Feature Extraction of ECG Signals using NI LabVIEW Biomedical Workbench and Classification with Artificial Neural Network

Abstract

In this study, a data set containing normal and different heart beat types recorded by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) was used for the detection of cardiac dysfunctions. In this data set, features were extracted using the LabVIEW Biomedical Workbench from the normal heartbeat and six different arrhythmia types. Obtained signals were evaluated by using Artificial Neural Network multiple classification method. Classification performances were compared before extracting the feature on the same data set. Classifier performances were evaluated by accuracy, sensitivity and selectivity performances criteria of classification. In the classifier performances, the "Normal" beat rate was found to be 99% accurate with the highest success compared to other arrhythmia types. As a result, both analysis methods are successful, but when the LabVIEW Biomedical Workbench is used, the classification results have achieved higher success.