Application of Convolutional Neural Network and Deep Learning for Detection of Cardiac Arrhythmia Heart Disease

Abstract

The goal of this paper is apply convolutional neural networks to Electrocardiogram signals to detect cardiac arrhythmia, which is a form of heart diseases. The new high-tech Electrocardiogram sensors and other medical devices have significantly improved the quantity and quality of Electrocardiogram recordings in high volume. Futhermore, the availability of high computing GPUs have made it easy to process large amout of data in a short amount of time. We have developed a method for Electrocardiogram arrhythmia classification which converts Electrocardiogram signals to two dimentional images to be processed with  convolutional neural networks, which is a form of deep machine learning. Deep learning has been proven to be an effective means for complex data analysis with minimal pre- and post-processing requirement. It is the primary tool in this research. We use the proposed convolutional neural networks architecture for classifying cardica arrhythmia into three distinct categories: normal sinus rhythm, paced rhythm, and other rhythm. The Electrocardiogram signal is converted into a two-dimensional grayscale image and used as an input data for the convolutional neural networks classifier. We use various deep learning techniques such as batch normalization, data augmentation, and averaging-based feature aggregation across time. We use several image crop techniques for data augmentation and K fold cross validation for overcoming over-fitting. The proposed classifier can reach a classification accuracy of over 95% on the data we acquired from PhysioNet/CinC Challenge 2017.

Keywords: Deep machine learning, electrocardiogram (ECG), arrhythmia, convolutional neural network (CNN), data augmentation