Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine.

Robert Czabanski,Jacek Leski,Adam Matonia,Radek Martinek,Tomasz Kupka,Krzysztof Horoba,Radana Kahankova,Janusz Jezewski,Michal Jezewski,Janusz Wrobel

doi:10.3390/s20030765

Abstract

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

Highlights

Atrial fibrillation (AF) is the most common heart arrhythmia, which occurs when the atria contracts quickly and irregularly at rates of 400 to 600 per minute
ECG records has been described in this paper, that represents a new approach from the machine hasrecords been described in this paper, that represents a new approach derivedderived from the machine learning learning principles – the Lagrangian Support Vector Machine (LSVM)
The objectivity and efficiency of the visual analysis of long-term recordings can be improved by automated AF detection

Summary

Introduction

Atrial fibrillation (AF) is the most common heart arrhythmia, which occurs when the atria contracts quickly and irregularly at rates of 400 to 600 per minute. These contractions are independent from ventricles, which themselves operate at much lower rate. AF symptoms often include palpitations, irregular heartbeat, shortness of breath, chest pains and others, but they can be asymptomatic and is called silent AF. The frequency of AF occurrence is strictly correlated with the patient’s age [1,2]. The prognosis indicates that the AF occurrence within the period of the 20–30 years will.

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