Cho, Hyun-Myung Park, Heesu Dong, Suh-Yeon Youn, Inchan 2024-01-19T19:02:39Z 2024-01-19T19:02:39Z 2021-09-05 2019-10-02 1424-8220 https://pubs.kist.re.kr/handle/201004/119471 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. English MDPI HEART-RATE-VARIABILITY CLASSIFICATION Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network Article 10.3390/s19204408 1 SENSORS, v.19, no.20 SENSORS 19 20 scie scopus 000497864700056 2-s2.0-85073477438 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article HEART-RATE-VARIABILITY CLASSIFICATION stress detection electrocardiogram deep neural network convolutional neural network