njit-etd2003-081 - New Jersey Institute of Technology

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autoregressive (ARX) system identification techniques [6] are introduced to characterize
the system. Pre-processing (pre-filtering of the HR fluctuations and pre-enhancement of
the high frequency contents of respiration) was proven to be effective in obtaining stable
estimates of MA coefficients. Model order selection of the ARX system based on a
priori knowledge of the system characteristics is proposed. Data analysis using the time
domain techniques may reveal some temporal transfer characteristics which are poorly
resolved using frequency domain analysis.
This study also employs a new approach to Exogenous Input AutoRegressive
(ARX) modeling [7], which automatically seeks the best model order to represent,
investigated linear, time invariant systems using their input/output data. The algorithm
seeks the ARX parameterization, which accounts for variability in the output of the
system due to input activity and contains the fewest number of parameters required to do
so. The unique characteristics of the proposed system identification algorithm are its
simplicity and efficiency in handling systems with delays and multiple inputs. Results
of applying the algorithm to simulated data and experimental biological data are
presented. In addition, a technique for assessing the error associated with the impulse
responses, calculated from estimated ARX parameterizations, is presented. This
technique is one example of a variety of techniques of system identification. It was
shown that system identification techniques could yields new insight into the sequence
of changes that occurs with COPD autonomic neuropathy and provided an accurate
easily comprehensible measurement of respiratory induced HR variability.
The last task of this study is normal-COPD separation and COPD severity
classification. Major contributing components of the cardiovascular system are