@inproceedings {43140,
	title = {Electrophysiological Simulation of Maternal-Fetal ECG on a 3D Maternal Torso Model},
	journal = {Computing in Cardiology},
	volume = {49},
	year = {2023},
	publisher = {Computing in Cardiology},
	address = {Computing in Cardiology},
	abstract = {Congenital heart disease (CHD) is a leading cause of infant death. To diagnose CHD, recordings from abdomi- nal fetal electrocardiograms (fECG) can be used as a non- invasive tool. However, it is challenging to extract the fetal signal from fECG recordings partly due to the lack of data combining fECG recordings with a ground truth for the fetal signal, which can be obtained by using a scalp elec- trode during delivery. In this study, we present a computa- tional model of a pregnant female torso, in which we simu- late fetal and maternal ventricular excitation during sinus rhythm to derive fECGs, so as to enable isolated measure- ment of the fetal and maternal signal contributions.},
	keywords = {Non-invasive fetal electrocardiogram},
	issn = {2325-887X},
	doi = {10.22489/CinC.2022.136},
	url = {https://cinc.org/archives/2022/pdf/CinC2022-136.pdf},
	author = {Julie Johanne {Uv} and Arevalo, Hermenegild}
}
@inbook {42469,
	title = {3D Simulations of Fetal and Maternal Ventricular Excitation for Investigating the Abdominal ECG},
	booktitle = {Computational Physiology - Simula Summer School 2021 - Student Reports},
	volume = {12},
	year = {2022},
	month = {05/2022},
	pages = {13 - 24},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-031-05163-0},
	issn = {2512-1677},
	doi = {10.1007/978-3-031-05164-7_2},
	url = {https://doi.org/10.1007/978-3-031-05164-7_2},
	author = {Julie Johanne {Uv} and Myklebust, Lena and Hamid Khoshfekr {Rudsari} and Welle, Hannes and Arevalo, Hermenegild},
	editor = {Kimberly J {McCabe}}
}
@inbook {42975,
	title = {Digital tracing, validation, and reporting},
	booktitle = {Smittestopp - A Case Study on Digital Contact Tracing},
	volume = {11},
	year = {2022},
	pages = {99{\textendash}120},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Manual contact tracing has been a key component in controlling the outbreak of the COVID-19 pandemic. The identification and isolation of close contacts of confirmed cases have successfully interrupted transmission chains and reduced the disease spread. Even though manual contact tracing has been widely used, its practice has shown that it is slow and cannot be scaled up once the epidemic grows beyond the early phase. In this case, digital contact tracing can play a significant role in controlling the pandemic. In this chapter, based on our experience and lessons learned from the Smittestopp project, we discuss the main prerequisites for the efficient implementation and validation of digital contact tracing in a population. Specifically, we discuss how to translate a close contact defined for manual tracing to proximity events discovered by a phone, that is, how to define a meaningful risk score and validate the digital contact tracing. We discuss challenges related to each step and provide solutions to some of them, even though questions still remain.},
	isbn = {978-3-031-05466-2},
	doi = {10.1007/978-3-031-05466-2_6},
	url = {https://doi.org/10.1007/978-3-031-05466-2_6},
	author = {Elmokashfi, Ahmed and Simon W {Funke} and Klock, Timo and Kuchta, Miroslav and Naumova, Valeriya and Julie Johanne {Uv}},
	editor = {Elmokashfi, Ahmed and Lysne, Olav and Naumova, Valeriya}
}
@misc {43047,
	title = {An image-based 3D electrophysiological torso model for simulating maternal and fetal ECG},
	howpublished = {Virtual Physiological Human Conference - VPH},
	year = {2022},
	url = {https://vph-conference.org/wp-content/uploads/sites/11/2022/09/VPH2022_Abstract_book_2.pdf},
	author = {Myklebust, Lena and Julie Johanne {Uv} and Welle, Hannes and Hamid Khoshfekr {Rudsari} and Arevalo, Hermenegild}
}
@article {maleckar2021combined,
	title = {Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients},
	journal = {Frontiers in physiology},
	volume = {12},
	year = {2021},
	pages = {745349},
	publisher = {Frontiers},
	author = {Maleckar, Molly and Myklebust, Lena and Julie Johanne {Uv} and Per Magne {Florvaag} and Str{\o}m, Vilde and Glinge, Charlotte and Jabbari, Reza and Vejlstrup, Niels and Engstr{\o}m, Thomas and Ahtarovski, Kiril and Jespersen, Thomas and Tfelt-Hansen, Jacob and Arevalo, Hermenegild}
}
