Robust Segmentation of CPR-Induced Capnogram Using U-net: Overcoming Challenges with Deep Learning
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913565783359488 |
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| author | Elola, Andoni Ania, Imanol Jaureguibeitia, Xabier Wang, Henry Nassal, Michelle Idris, Ahamed Aramendi, Elisabete |
| author_facet | Elola, Andoni Ania, Imanol Jaureguibeitia, Xabier Wang, Henry Nassal, Michelle Idris, Ahamed Aramendi, Elisabete |
| contents | Objective: The accurate segmentation of capnograms during cardiopulmonary resuscitation (CPR) is essential for effective patient monitoring and advanced airway management. This study aims to develop a robust algorithm using a U-net architecture to segment capnograms into inhalation and non-inhalation phases, and to demonstrate its superiority over state-of-the-art (SoA) methods in the presence of CPR-induced artifacts.
Materials and methods: A total of 24354 segments of one minute extracted from 1587 patients were used to train and evaluate the model. The proposed U-net architecture was tested using patient-wise 10-fold cross-validation. A set of five features was extracted for clustering analysis to evaluate the algorithm performance across different signal characteristics and contexts. The evaluation metrics included segmentation-level and ventilation-level metrics, including ventilation rate and end-tidal-CO$_2$ values.
Results: The proposed U-net based algorithm achieved an F1-score of 98% for segmentation and 96% for ventilation detection, outperforming existing SoA methods by 4 points. The root mean square error for end-tidal-CO$_2$ and ventilation rate were 1.9 mmHg and 1.1 breaths per minute, respectively.
Detailed performance metrics highlighted the algorithm's robustness against CPR-induced interferences and low amplitude signals. Clustering analysis further demonstrated consistent performance across various signal characteristics.
Conclusion: The proposed U-net based segmentation algorithm improves the accuracy of capnogram analysis during CPR. Its enhanced performance in detecting inhalation phases and ventilation events offers a reliable tool for clinical applications, potentially improving patient outcomes during cardiac arrest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Segmentation of CPR-Induced Capnogram Using U-net: Overcoming Challenges with Deep Learning Elola, Andoni Ania, Imanol Jaureguibeitia, Xabier Wang, Henry Nassal, Michelle Idris, Ahamed Aramendi, Elisabete Quantitative Methods Image and Video Processing Objective: The accurate segmentation of capnograms during cardiopulmonary resuscitation (CPR) is essential for effective patient monitoring and advanced airway management. This study aims to develop a robust algorithm using a U-net architecture to segment capnograms into inhalation and non-inhalation phases, and to demonstrate its superiority over state-of-the-art (SoA) methods in the presence of CPR-induced artifacts. Materials and methods: A total of 24354 segments of one minute extracted from 1587 patients were used to train and evaluate the model. The proposed U-net architecture was tested using patient-wise 10-fold cross-validation. A set of five features was extracted for clustering analysis to evaluate the algorithm performance across different signal characteristics and contexts. The evaluation metrics included segmentation-level and ventilation-level metrics, including ventilation rate and end-tidal-CO$_2$ values. Results: The proposed U-net based algorithm achieved an F1-score of 98% for segmentation and 96% for ventilation detection, outperforming existing SoA methods by 4 points. The root mean square error for end-tidal-CO$_2$ and ventilation rate were 1.9 mmHg and 1.1 breaths per minute, respectively. Detailed performance metrics highlighted the algorithm's robustness against CPR-induced interferences and low amplitude signals. Clustering analysis further demonstrated consistent performance across various signal characteristics. Conclusion: The proposed U-net based segmentation algorithm improves the accuracy of capnogram analysis during CPR. Its enhanced performance in detecting inhalation phases and ventilation events offers a reliable tool for clinical applications, potentially improving patient outcomes during cardiac arrest. |
| title | Robust Segmentation of CPR-Induced Capnogram Using U-net: Overcoming Challenges with Deep Learning |
| topic | Quantitative Methods Image and Video Processing |
| url | https://arxiv.org/abs/2410.20899 |