  We present a novel methodology for automated feature subset selection from a
pool of physiological signals using Quantum Annealing (QA). As a case study, we
will investigate the effectiveness of QA-based feature selection techniques in
selecting the optimal feature subset for stress detection. Features are
extracted from four signal sources: foot EDA, hand EDA, ECG, and respiration.
The proposed method embeds the feature variables extracted from the
physiological signals in a binary quadratic model. The bias of the feature
variable is calculated using the Pearson correlation coefficient between the
feature variable and the target variable. The weight of the edge connecting the
two feature variables is calculated using the Pearson correlation coefficient
between two feature variables in the binary quadratic model. Subsequently,
D-Wave's clique sampler is used to sample cliques from the binary quadratic
model. The underlying solution is then re-sampled to obtain multiple good
solutions and the clique with the lowest energy is returned as the optimal
solution. The proposed method is compared with commonly used feature selection
techniques for stress detection. Results indicate that QA-based feature subset
selection performed equally as that of classical techniques. However, under
data uncertainty conditions such as limited training data, the performance of
quantum annealing for selecting optimum features remained unaffected, whereas a
significant decrease in performance is observed with classical feature
selection techniques. Preliminary results show the promise of quantum annealing
in optimizing the training phase of a machine learning classifier, especially
under data uncertainty conditions.
