A Transformer Architecture for Stress Detection from ECG

TL;DR

This paper introduces a novel deep neural network combining convolutional layers and a transformer to detect stress from ECG signals, achieving state-of-the-art results without handcrafted features.

Contribution

It presents an end-to-end model that effectively captures stress-related features from ECG data using a lightweight architecture with convolutional and transformer components.

Findings

Achieves comparable or superior performance to existing models on WESAD and SWELL-KW datasets.

Does not require handcrafted features, simplifying the stress detection pipeline.

Demonstrates robustness with minimal convolutional layers and a transformer mechanism.

Abstract

Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evaluate our method. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-the-art models for ECG-based stress detection on these two datasets. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations with only a few convolutional blocks and the transformer component.