RR Interval-based Atrial Fibrillation Detection using Traditional and Ensemble Machine Learning Algorithms (Methodology Article)

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Shrikanth Rao S.K.; Martis Roshan Joy

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Journal Paper

Paper Information

Journal: JOURNAL OF MEDICAL SIGNALS AND SENSORS (JMSS) Year:2023 | Volume:13 | Issue:3 Page(s): 224-232

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Title

RR Interval-based Atrial Fibrillation Detection using Traditional and Ensemble Machine Learning Algorithms (Methodology Article)

Author(s)

Shrikanth Rao S.K. | Martis Roshan Joy | Issue Writer Certificate

Keywords

Atrial fibrillation

area under the curve

C4.5

classification and regression tree

Discrete wavelet transform

Electrocardiogram

Iterative Dichotomiser 3

K-NN

Random Forest

rotation forest

Support Vector Machine

Abstract

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from Electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as classification and regression tree, K Nearest Neighbor, C4. 5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and rotation forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99. 10% with area under the curve of 0. 998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.

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