Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.
Background: Long-term exercise induces cardiac remodeling that potentially influences the electrical properties of the heart. Hypothesis/objectives: We assessed whether training alters cardiac conduction in Standardbred racehorses. Animals: Two hundred one trained and 52 untrained Standardbred horses. Methods: Cross-sectional study. Resting ECG recordings were analyzed to assess heart rate (HR) along with standard ECG parameters and for identification of atrial and ventricular arrhythmias. An electrophysiological study was performed in 13 horses assessing the effect of training on sinoatrial (SA) and atrioventricular (AV) nodal function by sinus node recovery time (SNRT) and His signal recordings. Age and sex adjustments were implemented in multiple and logistic regression models for comparison.Results: Resting HR in beats per minute (bpm) was lower in trained vs untrained horses (mean, 30.8 ± 2.6 bpm vs 32.9 ± 4.2 bpm; P = .001). Trained horses more often displayed second-degree atrioventricular block (2AVB; odds ratio, 2.59; P = .04). No difference in SNRT was found between groups (n = 13). Mean P-A, A-H, and H-V intervals were 71 ± 20, 209 ± 41, and 134 ± 41 ms, respectively (n = 7). We did not detect a training effect on AV-nodal conduction intervals. His signals were present in 1 horse during 2AVB with varying H-V interval preceding a blocked beat. Conclusions and Clinical Importance:We identified decreased HR and increased frequency of 2AVB in trained horses. In 5 of 7 horses, His signal recordings had variable H-V intervals within each individual horse, providing novel insight into AV conduction in horses.
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.
Hammer, for their support, motivation, and always behind me. Without them, this would be only a dream. After joining the HOST department as a Ph.D. candidate in 2018, I started experiencing a completely new environment with new people from different countries and cultures.Michael Riegler became my principal supervisor. I did not know anything about him despite his academic background. However, after few weeks, I realized that he is more than my principal supervisor for my life. Within few months, he became a game-changer in my life. I do not have words to express his qualities and how his advice, encouragement, kindness, and motivations are important to my life. Therefore, I would like to give my most enormous thanks to my primary supervisor, Michael, who is always behind me to support my academic life and get advice for my personal life.Pål Halvorsen is the department head and one of my co-supervisors, and he is a very kind person supporting us always silently. His advice and encouragements make me better and better every day in my academic life. Then, I would like to thank Pål for his kindness showed me through my Ph.D. journey and the wordless help given to me. Hugo Hammer is my second co-supervisor who supports me in handling advanced statistical problems in my Ph.D. research. Then, I would like to thank Hugo for his friendly help, as always.The research has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which the Research Council of Norway financially supports under contract 270053. Tore was the person who helped me to use these infrastructures.So, I would like to give my special thanks to him for his outstanding support.I also would like to thank the closest colleague, Steven Hicks, a good friend met within the department and in my life. Moreover, I thank all the other colleagues, Debesh, Hanna, Pia, Hakon, and Daniel, who work closely in my Ph.D. periods. There are more people v I want to thank, but writing them all here is not possible. However, they all are equally important for me, and therefore I would like to thank them all equally. Master students, other Ph.D. students from different departments and other counties, all the co-authors, and persons who are from the administration are a few of them.Finally, I would like to thank my wife, Shalike, who sacrificed the freedom of her life for finishing my Ph.D. successfully. Not only that, she always provided me food and other necessary stuff without asking anything from me. Last but not least, I would like to give my thanks to my parents, who sacrificed their whole life to provide us a good life while facing a challenging lifestyle in my country,
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