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ASA-ED: Automated Stress Assessment Via Emotion-Awareness-Driven Deep Hybrid Learning Fusing MTF and RP.

Mi Li, Yanbo Chen, Junzhe Li

IEEE Journal of Biomedical and Health Informatics

|May 12, 2026

Summary

This study introduces a deep learning framework for continuous mental stress assessment, outperforming previous methods. By converting physiological signals into images, it significantly improves stress level detection accuracy for early mental health identification.

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Psychophysiology

Background:

Current mental stress evaluation primarily uses classification, with limited research on continuous level estimation via deep learning.
Early identification of mental stress is crucial for timely intervention and mental health management.

Purpose of the Study:

To develop an end-to-end deep learning framework for accurate continuous mental stress assessment.
To enhance feature representation of physiological signals for improved stress detection.

Main Methods:

A deep hybrid learning architecture combining efficient channel attention convolution, bidirectional long short-term memory (BiLSTM), and emotional cross-attention.
Encoding pulse rate variability (PRV) and discrete pulse signals (dPS) from Photoplethysmography (PPG) into Markov Transition Field (MTF) and Recurrence Plot (RP) images.

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Utilizing an adaptive ridge stacking ensemble learning method for fusion and prediction.

Main Results:

PRV and dPS representations using MTF and RP images reduced detection errors by up to 15.05% compared to time-domain baselines.
The proposed fusion strategy of PRV-MTF and dPS-RP achieved state-of-the-art performance with MAE = 3.29 and RMSE = 4.05.
This approach significantly reduced Mean Absolute Error (MAE) by 24.88% and Root Mean Square Error (RMSE) by 21.96% compared to prior methods.

Conclusions:

Transforming time-domain physiological signals into structured encoding images effectively captures deep patterns related to psychological states.
The proposed framework significantly enhances the accuracy of continuous mental stress detection, offering a promising tool for mental health monitoring.