Emotional States versus Mental Heart Rate Component Monitored via Wearables

Abstract

Psychiatric illnesses are estimated to account for over 15% of the burden of disease, which is more than all kinds of cancer together. Since mental disease is often preceded by issues in emotion processing, a method to objectively measure emotions in daily life would be needed. The goal of this research is to investigate the possibilities of mental heart rate component, assessed with a real-time individualized algorithm that decomposes total heart rate in a physical, basal, and mental component, to classify discrete emotions. For this aim, twenty participants committed to wearing a wristband 24/7 for three months and to label the occurrence of fourteen emotions on their smartphones. In total, 1255 labels were added. The dynamics of the mental heart rate component responses to emotions were identified via data-based mechanistic transfer function models. For the classification, the numerator and denominator model orders and parameters, the four features that define transfer function models, were used as features in a support vector machine classifier. This resulted in an average classification accuracy of the mental heart rate responses of 51.1% over all participants, compared to a random classifier with an average accuracy of 28.5%. We concluded that the dynamics of emotions are not only highly variable between individuals, but that they are also time varying on an individual basis. To increase accuracy, more and higher quality labels are indispensable.