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Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface
Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7312740/ https://www.ncbi.nlm.nih.gov/pubmed/32617117 http://dx.doi.org/10.1155/2020/4930972 |
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author | Qiu, Shi Li, Junjun Cong, Mengdi Wu, Chun Qin, Yan Liang, Ting |
author_facet | Qiu, Shi Li, Junjun Cong, Mengdi Wu, Chun Qin, Yan Liang, Ting |
author_sort | Qiu, Shi |
collection | PubMed |
description | Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules. |
format | Online Article Text |
id | pubmed-7312740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-73127402020-07-01 Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface Qiu, Shi Li, Junjun Cong, Mengdi Wu, Chun Qin, Yan Liang, Ting Comput Math Methods Med Research Article Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules. Hindawi 2020-06-15 /pmc/articles/PMC7312740/ /pubmed/32617117 http://dx.doi.org/10.1155/2020/4930972 Text en Copyright © 2020 Shi Qiu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Qiu, Shi Li, Junjun Cong, Mengdi Wu, Chun Qin, Yan Liang, Ting Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title | Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title_full | Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title_fullStr | Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title_full_unstemmed | Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title_short | Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface |
title_sort | detection of solitary pulmonary nodules based on brain-computer interface |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7312740/ https://www.ncbi.nlm.nih.gov/pubmed/32617117 http://dx.doi.org/10.1155/2020/4930972 |
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