A Transformer Architecture for Stress Detection from ECG
This work addresses stress detection for healthcare applications, but it is incremental as it builds on existing transformer and convolutional methods for ECG analysis.
The paper tackles stress detection from ECG signals by proposing a deep neural network combining convolutional layers and a transformer mechanism, achieving results comparable or better than state-of-the-art models on the WESAD and SWELL-KW datasets.
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.