Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress.

Wonju Seo,Chanhee Lee,Sung-Min Park,Sehyeon Kim,Namho Kim

doi:10.3390/s19133021

Abstract

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

Highlights

Mental health is being recognized as an important issue in the workplace [1]
The average score was lower for the hard Stroop task than for the easy one, possibly because easy but tedious tasks may be more stressful than difficult tasks
We have proposed the first end-to-end deep learning approach to stress recognition based on ECG and RESP data

Summary

Introduction

Mental health is being recognized as an important issue in the workplace [1]. If mental stress is not treated in a timely manner (i.e., left unmanaged), employees can experience serious physical problems, such as heart disorders, diabetes, cancer, and stomachaches [2,3]. Stress causes mental disorders such as depression and anger, and can even lead to suicide [2,4] Such problems can seriously reduce productivity owing to absences and work disability [1], with the medical and socioeconomic costs in the United States adding up to $300 billion annually [5]. Stress is typically evaluated using a stress indicator questionnaire, where individuals answer questions such as the perceived stress scale (PSS) [6] and sleep quality assessment (PSQI) [7], and healthcare professionals evaluate the stress score based on those answers Because these methods rely on expert evaluations, they are not suitable for continuously monitoring stress in the workplace

Methods

Results

Discussion

Conclusion