ECG arrhythmia classification using a 2-D convolutional neural network

Understand

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.