Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements

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• 作者：Qim Y Lee (1)
  Stephen J Redmond (2)
  Gregory SH Chan (1)
  Paul M Middleton (2) (3) (5)
  Elizabeth Steel (4)
  Philip Malouf (4)
  Cristopher Critoph (4)
  Gordon Flynn (4)
  Emma O’Lone (4)
  Nigel H Lovell (1) (2)
  
• 关键词：Cardiac output ; Systemic vascular resistance ; Photoplethysmography ; Power spectrum analysis ; Photoplethysmogram variability ; Photoplethysmogram morphology ; Feature selection
• 刊名：BioMedical Engineering OnLine
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：12
• 期：1
• 全文大小：220 KB
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• 作者单位：Qim Y Lee (1)
  Stephen J Redmond (2)
  Gregory SH Chan (1)
  Paul M Middleton (2) (3) (5)
  Elizabeth Steel (4)
  Philip Malouf (4)
  Cristopher Critoph (4)
  Gordon Flynn (4)
  Emma O’Lone (4)
  Nigel H Lovell (1) (2)
  
  1. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, 2052, Australia
  2. Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
  3. Australian Resusitation Council, NSW Branch, Brookvale, NSW, 2100, Australia
  5. Discipline of Emergency Medicine, University of Sydney, Kragujevac, NSW, 2006, Australia
  4. Intensive Care Unit, Prince of Wales Hospital, Sydney, NSW, 2031, Australia
  
• ISSN：1475-925X

文摘

Background Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). Methods Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. Results The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min-1 when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm-5 when only one PPG variability feature was used. Conclusions These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings.