Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning

dc.contributor.authorAguet, Clémentine
dc.contributor.authorJorge, João
dc.contributor.authorZaen, Jérôme Van
dc.contributor.authorProença, Martin
dc.contributor.authorBonnier, Guillaume
dc.contributor.authorFrossard, Pascal
dc.contributor.authorLemay, Mathieu
dc.date.accessioned2023-06-20T07:47:57Z
dc.date.available2023-06-20T07:47:57Z
dc.date.issued2023-02-03
dc.description.abstractBlood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.
dc.identifier.citationPLOS ONE, vol. 18 (2), pp. e0279419
dc.identifier.doi10.1371/journal.pone.0279419
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1237
dc.identifier.urlhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279419
dc.titleBlood pressure monitoring during anesthesia induction using PPG morphology features and machine learning
dc.typeJournal Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Health
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