Our heart works nonstop throughout our life. Its failure means the death of the person. Diseases regarding cardiac system are the main cause of death in the whole world. Therefore it is compulsory to diagnose these types of abnormalities before the failure of the heart. For showing all the electrical actions of the cardiac system, the Electrocardiogram (ECG) signal is an easy and highly recommendable mean. Normally the physicians manually examine the ECG heartbeat to analyze the different types of Arrhythmia. But manually working on ECG graphs is not a satisfactory solution due to its non-stationary nature of ECG. Therefore, there is always a need of computer based systems to examine the ECG signals, which is helpful for physicians. For classification of ECG data, there are many techniques which are implemented by different researchers. This survey is focusing on the latest research papers in which machine learning and deep learning classification techniques are applied in different manners. The implemented machine learning techniques are Support Vector Machine, k-NN, Decision Tree, Neural Network, and Extreme Learning Machine. Convolutional Neural Network (CNN) is implemented in various researches, which is a deep learning technique. A CNN is defined as a deep feed-forward artificial neural network that can mine deep features from database automatically. Mostly works were evaluated on MIT-BIH arrhythmia database which is available publically. In this survey, the existing methods are compared according to qualitative factors like purpose of the work, implemented algorithms and results achieved.

Arrhythmia classification, Convolutional Neural Network, Electrocardiogram, Extreme Learning Machine, Support Vector Machine

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