Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/56250 |
| Acceso en línea: | http://hdl.handle.net/10230/56250 http://dx.doi.org/10.1080/21681163.2020.1835544 |
| Access Level: | acceso abierto |
| Palabra clave: | Electrocardiogram medical data digitisation augmented reality deep learning |
| Sumario: | 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. |
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