HRV Signal Feature Estimation and Classification for Healthcare System Based on Machine Learning

Abstract

A system for heartbeat classification is provided a solution against the mortality due to cardiac arrest. Here, an automated real-time system proposed to identify and classify the abnormalities in the electrocardiogram (ECG) signal or its variability. In healthcare services, multiple monitoring systems are available to monitor cardiac health conditions. Although, efficient monitoring and the alert system needed for prevention from any health loss or mortality. In this work, a public database is utilized named MIT-BIH Arrhythmia (MITA) ECG Database and Normal Sinus Rhythm Database (NSRDB). In the classification process features play a key role to identify the class of the signal that may indicate the particular health condition. Here, a statistical technique is used for the analysis of HRV data of ECG signal and decision tree for classification of parameters extracted from HRV signals. In this paper, a method developed for arrhythmia detection using time and time-frequency domain statistical features. Therefore, five statistical parameters of HRV signals were computed and considered as features of normal and arrhythmia HRV signals, used in training and test data in the decision tree classifiers. The use of multiple sizes of training dataset gives accuracy variation as well as the classification rate. As per the comparison of both feature sets and training dataset, Time-frequency feature is efficiently employable for identification of signal class that represents the cardiac health condition, its accuracy in classification reached up to 99.2% with 20:80 data distribution as a training and testing dataset.