Athanasios Tsanas
Athanasios Tsanas
University of Edinburgh
Verified email at - Homepage
Cited by
Cited by
Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests
A Tsanas, MA Little, PE McSharry, LO Ramig
IEEE transactions on Biomedical Engineering 57 (4), 884-893, 2010
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools
A Tsanas, A Xifara
Energy and Buildings 49, 560-567, 2012
Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease
A Tsanas, M Little, P McSharry, J Spielman, L Ramig
IEEE Transactions on Biomedical Engineering 59, 1264-1271, 2012
Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity
A Tsanas, MA Little, PE McSharry, LO Ramig
Journal of the Royal Society Interface 8 (59), 842-855, 2011
Objective automatic assessment of rehabilitative speech treatment in Parkinson's disease
A Tsanas, MA Little, C Fox, LO Ramig
IEEE Transactions on Neural Systems and Rehabilitation Engineering 22 (1 …, 2014
High-sensitivity troponin in the evaluation of patients with suspected acute coronary syndrome: a stepped-wedge, cluster-randomised controlled trial
ASV Shah, A Anand, FE Strachan, AV Ferry, KK Lee, AR Chapman, ...
Lancet 392, 919-928, 2018
Insomnia, nightmares, and chronotype as markers of risk for severe mental illness: results from a student population
B Sheaves, K Porcheret, A Tsanas, CA Espie, RG Foster, D Freeman, ...
Sleep 39 (1), 173-181, 2016
Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder
A Tsanas, KEA Saunders, AC Bilderbeck, N Palmius, M Osipov, ...
Journal of Affective Disorders 205, 225-233, 2016
Detecting Bipolar Depression from Geographic Location Data
N Palmius, A Tsanas, KEA Saunders, AC Bilderbeck, JR Geddes, ...
IEEE Transactions on Biomedical Engineering 64 (8), 1761-1771, 2017
Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning
A Tsanas
University of Oxford, 2012
Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression
A Tsanas, MA Little, PE McSharry, LO Ramig
2010 IEEE International Conference on Acoustics, Speech and Signal …, 2010
Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information fusion with adaptive Kalman filtering
A Tsanas, M Zañartu, MA Little, C Fox, LO Ramig, GD Clifford
Journal of the Acoustical Society of America 135, 2885-2901, 2014
Applications of machine learning in real-life digital health interventions: review of the literature
AK Triantafyllidis, A Tsanas
Journal of medical Internet research 21 (4), e12286, 2019
Patient specific predictions in the intensive care unit using a Bayesian ensemble
AEW Johnson, N Dunkley, L Mayaud, A Tsanas, AA Kramer, GD Clifford
2012 Computing in Cardiology, 249-252, 2012
Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing
A Tsanas, G Clifford
Frontiers in Human Neuroscience 9, 181, 2015
A methodology for the analysis of medical data
A Tsanas, MA Little, PE McSharry
Handbook of Systems and Complexity in Health, 113-125, 2013
New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity
A Tsanas, MA Little, PE McSharry, LO Ramig
International Symposium on Nonlinear Theory and its Applications (NOLTA …, 2010
The Windkessel model revisited: a qualitative analysis of the circulatory system
A Tsanas, JY Goulermas, V Vartela, D Tsiapras, G Theodorakis, ...
Medical engineering & physics 31 (5), 581-588, 2009
Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder
O Carr, KEA Saunders, A Tsanas, AC Bilderbeck, N Palmius, JR Geddes, ...
Scientific Reports 8, 1649, 2018
Machine Learning to Predict the Likelihood of Acute Myocardial Infarction
MP Than, JW Pickering, Y Sandoval, ASV Shah, A Tsanas, FS Apple, ...
Circulation 140, 899-909, 2019
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