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Machine Learning in ECG Interpretation

DISCOVERIES (ISSN 2359-7232), 2017, July-September issue


Ponomariov V, Chirila L, Apipie FM, Abate R, Rusu M, Wu Z, Liehn EA, Bucur I. Artificial Intelligence versus Doctors’ Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography, Discoveries 2017, Jul-Oct; 5(3): e76. DOI: 10.15190/d.2017.6

Submitted: May 20th, 2017; Revised: Sept. 28th, 2017; Accepted: Sept. 29th, 2017; Published: Sept. 30th, 2017;

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Artificial Intelligence versus Doctors’ Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography

Victor Ponomariov (1,2), Liviu Chirila (3), Florentina-Mihaela Apipie (4,5), Raffaele Abate (3,6), Mihaela Rusu (1), Zhuojun Wu (1,4), Elisa A. Liehn (1,2,7), Ilie Bucur (4, *)

(1) Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University, Germany;

(2) Department of Cardiology, Pulmonology, Angiology and Intensive Care, University Hospital, RWTH Aachen University, Germany;

(3) ECUORE LTD, London, England;

(4) Applied Systems srl, Craiova, Romania;

(5) Faculty of Economic and Business Administration, Doctoral School of Economics, University of Craiova, Romania;

(6) School of Medicine, University of Catania, Italy;

(7) Human Genetic Laboratory, University of Medicine and Pharmacy, Craiova, Romania;

*Corresponding author: Ilie Bucur, PhD, Applied Systems srl, Craiova, Romania; Email: bucur_il@yahoo.com 


Computational machine learning, especially self-enhancing algorithms, prove remarkable effectiveness in applications, including cardiovascular medicine. This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation. In practice, continuous real-time monitoring of electrocardiograms is still difficult to realize. Furthermore, automated ECG interpretation by implementing specific artificial intelligence algorithms is even more challenging. By collecting large datasets from one individual, computational approaches can assure an efficient personalized treatment strategy, such as a correct prediction on patient-specific disease progression, therapeutic success rate and limitations of certain interventions, thus reducing the hospitalization costs and physicians’ workload. Clearly such aims can be achieved by a perfect symbiosis of a multidisciplinary team involving clinicians, researchers and computer scientists. Summarizing, continuous cross-examination between machine intelligence and human intelligence is a combination of precision, rationale and high-throughput scientific engine integrated into a challenging framework of big data science.

Access full text of the manuscript here: 


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