Probabilistic classification approaches for cardiac arrest rhythm interpretation during resuscitation | IEEE Conference Publication | IEEE Xplore

Probabilistic classification approaches for cardiac arrest rhythm interpretation during resuscitation


Abstract:

Our ultimate objective is to develop methodology for resuscitation data analysis that involves monitoring of the patients response, the quality of therapy, and to underst...Show More

Abstract:

Our ultimate objective is to develop methodology for resuscitation data analysis that involves monitoring of the patients response, the quality of therapy, and to understand the interplay between therapy and response. To this end, methods to reliably detect the rhythm types during a resuscitation episode are needed. The objective of this study was to develop machine learning algorithms to recognize the rhythms appearing during a resuscitation episode. In this study, we used a probabilistic framework to classify different cardiac arrest rhythms. We propose two different classifiers; naïve Bayes and logistic regression classifier.
Date of Conference: 22-25 September 2013
Date Added to IEEE Xplore: 16 January 2014
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Conference Location: Zaragoza, Spain

References

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