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Fernando Andreotti

Affiliations: 
University of Oxford, Oxford, United Kingdom 
Area:
fetal ECG, machine learning, Kalman filter, estimation, detection
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Phan H, Andreotti F, Cooray N, et al. (2018) Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification. Ieee Transactions On Bio-Medical Engineering
Lakhno I, Behar JA, Oster J, et al. (2017) The use of non-invasive fetal electrocardiography in diagnosing second-degree fetal atrioventricular block. Maternal Health, Neonatology and Perinatology. 3: 1-6
Andreotti F, Graser F, Malberg H, et al. (2017) Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation Ieee Transactions On Biomedical Engineering. 64: 2793-2802
Hoyer D, Zebrowski J, Cysarz D, et al. (2017) Monitoring fetal maturation - objectives, techniques and indices of autonomic function. Physiological Measurement
Behar J, Andreotti F, Zaunseder S, et al. (2016) A practical guide to non-invasive foetal electrocardiogram extraction and analysis. Physiological Measurement. 37: R1-R35
Andreotti F, Behar J, Zaunseder S, et al. (2016) An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms. Physiological Measurement. 37: 627-48
Johnson AE, Behar J, Andreotti F, et al. (2015) Multimodal heart beat detection using signal quality indices. Physiological Measurement. 36: 1665-77
Andreotti F, Riedl M, Himmelsbach T, et al. (2014) Robust fetal ECG extraction and detection from abdominal leads. Physiological Measurement. 35: 1551-67
Behar J, Andreotti F, Zaunseder S, et al. (2014) An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings. Physiological Measurement. 35: 1537-50
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