Toward a Holistic Computational Representation for Sleep Quality and its Support for Explainability

Sleep quality is recognized as one of the main factors that affect human health. Thus, several studies have been encouraged to analyze features, such as stress level and female menopause, which are directly related to sleep quality. While these works rely mostly on reductionism as the philosophical framework, we approach this problem from a holist perspective, using a model with 10 features that could provide more reliable explanations for inductive conclusions. We demonstrate the principles of this hypothesis by analyzing the data regarding the day before a sleep episode of 1736 volunteers. This analysis shows, for example, the performance of each feature when they are jointly used along prediction tasks. Moreover, we evaluate the readability and accuracy of explanations, given as description logic sentences and based on a knowledge representation that considers the 10 features as elements that compose a sleep quality ontological definition.