title: Multivariate classification of systemic vascular resistance using photoplethysmography.
abstract: Systemic vascular resistance (SVR) classification is useful for the diagnosis and prognosis of critical pathophysiological conditions, with the ability to identify patients with abnormally high or low SVR of immense clinical value. In this study, a supervised classifier, based on Bayes' rule, is employed to classify a heterogeneous group of intensive care unit patients (N = 48) as being below (SVR < 900 dyn s cm(-5)), within (900 ⩽ SVR ⩽ 1200 dyn s cm(-5)) or above (SVR > 1200 dyn s cm(-5)) the clinically accepted range for normal SVR. Features derived from the finger photoplethysmogram (PPG) waveform and other routine cardiovascular measurements (heart rate and mean arterial pressure) were used as inputs to the classifier. In the construction of the classifier model, two techniques were used to approximate the class conditional probability densities--a single Gaussian distribution model (also known as discriminant analysis) and a non-parametric model using the Parzen window kernel density estimation method. An exhaustive feature search was performed to select a feature subset that maximized the performance indicator, Cohen's kappa coefficient (κ). The Gaussian model with multiple features achieved the best overall kappa coefficient (κ = 0.57), although the results from the non-parametric model were comparable (κ = 0.51). The optimum subset in the Gaussian model consisted of PPG waveform variability features, including the low-frequency to high-frequency ratio (LF/HF) and the normalized mid-frequency power (MF(NU)), in addition to the PPG pulse wave features, such as pulse width, peak-to-notch time, reflection index, and notch time ratio. The classifier performed particularly well in discriminating low SVR, with a sensitivity of 85%, specificity of 86%, positive predictive value of 88% and a negative predictive value of 82%. The results highlight the feasibility of deploying a multivariate statistical approach of SVR classification in the clinical setting, simply using a non-invasive and easy-to-measure PPG waveform signal.
authors.affiliation: Biomedical Systems Laboratory, School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Qim Y
authors.initials: QY
authors.lastname: Lee
authors.forename: Gregory S H
authors.initials: GS
authors.lastname: Chan
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.forename: Paul M
authors.initials: PM
authors.lastname: Middleton
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Steel
authors.forename: Philip
authors.initials: P
authors.lastname: Malouf
authors.forename: Christopher
authors.initials: C
authors.lastname: Critoph
authors.forename: Gordon
authors.initials: G
authors.lastname: Flynn
authors.forename: Emma
authors.initials: E
authors.lastname: O'Lone
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
mesh_terms: D000368:Aged; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D015999:Multivariate Analysis; D017156:Photoplethysmography / Q000379:methods*; D011237:Predictive Value of Tests; D014655:Vascular Resistance / Q000502:physiology*; D058067:Wavelet Analysis