Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied
The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions—often under the effect of drugs—it is regul...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1126957/full |
| _version_ | 1828000735377227776 |
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| author | Guillaume Weber-Boisvert Benoit Gosselin Frida Sandberg |
| author_facet | Guillaume Weber-Boisvert Benoit Gosselin Frida Sandberg |
| author_sort | Guillaume Weber-Boisvert |
| collection | DOAJ |
| description | The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions—often under the effect of drugs—it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant p<0.001 differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation ρ > 0.40 to systolic BP in PPG-BP all displayed muted correlation levels ρ < 0.10 in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population. |
| first_indexed | 2024-04-10T06:18:49Z |
| format | Article |
| id | doaj.art-b90287264429459ab6048e5a5b3949fe |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-10T06:18:49Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-b90287264429459ab6048e5a5b3949fe2023-03-02T05:24:02ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-03-011410.3389/fphys.2023.11269571126957Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantiedGuillaume Weber-Boisvert0Benoit Gosselin1Frida Sandberg2Department of Electrical and Computer Engineering, Université Laval, Quebec, QC, CanadaDepartment of Electrical and Computer Engineering, Université Laval, Quebec, QC, CanadaDepartment of Biomedical Engineering, Lund University, Lund, SwedenThe large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions—often under the effect of drugs—it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant p<0.001 differences between the datasets in diastolic BP and in 20 out of 31 features when adjusting for heart rate differences. The eight features showing the highest rank correlation ρ > 0.40 to systolic BP in PPG-BP all displayed muted correlation levels ρ < 0.10 in MIMIC. Regression tests showed twice higher baseline predictive power with PPG-BP than with MIMIC. Cross-dataset regression displayed a practically complete loss of predictive power for all models. The differences between the MIMIC and PPG-BP dataset exposed in this study suggest that BP estimation models based on the MIMIC dataset have reduced predictive power on the general population.https://www.frontiersin.org/articles/10.3389/fphys.2023.1126957/fullblood pressure estimationBP estimationphotoplethysmographymimicUCIPPG-BP |
| spellingShingle | Guillaume Weber-Boisvert Benoit Gosselin Frida Sandberg Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied Frontiers in Physiology blood pressure estimation BP estimation photoplethysmography mimic UCI PPG-BP |
| title | Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied |
| title_full | Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied |
| title_fullStr | Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied |
| title_full_unstemmed | Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied |
| title_short | Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied |
| title_sort | intensive care photoplethysmogram datasets and machine learning for blood pressure estimation generalization not guarantied |
| topic | blood pressure estimation BP estimation photoplethysmography mimic UCI PPG-BP |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1126957/full |
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