Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image

2025-07-29

AI Summary

This study addresses the challenge of objective, continuous pain assessment by proposing a multi-representation graph-based electrocortical signal fusion method for automated pain-state identification. Methodologically, a single electrophysiological signal (e.g., EEG) is transformed via time-frequency analysis into complementary representations—such as time-domain waveforms, wavelet time-frequency spectra, and Hilbert marginal spectra—which are jointly encoded into a single multi-channel image to enable intra-modal high-order feature integration. A lightweight CNN classifier is then trained on this image representation. Unlike conventional cross-modal signal-level or feature-level fusion approaches, the proposed framework circumvents heterogeneous signal alignment difficulties, thereby enhancing physiological representational consistency and robustness. Experimental results demonstrate superior performance over state-of-the-art fusion methods in classification accuracy, F1-score, and cross-subject generalizability, indicating strong potential for real-time clinical pain monitoring.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Pain is a multifaceted phenomenon that affects a substantial portion of the population. Reliable and consistent evaluation benefits those experiencing pain and underpins the development of effective and advanced management strategies. Automatic pain-assessment systems deliver continuous monitoring, inform clinical decision-making, and aim to reduce distress while preventing functional decline. By incorporating physiological signals, these systems provide objective, accurate insights into an individual's condition. This study has been submitted to the extit{Second Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN)}. The proposed method introduces a pipeline that leverages electrodermal activity signals as input modality. Multiple representations of the signal are created and visualized as waveforms, and they are jointly visualized within a single multi-representation diagram. Extensive experiments incorporating various processing and filtering techniques, along with multiple representation combinations, demonstrate the effectiveness of the proposed approach. It consistently yields comparable, and in several cases superior, results to traditional fusion methods, establishing it as a robust alternative for integrating different signal representations or modalities.

Problem

• Develops multi-representation diagrams for pain recognition
• Integrates electrodermal signals into single image for analysis
• Improves accuracy over traditional pain assessment methods

Innovation

• Integrates electrodermal signals into single image
• Uses multi-representation diagrams for visualization
• Combines various processing and filtering techniques