The ECG arrhythmia database is the image version of this dataset(https://www.kaggle.com/shayanfazeli/heartbeat).
Contex

ECG Arrhythmia Image Dataset

Abstract

This dataset is composed of two collections of heartbeat signals derived from two famous datasets in heartbeat classification, the MIT-BIH Arrhythmia Dataset and The PTB Diagnostic ECG Database. The number of samples in both collections is large enough for training a deep neural network.

This dataset has been used in exploring heartbeat classification using deep neural network architectures, and observing some of the capabilities of transfer learning on it. The signals correspond to electrocardiogram (ECG) shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. These signals are preprocessed and segmented, with each segment corresponding to a heartbeat.
Content

Arrhythmia Dataset

Number of Samples: 109446
Number of Categories: 5
Sampling Frequency: 125Hz
Data Source: Physionet's MIT-BIH Arrhythmia Dataset
Classes: ['N': 0, 'S': 1, 'V': 2, 'F': 3, 'Q': 4]

The PTB Diagnostic ECG Database
Number of Samples: 14552
Number of Categories: 2
Sampling Frequency: 125Hz
Data Source: Physionet's PTB Diagnostic Database

Data Files
This dataset is created by saving the each ECG arrhythmia into the image form. Then total images from each classes are divided into train and test data where training samples are a80% of the total data and test samples are 20%. 
Acknowledgements
Mohammad Kachuee, Shayan Fazeli, and Majid Sarrafzadeh. "ECG Heartbeat Classification: A Deep Transferable Representation." arXiv preprint arXiv:1805.00794 (2018).
Inspiration
Can you use GAN to generate more number of imbalance class images?