An overview on machine learning methods for ECG Heartbeat Arrhythmia Classification

The automated classifiers can assist cardiology experts in diagnosing heart-linked illnesses with the initial and well-predicted results of classified ECG signals and data of subjects. This work serves as a review of various research works on ECG data and signals classification using supervised machine learning approaches. We presented and reviewed, the performance and the strategies used to classify cardiac arrhythmias using five widely used classifiers, Support vector machine (SVM), Random forest (RF), Decision three (DT), Naive Bayes (NB), and K-nearest neighbour (KNN). And we discussed a variety of limitations and revealed that there is still area for increase in the classification's performance, specially by minimizing the preprocessing and feature extraction step which can cause an important computational cost and different accuracy outcomes. Such a coordinated research review allows scientists to merge an unobstructed view on some aspects of ECG classification for the identification of the research issues unmet so far. We plan to consider the obstacles discussed and some limitations to further raise the classification performance of the automated classifiers for the applicability of such solutions in clinical diagnosis.