Affiliations: [a]
Department of Mathematics, University of Balochistan, Quetta, Pakistan
| [b]
Department of Computer Science and IT, University of Balochistan, Quetta, Pakistan
| [c] LIG - Grenoble Informatics Laboratory, University of Grenoble Alpes, Grenoble, France
Correspondence:
[*]
Corresponding author. Imran Sabir, Department of Mathematics, University of Balochistan, Quetta, Pakistan. E-mail: isabir1963@yahoo.com.
Abstract: Electrocardiogram (ECG) data recorded by medical devices are hard to analyze manually. Therefore, it is important to analyze and categorize each heartbeat using machine learning. Recently, advancements in machine learning have made classification of complex data easy and fast. However, these machine learning algorithms require sufficient amount of training data and have limited performance in case the data is imbalance. In case of MIT-BIH arrhythmia dataset, the distribution of training instances are quite imbalance. Many machine learning, particularly deep learning, algorithms give high accuracy on these datasets but still the minority classes have zero accuracy. In this paper, we improve the accuracy of minority classes without hurting the overall accuracy of other classes using transfer learning. The accuracy of existing deep learning model is increased from 90.67% to 98.47%, respectively.
Keywords: Transfer learning, deep learning, imbalance dataset
