Statistical Parameter Estimation And Signal
Classification In Cardiovascular Diagnosis
Author(s)
S. Bernhard, K. Al Zoukra & C. Schütte
Abstract
Medical technology has seen impressive success in the past decades, generating
novel clinical data at an unexpected rate. Even though numerous physiological
models have been developed, their clinical application is limited. The major
reason for this lies in the difficulty of finding and interpreting the model
parameters, because most problems are ill-posed and do not have unique
solutions. On the one hand the reason for this lies in the information deficit of
the data, which is the result of finite measurement precision and contamination
by artifacts and noise and on the other hand on data mining procedures that
cannot sufficiently treat the statistical nature of the data. Within this work we
introduce a population based parameter estimation method that is able to reveal
structural parameters that can be used for patient-specific modeling. In contrast
to traditional approaches this method produces a distribution of physiologically
interpretable models defined by patient-specific parameters and model states. On
the basis of these models we identify disease specific classes that correspond to
clinical diagnoses, which enable a probabilistic assessment of human health
condition on the basis of a broad patient population. In an ongoing work this
technique is used to identify arterial stenosis and aneurisms from anomalous
patterns in parameter space. We think that the information-based approach will
provide a useful link between mathematical models and clinical diagnoses and
that it will become a constituent in medicine in near future.
Keywords: statistical cardiovascular system model, cardiovascular system
identification, multi-channel measurement, state-space model, parameter
estimation, Bayesian signal classification, patient-specific diagnosis.
Keywords
statistical cardiovascular system model, cardiovascular systemidentification, multi-channel measurement, state-space model, parameterestimation, Bayesian signal classification, patient-specific diagnosis
Related Book
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