Towards a mobile and wearable system for predicting panic attacks

Abstract

In this paper, we present first steps towards a mobile and wearable system intended to help people who experience regular and spontaneous panic attacks due to panic disorder. The goal of the system is to predict oncoming panic attacks and to deliver in-the-moment interventions on a smartphone device. Interventions are intended to reduce symptom severity by enabling a user to respond to approaching panic episodes. An initial feasibility study is described where a small real-world data set was collected. Personalized prediction models were trained which take, as input, physiological data and output a binary classification of either pre-panic or non-panic. We demonstrate proof-of-concept of episode prediction on this small dataset.