Looking for the brain stroke signature

This conference paper investigates the possibility of using on-line handwritten signatures for biomedical biometry. More specifically, features extracted from sigma-lognormal representations of signatures are applied to the problem of brain stroke susceptibility assessment. The area under the receiver operating characteristic curve (AUC) is used to evaluate the predictability of the most important modifiable brain stroke risk factors (diabetes, hypertension, hypercholesterolemia, obesity, cigarette smoking, cardiac problems) based on four different statistical modeling of the features' variation (random forest, linear discriminant analysis, logistic regression and linear regression). Our preliminary results show a potential predictability (AUC of about 0.7-0.8) for every risk factor, except for cigarette smoking. Avenues for improving these results are discussed.