2023 - Research.com Computer Science in United Kingdom Leader Award
2000 - Fellow of the Royal Academy of Engineering (UK)
Lionel Tarassenko mostly deals with Artificial intelligence, Respiratory rate, Pattern recognition, Photoplethysmogram and Machine learning. Lionel Tarassenko merges Artificial intelligence with Construct in his research. His Respiratory rate research integrates issues from Vagal tone, Oxygen saturation, Vital signs and Intensive care medicine.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Electroencephalography, Speech recognition, Probabilistic logic, Nonlinear system and Epilepsy. In his study, Pulse oximetry is inextricably linked to Kalman filter, which falls within the broad field of Photoplethysmogram. His Machine learning research is multidisciplinary, incorporating elements of Information system and Pattern recognition.
The scientist’s investigation covers issues in Artificial intelligence, Vital signs, Artificial neural network, Pattern recognition and Medical emergency. His study looks at the relationship between Artificial intelligence and fields such as Data mining, as well as how they intersect with chemical problems. Lionel Tarassenko has researched Vital signs in several fields, including Observational study, Respiratory rate, Warning system, Intensive care medicine and Emergency medicine.
As part of one scientific family, he deals mainly with the area of Respiratory rate, narrowing it down to issues related to the Cardiology, and often Blood pressure. His Artificial neural network study integrates concerns from other disciplines, such as Speech recognition and Very-large-scale integration. His work deals with themes such as Mobile phone, Emergency department and Clinical trial, which intersect with Medical emergency.
Blood pressure, Vital signs, Emergency medicine, Respiratory rate and Randomized controlled trial are his primary areas of study. His Vital signs research is multidisciplinary, incorporating perspectives in Observational study, Patient safety, Video camera, Acute hospital and Pediatrics. His research in Respiratory rate intersects with topics in Electrocardiography, Photoplethysmogram, Neonatal intensive care unit and Pattern recognition.
Lionel Tarassenko has included themes like Remote patient monitoring, Wavelet, Artificial intelligence, Computer vision and Continuous monitoring in his Photoplethysmogram study. His Artificial intelligence and Artificial neural network and Cluster analysis investigations all form part of his Artificial intelligence research activities. His research integrates issues of Intervention, Self-management, Physical therapy and Glycemic in his study of Randomized controlled trial.
His primary areas of investigation include Emergency medicine, Blood pressure, Respiratory rate, Vital signs and Randomized controlled trial. His studies deal with areas such as Photoplethysmogram and Key as well as Respiratory rate. The study incorporates disciplines such as Remote patient monitoring, Longest common subsequence problem, Artificial intelligence, Video monitoring and Continuous monitoring in addition to Photoplethysmogram.
His Artificial intelligence research incorporates elements of Modulation, Signal, Computer vision and Pattern recognition. His Vital signs research includes themes of Respiratory failure, Retrospective cohort study and Pneumonia. The various areas that Lionel Tarassenko examines in his Randomized controlled trial study include Odds ratio, Abnormal oral glucose tolerance, Physical therapy and Obstetrics.
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Review: A review of novelty detection
Marco A. F. Pimentel;David A. Clifton;Lei Clifton;Lionel Tarassenko.
Signal Processing (2014)
A dynamical model for generating synthetic electrocardiogram signals
P.E. McSharry;G.D. Clifford;L. Tarassenko;L.A. Smith.
IEEE Transactions on Biomedical Engineering (2003)
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.
The Lancet (2011)
Novelty detection for the identification of masses in mammograms
L. Tarassenko;P. Hayton;N. Cerneaz;M. Brady.
international conference on artificial neural networks (1995)
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
L Tarassenko;M Villarroel;A Guazzi;J Jorge.
Physiological Measurement (2014)
A Guide to Neural Computing Applications
A review of parametric modelling techniques for EEG analysis
J. Pardey;S. Roberts;L. Tarassenko.
Medical Engineering & Physics (1996)
Logistic Regression-HSMM-Based Heart Sound Segmentation
David B. Springer;Lionel Tarassenko;Gari D. Clifford.
IEEE Transactions on Biomedical Engineering (2016)
Quantifying errors in spectral estimates of HRV due to beat replacement and resampling
G.D. Clifford;L. Tarassenko.
IEEE Transactions on Biomedical Engineering (2005)
Application of independent component analysis in removing artefacts from the electrocardiogram
Taigang He;Gari Clifford;Lionel Tarassenko.
Neural Computing and Applications (2006)
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