HRV Analysis for Daignosis: A Review

摘要

For health diagnosis, HRV signals have become a promising tool and is being used for patient monitoring. Some linear and nonlinear analysis need to be done and then it becomes mandatory to have a better classification strategy to predict and classify the patients under test into different classes. These classes can be based on the arrhythmias or some other heart issues. This paper presents a brief review about analysis of HRV signals and then describe some classifiers which proves good for predicting the state of the patients under test. Some of the techniques like neural network based, fuzzy based, and SVM have shown better results in the literature. Some feature reduction techniques are also discussed which are used to reduce the feature numbers to increase the classification accuracy and prediction rate.