L
Lionel Tarassenko
Researcher at University of Oxford
Publications - 419
Citations - 19351
Lionel Tarassenko is an academic researcher from University of Oxford. The author has contributed to research in topics: Artificial neural network & Vital signs. The author has an hindex of 67, co-authored 395 publications receiving 16265 citations. Previous affiliations of Lionel Tarassenko include National Institutes of Health & National Institute for Health Research.
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Review: A review of novelty detection
TL;DR: This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.
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A dynamical model for generating synthetic electrocardiogram signals
TL;DR: A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals and may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.
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Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies
Susannah Fleming,Matthew Thompson,Matthew Thompson,Richard Stevens,Carl Heneghan,Annette Plüddemann,Ian Maconochie,Lionel Tarassenko,David Mant +8 more
TL;DR: The authors' evidence-based centile charts for children from birth to 18 years should help clinicians to update clinical and resuscitation guidelines and show decline in respiratory rate fromBirth to early adolescence.
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Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
Lionel Tarassenko,Mauricio Villarroel,Alessandro Guazzi,João Jorge,David A. Clifton,Christopher W. Pugh +5 more
TL;DR: This work has devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation, and has been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model.
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Logistic Regression-HSMM-Based Heart Sound Segmentation
TL;DR: This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation, and implements a modified Viterbi algorithm for decoding the most likely sequence of states.