RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning
Advancements in lung health assessment, a critical component in diagnosing respiratory conditions, have gained prominence in medical research. This is especially true with the advent of non-invasive techniques such as spirometry. Central to this diagnostic method are three key metrics: Forced Vital...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10473011/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582303099912192 |
|---|---|
| author | Ahmed Sharshar Muhammad Sharshar Hosam Elhady Ahmed Aboeitta Youssef Nafea Yasser Ashraf Mohammad Yaqub Mohsen Guizani |
| author_facet | Ahmed Sharshar Muhammad Sharshar Hosam Elhady Ahmed Aboeitta Youssef Nafea Yasser Ashraf Mohammad Yaqub Mohsen Guizani |
| author_sort | Ahmed Sharshar |
| collection | DOAJ |
| description | Advancements in lung health assessment, a critical component in diagnosing respiratory conditions, have gained prominence in medical research. This is especially true with the advent of non-invasive techniques such as spirometry. Central to this diagnostic method are three key metrics: Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). In light of the increasing need for accurate and reliable assessment tools, developing comprehensive datasets is imperative for advancing research in this field. Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, a total of more than 2k samples, compiled from 60 participants, covering a wide range of age, weight, height and others. This dataset incorporates various data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. We elaborate on the data collection methodology, post-processing techniques employed, and the analytical approach to extract meaningful patterns and insights. Our evaluation, using a 5-fold cross-validation method on binary classification models for FEV1/FVC, revealed remarkable accuracies: 99.7% for the RGB model and a perfect 100% for the thermal model. For the PEF 3-class model, accuracies were 97.14% for both RGB and 96.0% for thermal models. This study aims to underscore the dataset’s potential in establishing and enhancing a robust deep-learning model for lung health diagnostics. |
| format | Article |
| id | doaj-art-5ed9d6d9bfb4406bb89e2536fd93638e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5ed9d6d9bfb4406bb89e2536fd93638e2025-01-30T00:01:13ZengIEEEIEEE Access2169-35362024-01-0112426144262810.1109/ACCESS.2024.337765910473011RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep LearningAhmed Sharshar0https://orcid.org/0000-0003-2280-5240Muhammad Sharshar1https://orcid.org/0009-0004-9267-0120Hosam Elhady2https://orcid.org/0009-0003-8396-842XAhmed Aboeitta3Youssef Nafea4https://orcid.org/0000-0002-6286-8708Yasser Ashraf5https://orcid.org/0000-0002-9350-5774Mohammad Yaqub6https://orcid.org/0000-0001-6896-1105Mohsen Guizani7https://orcid.org/0000-0002-8972-8094Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesCyber-Physical Lab, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, EgyptCyber-Physical Lab, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, EgyptDepartment of Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesDepartment of Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesDepartment of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesDepartment of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesDepartment of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab EmiratesAdvancements in lung health assessment, a critical component in diagnosing respiratory conditions, have gained prominence in medical research. This is especially true with the advent of non-invasive techniques such as spirometry. Central to this diagnostic method are three key metrics: Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). In light of the increasing need for accurate and reliable assessment tools, developing comprehensive datasets is imperative for advancing research in this field. Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, a total of more than 2k samples, compiled from 60 participants, covering a wide range of age, weight, height and others. This dataset incorporates various data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. We elaborate on the data collection methodology, post-processing techniques employed, and the analytical approach to extract meaningful patterns and insights. Our evaluation, using a 5-fold cross-validation method on binary classification models for FEV1/FVC, revealed remarkable accuracies: 99.7% for the RGB model and a perfect 100% for the thermal model. For the PEF 3-class model, accuracies were 97.14% for both RGB and 96.0% for thermal models. This study aims to underscore the dataset’s potential in establishing and enhancing a robust deep-learning model for lung health diagnostics.https://ieeexplore.ieee.org/document/10473011/Datasetdeep learninglung healthremote spirometryRGB videossmart healthcare |
| spellingShingle | Ahmed Sharshar Muhammad Sharshar Hosam Elhady Ahmed Aboeitta Youssef Nafea Yasser Ashraf Mohammad Yaqub Mohsen Guizani RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning IEEE Access Dataset deep learning lung health remote spirometry RGB videos smart healthcare |
| title | RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning |
| title_full | RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning |
| title_fullStr | RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning |
| title_full_unstemmed | RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning |
| title_short | RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning |
| title_sort | respirodynamics a multifaceted dataset for enhanced lung health assessment using deep learning |
| topic | Dataset deep learning lung health remote spirometry RGB videos smart healthcare |
| url | https://ieeexplore.ieee.org/document/10473011/ |
| work_keys_str_mv | AT ahmedsharshar respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT muhammadsharshar respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT hosamelhady respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT ahmedaboeitta respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT youssefnafea respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT yasserashraf respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT mohammadyaqub respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning AT mohsenguizani respirodynamicsamultifaceteddatasetforenhancedlunghealthassessmentusingdeeplearning |