Quantitative data validation (automated visual evaluations)
Historically, validation has been perfonned on a case study basis employing visual
evaluations, gradually inspiring confidence through continual application. At present,
the method of visual evaluation is the most prevalent form of data analysis, as the brain
is the best pattern recognition device known. However, the human visual/perceptual
system is a complicated mechanism, prone to many types of physical and psychological
influences. Fatigue is a major source of inaccuracy within the results of subjects
perfonning complex visual evaluation tasks. Whilst physical and experiential
differences along with age have an enormous bearing on the visual evaluation results of
different subjects. It is to this end that automated methods of validation must be
developed to produce repeatable, quantitative and objective verification results.
This thesis details the development of the Feature Selective Validation (FSV) method.
The FSV method comprises two component measures based on amplitude differences
and feature differences. These measures are combined employing a measured level of
subjectivity to fonn an overall assessment of the comparison in question or global
difference. The three measures within the FSV method are strengthened by statistical
analysis in the form of confidence levels based on amplitude, feature or global
discrepancies between compared signals. Highly detailed diagnostic infonnation on the
location and magnitude of discrepancies is also made available through the employment
of graphical (discrete) representations of the three measures.
The FSV method also benefits from the ability to mirror human perception, whilst
producing infonnation which directly relates human variability and the confidence
associated with it. The FSV method builds on the common language of engineers and
scientists alike, employing categories which relate to human interpretations of
comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and
'extremely poor' .