Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

Autor: Alejandra Zepeda-Echavarria, Rutger R van de Leur, Meike van Sleuwen, Rutger J Hassink, Thierry X Wildbergh, Pieter A Doevendans, Joris Jaspers, René van Es

Publikováno v: JMIR Cardio, Vol 7, p e44003 (2023)

BackgroundElectrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is

Externí odkaz: https://doaj.org/article/b4e1e1c5b38e48c690a9fadefdfd4cc7

Interatrial Block Predicts Life‐Threatening Arrhythmias in Dilated Cardiomyopathy

Autor: Michiel T. H. M. Henkens, Helena López Martínez, Jerremy Weerts, Arjan Sammani, Anne G. Raafs, Job A. J. Verdonschot, Rutger R. van de Leur, Maurits A. Sikking, Sophia Stroeks, Vanessa P. M. van Empel, Hans‐Peter Brunner‐La Rocca, Antonius M. W. van Stipdonk, Dimitrios Farmakis, Mark R. Hazebroek, Kevin Vernooy, Antoni Bayés‐de‐Luna, Folkert W. Asselbergs, Antoni Bayés‐Genís, Stephane R. B. Heymans

Publikováno v: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 11, Iss 14 (2022)

Background Interatrial block (IAB) has been associated with supraventricular arrhythmias and stroke, and even with sudden cardiac death in the general population. Whether IAB is associated with life‐threatening arrhythmias (LTA) and sudden cardiac

Externí odkaz: https://doaj.org/article/cb4fdebc7223417d8eb115fbd394b9be

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

Autor: Philippe C Wouters, Rutger R van de Leur, Melle B Vessies, Antonius M W van Stipdonk, Mohammed A Ghossein, Rutger J Hassink, Pieter A Doevendans, Pim van der Harst, Alexander H Maass, Frits W Prinzen, Kevin Vernooy, Mathias Meine, René van Es

Publikováno v: European Heart Journal, 44(8). Oxford University Press

AimsThis study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning–based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG cri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::052a3ced9cd8b54aec12dddfe169c069
https://doi.org/10.1093/eurheartj/ehac617

Improving explainability of deep neural network-based electrocardiogram interpretation using variational auto-encoders

Autor: Rutger R van de Leur, Max N Bos, Karim Taha, Arjan Sammani, Ming Wai Yeung, Stefan van Duijvenboden, Pier D Lambiase, Rutger J Hassink, Pim van der Harst, Pieter A Doevendans, Deepak K Gupta, René van Es

Publikováno v: European Heart Journal-Digital Health, 3(3). Oxford University Press

Aims Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developme

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b3a36505ce04c0958850d40785c63fd
https://doi.org/10.1093/ehjdh/ztac038

Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk

Autor: Klaske R Siegersma, Rutger R van de Leur, N Charlotte Onland-Moret, David A Leon, Ernest Diez-Benavente, Liesbeth Rozendaal, Michiel L Bots, Ruben Coronel, Yolande Appelman, Leonard Hofstra, Pim van der Harst, Pieter A Doevendans, Rutger J Hassink, Hester M den Ruijter, René van Es

Publikováno v: European Heart Journal-Digital Health, 3(2), 245-254
Siegersma, K R, Van De Leur, R R, Onland-Moret, N C, Leon, D A, Diez-Benavente, E, Rozendaal, L, Bots, M L, Coronel, R, Appelman, Y, Hofstra, L, Van Der Harst, P, Doevendans, P A, Hassink, R J, Den Ruijter, H M & Van Es, R 2022, ' Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk ', European Heart Journal-Digital Health, vol. 3, no. 2, pp. 245-254 . https://doi.org/10.1093/ehjdh/ztac010

Aims Incorporation of sex in study design can lead to discoveries in medical research. Deep neural networks (DNNs) accurately predict sex based on the electrocardiogram (ECG) and we hypothesized that misclassification of sex is an important predictor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d7be37a2eda71f2f133910bd34315e2
https://researchonline.lshtm.ac.uk/id/eprint/4665956/1/ztac010.pdf