A novel and optimized recurrence plot-based system for ECG beat classification
Category:- Journal; Year:- 2021
Discipline:- Electronics and Communication Engineering Discipline
School:- Science, Engineering & Technology School
Abstract
Cardiac arrhythmia refers to irregularities in heartbeats.
Left undiagnosed arrhythmias can cause severe and potentially fatal
complications. As a result, early finding of such abnormalities is critical.
Electrocardiogram (ECG) is regularly used by medical professionals to diagnose
and differentiate cardiac arrhythmias. As a result, there have been many deep
learning methods over the years in an attempt to automate this process. But
traditional deep learning methods require big training data which often clearly
do not reflect the age, weight and gender spectrum of patients and are prone to
misclassification when data from different demographics is shown. Hence,
temporal features extracted from these datasets are demographically biased.
Consequently, in this paper, we intend to introduce Optimum Recurrence Plot
based Classifier (OptRPC); a dynamical systems-based method of classifying ECG
beats by embedding them in higher dimensions and devising an optimized
recurrence plot. A Convolutional Neural Network architecture is then used to
classify these recurrence plots. The proposed scheme accomplished an overall
accuracy of 98.67% and 98.48% on two benchmark databases and delivered better
performance than the previous state-of-the-art methods.