Machine Learning Approaches for PPG-based Blood Pressure Monitoring: Validation against Invasive Arterial Line Measurements

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dc.contributor.authorJorge, João
dc.contributor.authorProença, Martin
dc.contributor.authorAguet, Clémentine
dc.contributor.authorVan Zaen, Jérôme
dc.contributor.authorBonnier, Guillaume
dc.contributor.authorRenevey, Philippe
dc.contributor.authorLemkaddem, Alia
dc.contributor.authorSchoettker, Patrick
dc.contributor.authorLemay, Mathieu
dc.date.accessioned2025-11-11T15:37:38Z
dc.date.available2025-11-11T15:37:38Z
dc.date.issued2020
dc.description.abstractArterial blood pressure is a physiological parameter of major importance to medical applications. CSEM has developed pioneering techniques for blood pressure estimation based on optical signals such as photoplethysmographic pulse wave analysis (known as oBPM®), which enable continuous non-occlusive blood pressure monitoring. Recently, CSEM explored data-driven approaches using machine learning to enhance the performance of these techniques. The novel methods were tested in the clinical setting and found to outperform previous approaches by up to 15%.
dc.identifier.citationCSEM Scientific and Technical Report 2020, p. 90
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1780
dc.titleMachine Learning Approaches for PPG-based Blood Pressure Monitoring: Validation against Invasive Arterial Line Measurements
dc.typeCSEM Report
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasDigital Health
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