  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.
