Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset

ABSTRACT

The interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of cardiovascular health. Despite the progressive digital transformation in healthcare, it is still common for clinicians to analyse ECG printed on paper. Although some systems provide signal processing-based ECG classification, clinicians often find it unreliable. Artificial Intelligence (AI) techniques are becoming state-of-the-art for ECG processing but the lack of digitised ECG has hampered the clinical translation of these techniques. Concurrently, we are living a rise in augmented reality (AR) technologies, with an increasing availability of devices. In this work, we present an automatic digitisation and assisted interpretation of ECG based on an AI-enabled Augmented Reality headset. The AR headset is used to acquire an image of the printed ECG, from which the digitised ECG signal is extracted. Afterwards, the digitised ECG is introduced into a Deep Learning (DL) algorithm pre-trained on a public database of 12-lead ECG recordings. The output of the DL algorithm classifies the ECG signal onto different cardiomyopathy categories, which is then visualized back in the AR headset. Preliminary classification results on simulated ECG images (96.5% of accuracy) confirm the potential of the developed approach to contribute on the digital transformation of ECG processing.

KEYWORDS:

• Electrocardiogram
• medical data digitisation
• augmented reality
• deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministerio de Ciencia, Innovación y Universidades under the Retos I+D Programme (RTI2018-101193-B-I00), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Ministerio de Economíay Competitividad under the Programme for the Formation of Doctors (PRE2018-084062). Alberto Gomez acknowledges financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s and St Thomas’ NHS Foundation Trust in partnership with King's College London and King’s College Hospital NHS Foundation Trust.

Notes on contributors

P. Lampreave

Paula Lampreave received her BS in Biomedical Engineering by the Universidad Politécnica de Madrid, Spain in July 2019 and her MSc in Computational Biomedical Engineering by Universitat Pompeu Fabra, Barcelona Spain in July 2020. Her research interests include Medical Imaging analysis and the use of Mixed Reality in clinical applications.

G. Jimenez-Perez

Guillermo Jimenez-Perez is a predoctoral researcher in the PhySense research group at the Universitat Pompeu Fabra, Barcelona. He received his BsC degree in Biomedical engineering in 2015, and his MsC degree, related to artificial intelligence, in 2016, at the University of Sevilla. His research interests are related to applying machine learning for electrocardiographic and intracavitary electrographic data, as well as on echocardiographic images, with a focus on interpretable solutions. 

I. Sanz

Isidro Sanz-Pérez works as a physician in Hospital Universitari Vall d’Hebron. He is specialized in Internal Medicine and currently working at Emergency Department and Hospital at Home Unity. He graduated in Medicine in 2005 at Universitat Autònoma de Barcelona. He is currently pursuing a doctoral degree in ultrasound imaging, to detect subclinical cardiovascular disease in systemic autoimmune diseases.

A. Gomez

Alberto Gomez is a Research Fellow in the School of Biomedical Engineering & Imaging Sciences at King’s College London, UK. He received his BSc degree in Telecommunications Engineering from the Technical University of Madrid, Spain, in 2009, and an MRes in image and signal processing from IMT-Atlantique, Brest, France, the same year. He received his PhD degree from King's College London in 2013. His research interests are focused on Smart Ultrasound Imaging, including acquisition, analysis and visualisation for cardiac and fetal applications

O. Camara

Oscar Camara received the B.S. degree in telecommunication engineering from the Universitat Politècnica de Catalunya, Barcelona, Spain, in 1999, and the master’s and Ph.D. degrees in image processing from École Nationale Supérieure des Télécommunications, Paris, France, in 2000 and 2003, respectively. From 2004 to 2007, he held a Post-Doctoral position with King's College London, London, U.K. and University College London, London. In 2007, he joined Universitat Pompeu Fabra (UPF), Barcelona, Spain, as a Ramón y Cajal Fellow and later became an Associate and Full Professor in Biomedical Engineering in 2012 and 2020, respectively. He is coordinating the PhySense Research Group, UPF, which he founded in 2011. He is also one of the founders of the BCN Medtech unit, UPF and is involved in several technology transfer projects, including Open Source solutions and creation of spin-offs. His current research interests include methodologies at the crossroads of computational imaging and modeling areas that can be effectively used in a clinical environment, including neurology, cardiology, and oncology applications.