Earth Mover’s Distance-Based Automated Geometric Visualization/Classification of Electrocardiogram Signals

The tremendous load on the rather archaic medical system in developing countries has necessitated the need to implement artificial intelligence-enabled automated systems to classify different kinds of electrocardiogram (ECG) traces. To this end, we are proposing a novel R-based open-source software with inherent capability to classify different kinds of automated geometric visualizations along with its categorization based upon similarity indices as measured by earth mover’s distance (EMD). This innovative automated software needs verification and validation by clinical practitioners/cardiologists before being implemented to classify large ECG databases to enhance its machine learning capabilities. We anticipate that integration of this robust automated classifier with divergent platforms such as mobile health applications would enable the subjects/patients to continuously monitor the heart rate themselves.

[1]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[2]  Zhen Fang,et al.  False arrhythmia alarm reduction in the intensive care unit using data fusion and machine learning , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[3]  Henggui Zhang,et al.  Effects of Persistent Atrial Fibrillation-Induced Electrical Remodeling on Atrial Electro-Mechanics – Insights from a 3D Model of the Human Atria , 2015, PloS one.

[4]  Moongu Jeon,et al.  Implementation of a portable device for real-time ECG signal analysis , 2014, Biomedical engineering online.

[5]  Nicolas Smith,et al.  Computational methods to reduce uncertainty in the estimation of cardiac conduction properties from electroanatomical recordings , 2014, Medical Image Anal..

[6]  Kawal S. Rhode,et al.  Personalization of Atrial Anatomy and Electrophysiology as a Basis for Clinical Modeling of Radio-Frequency Ablation of Atrial Fibrillation , 2013, IEEE Transactions on Medical Imaging.

[7]  S. L. Cloherty,et al.  Computational Model of Atrial Electrical Activation and Propagation , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Z. Obermeyer,et al.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. , 2016, The New England journal of medicine.

[9]  Hervé Delingette,et al.  Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to eikonal-diffusion models in cardiac electrophysiology. , 2011, Progress in biophysics and molecular biology.

[10]  Lovekesh Vig,et al.  Anomaly detection in ECG time signals via deep long short-term memory networks , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[11]  Hagit Shatkay,et al.  Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification , 2015, IEEE Transactions on NanoBioscience.

[12]  Ana Mincholé,et al.  Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[13]  A. Holden,et al.  Heterogeneous three-dimensional anatomical and electrophysiological model of human atria , 2006, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[15]  Jun Cheng,et al.  A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[16]  Steven Shea,et al.  Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis , 2017, Circulation research.

[17]  Stephan Achenbach,et al.  Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Pablo Laguna,et al.  Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances , 2018, Journal of The Royal Society Interface.