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Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology

Abstract

Purpose

To develop and validate a continuous non-invasive blood pressure (BP) monitoring system using photoplethysmography (PPG) technology through pulse oximetry (PO).

Methods

This prospective study was conducted at a critical care department and post-anesthesia care unit of a university teaching hospital. Inclusion criteria were critically ill adult patients undergoing invasive BP measurement with an arterial catheter and PO monitoring. Exclusion criteria were arrhythmia, imminent death condition, and disturbances in the arterial or the PPG curve morphology. Arterial BP and finger PO waves were recorded simultaneously for 30 min. Systolic arterial pressure (SAP), mean arterial pressure (MAP), and diastolic arterial pressure (DAP) were extracted from computer-assisted arterial pulse wave analysis. Inherent traits of both waves were used to construct a regression model with a Deep Belief Network-Restricted Boltzmann Machine (DBN-RBM) from a training cohort of patients and in order to infer BP values from the PO wave. Bland–Altman analysis was performed.

Results

A total of 707 patients were enrolled, of whom 135 were excluded. Of the 572 studied, 525 were assigned to the training cohort (TC) and 47 to the validation cohort (VC). After data processing, 53,708 frames were obtained from the TC and 7,715 frames from the VC. The mean prediction biases were −2.98 ± 19.35, −3.38 ± 10.35, and −3.65 ± 8.69 mmHg for SAP, MAP, and DAP respectively.

Conclusions

BP can be inferred from PPG using DBN-RBM modeling techniques. The results obtained with this technology are promising, but its intrinsic variability and its wide limits of agreement do not allow clinical application at this time.

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Fig. 1

Abbreviations

AAMI:

Association for the Advancement of Medical Instrumentation

ANOVA:

Analysis of variance

APACHE II:

Acute Physiology and Chronic Health Evaluation II

BP:

Blood pressure

DAP:

Diastolic arterial pressure

DBN-RBM:

Deep Belief Network-Restricted Boltzmann Machines

GE:

General Electric

MAP:

Mean arterial pressure

PO:

Pulse oximetry

PPG:

Photoplethysmography

SAP:

Systolic arterial pressure

SOFA:

Sequential Organ Failure Assessment

TC:

Training cohort

VC:

Validation cohort

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Acknowledgments

This work was supported in part by Sabirmedical SL (Parc Cientific de Barcelona, Barcelona, Spain), and by “Avanza” grants (Ministry of Industry, Tourism and Trade, Government of Spain, TSI-020100-2009-204 and TSI-020100-2010-625).

Conflicts of interest

J.C. Ruiz-Rodriguez and J. Caballero have received grant support for travel expenses from Sabirmedical SL. J.C. Ruiz-Rodriguez, J. Caballero, A. Ruiz-Sanmartin, J. Riera, S. García-Roche, and J. Rello have collaborated with Sabirmedical SL in other research projects or in an advisory capacity. V. Ribas was the scientific director of Sabirmedical. O. de Sola-Morales was Sabirmedical’s medical and market access director. No other authors report any financial disclosures.

Author information

Correspondence to Juan C. Ruiz-Rodríguez.

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Ruiz-Rodríguez, J.C., Ruiz-Sanmartín, A., Ribas, V. et al. Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology. Intensive Care Med 39, 1618–1625 (2013). https://doi.org/10.1007/s00134-013-2964-2

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Keywords

  • Non-invasive hemodynamic monitoring
  • Blood pressure
  • Photoplethysmography
  • Critical care
  • Machine learning