Heart rate variability Based arrhythmia classification using support vector machine

Authors

Bharathi K.N.

Priya K. Chandra

Savitha V.S.

Swathi Suresh G

Abstract

The objective of the project is to classify arrhythmias using Support Vector Machines based on features of HRV. HRV analysis is a powerful tool to assess autonomic function in both health and disease. This study was conducted with a goal to Obtain time and frequency parameters of HRV for the data taken from MIT-BIH arrhythmia database using LABVIEW.Test the significance of the obtained results using analysis of variance.Train the SVM using the obtained data.Predict the respective classes for the testing data.

This project explores techniques for Arrhythmia analysis based on heart rate variability signal in an attempt to develop robust methods for Arrhythmia classification using support vector machines (SVM).

The study was conducted to derive HRV parameters from ECG signals. All time/frequency domain parameters were chosen and program was successfully implemented in classifying the Arrhythmias into respective classes.

The data is collected from the MIT-BIH arrhythmia database. The tachograms is extracted from the ECG signal which is further divided into small segments of 32 segments. They are characterized by MIT-BIH arrhythmia database annotations. Using XVIEW these annotated segments are made readable into LABVIEW. This gives the HRV parameters in time and frequency domain. Using analysis of variance, the features are reduced based on p-value obtained. These features are given to a classifier called SVM, for training and the remaining features for testing. This classifies all the features into the respective classes.

The proposed SVM classifier shows satisfactory performances in discriminating four types of arrhythmia. The discrimination of NSR, VF, PVC and AF classes was found to have an accuracy of 96.5% .