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Physiol Meas. 2010 Jul;31(7):979-93. doi: 10.1088/0967-3334/31/7/008. Epub 2010 Jun 15.

Patients on weaning trials classified with support vector machines.

Author information

1
Department of ESAII, Universitat Polit├ęcnica de Catalunya (UPC), Institute for Bioengineering of Catalonia (IBEC) and the Networking Center of Biomedical Research in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain. Ainara.Garde@upc.edu

Abstract

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h (GSucc); 39 patients who failed the weaning trial (GFail) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h (GRein). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients (GSucc, GFail and GRein) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

PMID:
20551506
DOI:
10.1088/0967-3334/31/7/008
[Indexed for MEDLINE]

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