  In this paper, we propose an effective electrocardiogram (ECG) arrhythmia
classification method using a deep two-dimensional convolutional neural network
(CNN) which recently shows outstanding performance in the field of pattern
recognition. Every ECG beat was transformed into a two-dimensional grayscale
image as an input data for the CNN classifier. Optimization of the proposed CNN
classifier includes various deep learning techniques such as batch
normalization, data augmentation, Xavier initialization, and dropout. In
addition, we compared our proposed classifier with two well-known CNN models;
AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were
used for the evaluation of the classifier. As a result, our classifier achieved
99.05% average accuracy with 97.85% average sensitivity. To precisely validate
our CNN classifier, 10-fold cross-validation was performed at the evaluation
which involves every ECG recording as a test data. Our experimental results
have successfully validated that the proposed CNN classifier with the
transformed ECG images can achieve excellent classification accuracy without
any manual pre-processing of the ECG signals such as noise filtering, feature
extraction, and feature reduction.
