Non-invasive sepsis patient classification using least squares support vector machine

BIOSIGNALS 2009 - Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing

Volumn , Issue , 2009, Pages 406-410

  Tang, Collin H H ^a   Savkin, Andrey V ^a   Middleton, Paul M ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Photoplethysmography; Power spectral analysis; Severe sepsis; Support vector machine; Systemic inflammatory response syndrome

Indexed keywords

AGE FACTORS; CLASSIFICATION ACCURACY; CROSS-VALIDATION TECHNIQUE; DISTINCTIVE FEATURES; GAUSSIAN RADIAL BASIS FUNCTIONS; INFLAMMATORY RESPONSE; INPUT SPACE; LEAST SQUARES SUPPORT VECTOR MACHINES; LOW FREQUENCY; NEURAL ACTIVITY; NON-INVASIVE; PHOTOPLETHYSMOGRAPHY; POWER SPECTRAL ANALYSIS; PULSE TRANSIT TIME; RBF KERNELS; SEVERE SEPSIS; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; SYSTEMIC INFLAMMATORY RESPONSE SYNDROMES;

IMAGE RETRIEVAL; RADIAL BASIS FUNCTION NETWORKS; SIGNAL PROCESSING; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS; SUPPORT VECTOR MACHINES; TIME SERIES; VECTORS;

MACHINE COMPONENTS;

EID: 67650566581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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