Emotion recognition from multimodal physiological measurements based on an interpretable feature selection method.

Many studies in literature successfully use classification algorithms to classify emotions by means of physiological signals. However, there are still important limitations in interpretability of the results, i.e. lack of feature specific characterizations for each emotional state. To this extent, our study proposes a feature selection method that allows to determine the most informative subset of features extracted from physiological signals by maintaining their original dimensional space. Results show that features from the galvanic skin response are confirmed to be relevant in separating the arousal dimension, especially fear from happiness and relaxation. Furthermore, the average and the median value of the galvanic skin response signal together with the ratio between SD1 and SD2 from the Poincarè analysis of the electrocardiogram signal, were found to be the most important features for the discrimination along the valence dimension. A Linear Discriminant Analysis model using the first ten features sorted by importance, as defined by their ability to discriminate emotions with a bivariate approach, led to a three-class test accuracy in discriminating happiness, relaxation and fear equal to 72%, 67% and 89% respectively.Clinical relevance This study demonstrates the ability of physiological signals to assess the emotional state of different subjects, by providing a fast and efficient method to select most important indexes from the autonomic nervous system. The approach has high clinical relevance as it could be extended to assess other emotional states (e.g. stress and pain) characterizing pathological states such as post traumatic stress disorder and depression.