify the coupling between time series data based on surrogate data analysis, and the limitations of Detrended Fluctuation Analysis (DFA) are studied in the context of the detection of self similarities in EEG time series data. The techniques that are developed in the thesis are applied in several areas to evaluate their suitability and ffectiveness. In one application, neurodevelopment and maturation of the neonatal brain is studied and the effectiveness of strategies that can improve sleep organization, such as skin-to-skin contact or Kangaroo Care (KC) intervention, are evaluated using Approximate and Sample Entropy. The results show that the KC intervention improves neurodevelopment and maturation. In a study of epilepsy, a novel technique to quantify electrocorticography data using the DFA is presented. The DFA can detect changes in the electrical activity of the brain that are associated with different brain states such as seizure (ictal), preictal, postictal as well as arousals that are occurring during sleep. In the analysis of respiratory data, the complexity of in vitro modularly prepared neonatal rat slices, that are capable of generating a spontaneous respiratory related rhythm at different extra cellular K+ levels are quantified and further, the coupling between the respiratory and cardiac networks is investigated using a novel approach based on surrogate data analysis.
Kenneth Loparo (Advisor)
124 p.
Engineering, Biomedical
Epilepsy; Neonatal EEG; Respiration; Variability Analysis; Approximate Entropy; Sample Entropy

Recommended Citations

Citations

  • Kaffashi, F. (2007). VARIABILITY ANALYSIS & ITS APPLICATIONS TO PHYSIOLOGICAL TIME SERIES DATA [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1181072302

    APA Style (7th edition)

  • Kaffashi, Farhad. VARIABILITY ANALYSIS & ITS APPLICATIONS TO PHYSIOLOGICAL TIME SERIES DATA. 2007. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1181072302.

    MLA Style (8th edition)

  • Kaffashi, Farhad. "VARIABILITY ANALYSIS & ITS APPLICATIONS TO PHYSIOLOGICAL TIME SERIES DATA." Doctoral dissertation, Case Western Reserve University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1181072302

    Chicago Manual of Style (17th edition)

case1181072302
2,281
© 2007, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.