NON-INVASIVE SEPSIS PATIENT CLASSIFICATION USING LEAST SQUARES SUPPORT VECTOR MACHINE

Collin H. H. Tang, Andrey V. Savkin, Paul M. Middleton

Abstract

Sepsis is a systemic inflammatory response to serious infection. Without proper identification and treatment at its early stage, this syndrome can deteriorate within hours to a more devastating state. In this paper, it was hypothesized that early identification of sepsis stages can be achieved through the evaluation of patients’ autonomic neural activity by means of power spectral analysis. Least squares support vector machine (LSSVM) was utilized to classify sepsis patients into systemic inflammatory response syndrome (SIRS) and severe sepsis groups, based on the measured normalized low-frequency (LFn) components of heard period (RRi) and pulse transit time (PTT) time series. Polar-like transformation of LFn pair of RRi and PTT provides another two distinctive features into the construction of input space. Age factor was also used as an attribute in sepsis classification. The performance of the proposed LSSVM with two different kernels: cubic-polynomial and Gaussian radial basis function (RBF), was evaluated using 5-fold cross-validation technique. From the study, LSSVM with RBF kernel was found to be an effective classifier in the identification of sepsis syndrome progression, with the classification accuracy, sensitivity, and specificity: 93.32%, 99.87%, and 79.29% respectively.

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Paper Citation


in Harvard Style

H. H. Tang C., V. Savkin A. and M. Middleton P. (2009). NON-INVASIVE SEPSIS PATIENT CLASSIFICATION USING LEAST SQUARES SUPPORT VECTOR MACHINE . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 406-410. DOI: 10.5220/0001775704060410


in Bibtex Style

@conference{biosignals09,
author={Collin H. H. Tang and Andrey V. Savkin and Paul M. Middleton},
title={NON-INVASIVE SEPSIS PATIENT CLASSIFICATION USING LEAST SQUARES SUPPORT VECTOR MACHINE},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={406-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001775704060410},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - NON-INVASIVE SEPSIS PATIENT CLASSIFICATION USING LEAST SQUARES SUPPORT VECTOR MACHINE
SN - 978-989-8111-65-4
AU - H. H. Tang C.
AU - V. Savkin A.
AU - M. Middleton P.
PY - 2009
SP - 406
EP - 410
DO - 10.5220/0001775704060410