Authors :
Onwuka, Ugochukwu C; Asagba, Prince O
Volume/Issue :
Volume 5 - 2020, Issue 12 - December
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2LgJJrU
Abstract :
Arrhythmia is an abnormal condition of the
heart that occurs when the electrical impulses that
coordinate the heartbeats do not work properly, causing
the heart to beat too fast, too slow, irregular or even have
premature contractions; accurate and quick diagnosis
can reduce fatalities, Thispaper focuses on design and
development of a system that will automatically and
accurately diagnose five super-class of arrhythmias
recommended by theAssociation for the Advancement of
Medical Instrumentation (AAMI) to be detectable by
equipment/methodswhich includes: Normal(N),
Supraventricular ectopicbeat (SVEB), Ventricular ectopic
beat (VEB), Fusion beat (F) and Unknown beat (Q). To
achieve this, the system uses a hybrid classification
algorithm achieved by the combination of two better
performing algorithm (Random Forest and J.48
ensembled with the stacking algorithm). The MIT-BIH
ECG arrhythmia database accessible at Kaggle.com was
used for training, testing and validation of the system.
since this design is intended for edge devices or mobile
devices, the design focuses on development of a system
that uses less computation power, less application size
and ability to give correct classification with less time.
Our results showed that the designed system has an
overall accuracy of 97.67%, average precision value of
0.977, average recall value of 0.977 and average F1-
measure value of 0.977.
Keywords :
Arrhythmia, Machine Learning, Random Forest, J.48, Stacking Ensemble.
Arrhythmia is an abnormal condition of the
heart that occurs when the electrical impulses that
coordinate the heartbeats do not work properly, causing
the heart to beat too fast, too slow, irregular or even have
premature contractions; accurate and quick diagnosis
can reduce fatalities, Thispaper focuses on design and
development of a system that will automatically and
accurately diagnose five super-class of arrhythmias
recommended by theAssociation for the Advancement of
Medical Instrumentation (AAMI) to be detectable by
equipment/methodswhich includes: Normal(N),
Supraventricular ectopicbeat (SVEB), Ventricular ectopic
beat (VEB), Fusion beat (F) and Unknown beat (Q). To
achieve this, the system uses a hybrid classification
algorithm achieved by the combination of two better
performing algorithm (Random Forest and J.48
ensembled with the stacking algorithm). The MIT-BIH
ECG arrhythmia database accessible at Kaggle.com was
used for training, testing and validation of the system.
since this design is intended for edge devices or mobile
devices, the design focuses on development of a system
that uses less computation power, less application size
and ability to give correct classification with less time.
Our results showed that the designed system has an
overall accuracy of 97.67%, average precision value of
0.977, average recall value of 0.977 and average F1-
measure value of 0.977.
Keywords :
Arrhythmia, Machine Learning, Random Forest, J.48, Stacking Ensemble.
