Classification of Respiratory Diseases
Contactless measurement methods offer a novel approach to assessing respiratory parameters. This study investigates the feasibility of classifying chronic obstructive pulmonary disease, asthma, and healthy individuals using depth-based plethysmography (DPG). The approach involves calculating Pearson...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-12-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2024-2163 |
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| Summary: | Contactless measurement methods offer a novel approach to assessing respiratory parameters. This study investigates the feasibility of classifying chronic obstructive pulmonary disease, asthma, and healthy individuals using depth-based plethysmography (DPG). The approach involves calculating Pearson's correlation coefficient for all pixel-wise signals against each other, with the cumulative result visualized in patient-specific masks. A convolutional neural network is used for the classification process. For evaluation, on a recorded data set (N=53), a classification accuracy of 57.7% and Cohen’s Kappa of 0.28 were reached. These findings provide indications that DPG might effectively classify respiratory conditions by analyzing respiratory motion dynamics. |
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| ISSN: | 2364-5504 |