A Transformer Architecture for Stress Detection from ECG

• URL: http://arxiv.org/abs/2108.09737v1
• Date: Sun, 22 Aug 2021 14:34:44 GMT
• Title: A Transformer Architecture for Stress Detection from ECG
• Authors: Behnam Behinaein, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, Ali Etemad
• Abstract summary: We present a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. 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.
• Score: 7.559720049837459
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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