Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.

Yunendah Nur Fuadah,Ki Moo Lim

doi:10.3389/fphys.2021.761013

Abstract

Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.

Highlights

Atrial fibrillation (AF) is one of the most common sustained arrhythmias affecting 59.7 million people in 2019, more than two times the number of cases reported in 1990 (Roth et al, 2020)
The broader objective of this study was to develop machine learning algorithms that would improve the accuracy performance achieved by the earlier studies in classifying AF, congestive heart failure (CHF), and normal sinus rhythm (NSR) conditions (Rizal and Hadiyoso, 2015; Hadiyoso and Rizal, 2017)
We applied the discrete wavelet transform (DWT), Hjorth descriptors, and entropy-based features as the feature extraction methods to generate the feature sets and trained them using several classifier algorithms, including support vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN) to recognize a set of features that are associated with a particular condition of ECG signal

Summary

Introduction

Atrial fibrillation (AF) is one of the most common sustained arrhythmias affecting 59.7 million people in 2019, more than two times the number of cases reported in 1990 (Roth et al, 2020). As cardiovascular disorders affect millions of people and potentially lead to death, AF and CHF have become a major public health concern worldwide (Savarese and Lund, 2002). In recent decades, there has been a significant increase in interest in the field of automatic classification of cardiovascular disorders (Sharma et al, 2019; Jeong et al, 2021), including AF and CHF, based on ECG signals and machine learning approaches (Acharya et al, 2008; Rizal and Hadiyoso, 2015; Hadiyoso and Rizal, 2017; Yingthawornsuk and Temsang, 2019; Faust et al, 2020; Krittanawong et al, 2020)

Objectives

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Results

Conclusion