Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors
This work addresses the need for reliable and unbiased pain assessment in healthcare, though it appears incremental by building on existing methods with demographic integration.
The paper tackled the problem of objective pain intensity estimation by developing a multi-task neural network that uses electrocardiogram signals along with age and gender information, showing advantages over other approaches.
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.