An electrocardiogram (ECG) is a time-series signal that is represented by one-dimensional (1-D) data. Higher dimensional representation contains more information that is accessible for feature extraction. Hidden variables such as frequency relation and morphology of segment is not directly accessible in the time domain. In this paper, 1-D time series data is converted into multi-dimensional representation in the form of multichannel 2-D images. Following that, deep learning was used to train a deep neural network based classifier to detect arrhythmias. The results of simulation on testing database demonstrate the effectiveness of the proposed methodology by showing an outstanding classification performance compared to other existing methods and hand-crafted annotations made by certified cardiologists.
@article{arxiv.1904.00138,
title = {On Arrhythmia Detection by Deep Learning and Multidimensional Representation},
author = {K. S. Rajput and S. Wibowo and C. Hao and M. Majmudar},
journal= {arXiv preprint arXiv:1904.00138},
year = {2019}
}