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Open AccessArticle

Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

1
Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea
2
Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Daejeon 02792, Korea
3
Department of Biomedical Science, College of Medicine, Korea University, Seoul 02841, Korea
4
Department of Information Technology Engineering, Sookmyung Women’s University, Seoul 04310, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(20), 4408; https://doi.org/10.3390/s19204408
Received: 24 September 2019 / Revised: 8 October 2019 / Accepted: 10 October 2019 / Published: 11 October 2019
The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life. View Full-Text
Keywords: stress detection; electrocardiogram; deep neural network; convolutional neural network stress detection; electrocardiogram; deep neural network; convolutional neural network
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Cho, H.-M.; Park, H.; Dong, S.-Y.; Youn, I. Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network. Sensors 2019, 19, 4408.

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