  Many people are currently suffering from heart diseases that can lead to
untimely death. The most common heart abnormality is arrhythmia, which is
simply irregular beating of the heart. A prediction system for the early
intervention and prevention of heart diseases, including cardiovascular
diseases (CDVs) and arrhythmia, is important. This paper introduces the
classification of electrocardiogram (ECG) heartbeats into normal or abnormal.
The approach is based on the combination of swarm optimization algorithms with
a modified PannTompkins algorithm (MPTA) and support vector machines (SVMs).
The MPTA was implemented to remove ECG noise, followed by the application of
the extended features extraction algorithm (EFEA) for ECG feature extraction.
Then, elephant herding optimization (EHO) was used to find a subset of ECG
features from a larger feature pool that provided better classification
performance than that achieved using the whole set. Finally, SVMs were used for
classification. The results show that the EHOSVM approach achieved good
classification results in terms of five statistical indices: accuracy, 93.31%;
sensitivity, 45.49%; precision, 46.45%; F-measure, 45.48%; and specificity,
45.48%. Furthermore, the results demonstrate a clear improvement in accuracy
compared to that of other methods when applied to the MITBIH arrhythmia
database.
