Authors:
Collin H. H. Tang
1
;
Andrey V. Savkin
2
and
Paul M. Middleton
3
Affiliations:
1
School of Electrical Engineering and Telecommunications, University of New South Wales, Australia
;
2
University of New South Wales, Australia
;
3
Prince of Wales Clinical School, University of New South Wales, Australia
Keyword(s):
Systemic inflammatory response syndrome, Severe sepsis, Support vector machine, Photoplethysmography, Power spectral analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Soft Computing
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 fun
ction (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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