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Related Experiment Video

Updated: Jan 16, 2026

A Preterm Rat Model for Pain Studies

A Preterm Rat Model for Pain Studies

Published on: February 9, 2024

Enhancing Neonatal Pain Assessment Transparency via Explanatory Training Examples Identification.

Imran Hossain¹, Ghada Zamzmi¹, Peter Mouton²

¹Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems

|September 29, 2025

Summary

This summary is machine-generated.

This study enhances neonatal pain assessment using audio signals by visualizing spectrograms and explaining deep learning decisions. It identifies and removes unhelpful training data, improving model accuracy and trustworthiness in clinical settings.

Keywords:

Deep Neural Networks Explainability Influence Function Neonatal Pain

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Electrophysiological Measurement of Noxious-evoked Brain Activity in Neonates Using a Flat-tip Probe Coupled to Electroencephalography

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Published on: November 29, 2017

Area of Science:

Medical Informatics
Signal Processing
Artificial Intelligence

Background:

Visual data occlusion is common in neonatal pain assessment.
Audio signals offer an alternative for pain detection when visual data is unavailable.
Current deep learning (DL) methods for audio-based pain assessment lack transparency.

Purpose of the Study:

To develop an explainable DL approach for neonatal pain detection from audio signals.
To improve user trust in automated pain assessment systems.
To identify and mitigate the impact of unhelpful training data.

Main Methods:

Visualizing neonatal audio signals as spectrogram images for pain classification.
Implementing an instance-based approach for explaining black-box DL model decisions.
Utilizing an influence score to analyze and remove harmful training instances.

Main Results:

The proposed method successfully visualizes audio signals for pain classification.
Explainability was added to DL-based pain detection, enhancing user trust.
Harmful training instances were detected and removed, leading to a compressed dataset and improved predictions.

Conclusions:

The approach provides a transparent and explainable method for neonatal pain assessment using audio.
This technique can enhance clinical trust in AI-driven pain detection systems.
The study offers a viable strategy for improving pain assessment in real-world clinical environments.