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A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

In this study, a novel classification method for a driver’s cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver’s...

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Autores principales: Huang, Jing, Luo, Xiong, Peng, Xiaoyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085664/
https://www.ncbi.nlm.nih.gov/pubmed/32121440
http://dx.doi.org/10.3390/s20051340
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author Huang, Jing
Luo, Xiong
Peng, Xiaoyan
author_facet Huang, Jing
Luo, Xiong
Peng, Xiaoyan
author_sort Huang, Jing
collection PubMed
description In this study, a novel classification method for a driver’s cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver’s cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).
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spelling pubmed-70856642020-04-21 A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures Huang, Jing Luo, Xiong Peng, Xiaoyan Sensors (Basel) Article In this study, a novel classification method for a driver’s cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver’s cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019). MDPI 2020-02-29 /pmc/articles/PMC7085664/ /pubmed/32121440 http://dx.doi.org/10.3390/s20051340 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Jing
Luo, Xiong
Peng, Xiaoyan
A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title_full A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title_fullStr A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title_full_unstemmed A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title_short A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
title_sort novel classification method for a driver’s cognitive stress level by transferring interbeat intervals of the ecg signal to pictures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085664/
https://www.ncbi.nlm.nih.gov/pubmed/32121440
http://dx.doi.org/10.3390/s20051340
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