# Stress Assessment with Convolutional Neural Network Using PPG Signals

arXiv: https://arxiv.org/abs/2410.12273 · HTML: https://mlsanity.com/paper/2410.12273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)

## Built on by (most cited first, 3 of 3)

- [Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings](https://mlsanity.com/paper/2502.01108/) (2025)
- [Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images](https://mlsanity.com/paper/2509.13636/) (2025)
- [Artifact Annotations Partially Substitute for Per-User Calibration: SAFE-EDA and a Normalization-Controlled Evaluation of Wrist-EDA Affect Recognition](https://mlsanity.com/paper/2610.01692/) (2026)

## Abstract (arXiv)

Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%.