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Electrocardiogram (ECG) has been widely used for emotion recognition. Thispaper presents a deep neural network based on convolutional layers and atransformer mechanism to detect stress using ECG signals. We performleave-one-subject-out experiments on two publicly available datasets, WESAD andSWELL-KW, to evaluate our method. Our experiments show that the proposed modelachieves strong results, comparable or better than the state-of-the-art modelsfor ECG-based stress detection on these two datasets. Moreover, our method isend-to-end, does not require handcrafted features, and can learn robustrepresentations with only a few convolutional blocks and the transformercomponent.

A Transformer Architecture for Stress Detection from ECG