Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Author:

Lyon Aurore1ORCID,Mincholé Ana1,Martínez Juan Pablo2,Laguna Pablo2,Rodriguez Blanca1

Affiliation:

1. Department of Computer Science, British Heart Foundation, Oxford, UK

2. Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, University of Zaragoza, CIBER-BBN, Zaragoza, Spain

Abstract

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

Funder

CompBiomed project

Wellcome Trust

National Centre for the Replacement, Refinement and Reduction of Animals in Research

Ministerio de Economía y Competitividad

British Heart Foundation

Grupo Consolidado BSICoS

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

Reference109 articles.

1. Institute of Medicine (US) Committee on Preventing the Global Epidemic of Cardiovascular Disease: Meeting the Challenges in Developing Countries. 2010 Epidemiology of cardiovascular disease. In Promoting cardiovascular health in the developing world: a critical challenge to achieve global health (eds V Fuster BB Kelly). Washington DC: National Academies Press. See https://www.ncbi.nlm.nih.gov/books/NBK45688/

2. Predicting the Future — Big Data, Machine Learning, and Clinical Medicine

3. Sörnmo L Laguna P. 2005 Bioelectrical signal processing in cardiac and neurological applications (ed. LS Laguna). Burlington: Academic Press. See http://www.sciencedirect.com/science/article/pii/B9780124375529500015 (accessed 10 October 2014)

4. Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria

5. A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features

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