Affiliations: Electrical and Computer Engineering Department,
University of Alabama at Birmingham, AL, USA | Department of Computer Science, Indiana University
Purdue University Fort Wayne, IN, USA | Electrical Engineering Department, University of North
Florida, Jacksonville, FL, USA
Abstract: In this paper, visual data analysis was applied to raw medical data
using probability theory to provide valuable information for preliminary
diagnosis. The evolvable hardware design approach combined with information
theory was applied to model an adaptive cardiovascular system. The
cardiovascular system is modelled by a digital logic circuit based on ECG and
ABP signal samples as input and output respectively. In our experiments, five
patients' ECG and ABP data was chosen for the visual analysis. A user friendly
GUI was demoed and the correlation of patient data was analyzed in the space
and time domain. The digital circuit model was extrinsically evolved using
genetic programming as the evolutionary algorithm and mutual information as the
fitness function. In our experiments using MATLAB, we demonstrated that the
data analysis could provide valuable information for preliminary diagnosis, and
the proposed method could fit the input-output relationship as recorded samples
piece-wise in which each piece contains monotonic input data. The model we
proposed is a self reconfigurable digital circuit model based on input and
output information. It's safe to conclude that the model is adaptive to changes
based on different patient's unique ECG and ABP signals since the I/O
information is also changed. Furthermore, a "divide and conquer" method was
employed to get a more accurate piece-wise model. Experimental results show
that the method is feasible, scalable, and promising as a personalized medical
simulation tool.
Keywords: modelling, medical data visualization, evolvable hardware, information theory, combinational logic circuits
