Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors

TL;DR

This paper introduces a multi-task neural network that estimates pain intensity from electrocardiogram signals while incorporating demographic factors like age and gender to improve accuracy.

Contribution

It presents a novel multi-task neural network model that leverages demographic data alongside ECG signals for more reliable pain estimation.

Findings

The model outperforms existing approaches in pain intensity estimation.

Demographic factors improve the accuracy of pain recognition.

Electrocardiogram signals reveal variations in pain perception among different groups.

Abstract

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches.