Gaussian Processes with Physiologically-Inspired Priors for Physical Arousal Recognition

Abstract

While machine learning algorithms are able to detect subtle patterns of interest in data, expert knowledge may contain crucial information that is not easily extracted from a given dataset, especially when the latter is small or noisy. In this paper we investigate the suitability of Gaussian Process Classification (GPC) as an effective model to implement the domain knowledge in an algorithm's training phase. Building on their Bayesian nature, we proceed by injecting problemspecific domain knowledge in the form of an a-priori distribution on the GPC latent function. We do this by extracting handcrafted features from the input data, and correlating them to the logits of the classification problem through fitting a prior function informed by the physiology of the problem. The physiologically-informed prior of the GPC is then updated through the Bayes formula using the available dataset. We apply the methods discussed here to a two-class classification problem associated to a dataset comprising Heart Rate Variability (HRV) and Electrodermal Activity (EDA) signals collected from 26 subjects who were exposed to a physical stressor aimed at altering their autonomic nervous systems dynamics. We provide comparative computational experiments on the selection of appropriate physiologically-inspired GPC prior functions. We find that the recognition of the presence of the physical stressor is significantly enhanced when the physiologically-inspired prior knowledge is injected into the GPC model.

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States

“…This paper is a significant extension of our previous work [13], which we have extended in a number of directions.…”

“…• We develop techniques that, by building on physiologically-inspired priors explored in [13], enable us to train more transparent and interpretable GP classification models directly from raw, unprocessed physiological signals.…”

Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries

“…Gaussian process classification model: Gaussian process classification (GPC) models are a class of machine learning models which are based on non-parametric Bayesian formulation. In GPC settings, a latent variable that represents the classification logit is defined and a prior distribution is placed over the latent space in the form of a Gaussian process (GP) [ 43 , 44 ]. We used the Gaussian Processes for Machine Learning (GPML) toolbox to implement the GPC model training in this study [ 45 ].…”