Sleep Staging Using Plausibility Score: A Novel Feature Selection Method Based on Metric Learning

As an effective method, feature selection can reduce computational complexity and improve classification performance. A number of criteria exist for feature selection using labeled data, unlabeled data and pairwise constraints, most of which are based on the Euclidean distance. In this paper, we propose a filter method for feature selection with pairwise constraints, aiming to jointly evaluate a feature subset based on metric learning. Two criteria are designed based on the well-known Kullback-Leibler divergence for measuring the difference between must-link constraints and cannot-link constraints that can indicate the feature subset discrimination based on Keep It Simple and Straightforward (KISS) metric learning and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning. To address the challenging feature selection problem, we formulate a sequential search algorithm guided by indicators that are simplified from the proposed criteria. Furthermore, we conducted several experiments on sleep staging based on electroencephalogram (EEG) recordings from the Sleep-EDF Database Expanded. The experimental results demonstrate the effectiveness of the proposed method compared with nine representative feature selection methods. On the data set from healthy volunteers and the data set from volunteers that had mild difficulty falling asleep, the classification average accuracies achieve 97.66% and 93.57% by using the proposed method, respectively.