Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

TL;DR

This paper introduces a multi-channel deep learning framework combining CNN and LSTM to objectively assess neonatal pain from videos by analyzing facial expressions and body movements, improving accuracy over existing methods.

Contribution

The paper presents a novel multi-channel neural network that integrates facial and body movement analysis with temporal modeling for neonatal pain assessment.

Findings

The proposed framework outperforms existing methods in accuracy.

Multi-channel analysis improves pain detection reliability.

Temporal modeling enhances assessment consistency.

Abstract

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.