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Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks
When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional me...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866008/ https://www.ncbi.nlm.nih.gov/pubmed/33513994 http://dx.doi.org/10.3390/s21030846 |
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author | Zhao, Liang Bao, Yu Zhang, Yu Ye, Ruidong Zhang, Aijuan |
author_facet | Zhao, Liang Bao, Yu Zhang, Yu Ye, Ruidong Zhang, Aijuan |
author_sort | Zhao, Liang |
collection | PubMed |
description | When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices. |
format | Online Article Text |
id | pubmed-7866008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78660082021-02-07 Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks Zhao, Liang Bao, Yu Zhang, Yu Ye, Ruidong Zhang, Aijuan Sensors (Basel) Article When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices. MDPI 2021-01-27 /pmc/articles/PMC7866008/ /pubmed/33513994 http://dx.doi.org/10.3390/s21030846 Text en © 2021 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 Zhao, Liang Bao, Yu Zhang, Yu Ye, Ruidong Zhang, Aijuan Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title | Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title_full | Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title_fullStr | Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title_full_unstemmed | Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title_short | Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks |
title_sort | recognition of abnormal chest compression depth using one-dimensional convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866008/ https://www.ncbi.nlm.nih.gov/pubmed/33513994 http://dx.doi.org/10.3390/s21030846 |
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