ACCURATE TELEMONITORING OF PARKINSON’S DISEASE SYMPTOM SEVERITY USING SPEECH SIGNALS Schematic representation of the UPDRS estimation process Athanasios Tsanas 1,2, Max A. Little 1,2,3, Patrick E. McSharry 1,2, Lorraine O. Ramig 4,5 1 Systems Analysis, Modelling and Prediction (SAMP), Mathematical Institute, University of Oxford, Oxford, UK, 2 Oxford Centre for Industrial and Applied Mathematics (OCIAM), University of Oxford, Oxford, UK, 3 Oxford Centre for Integrative Systems Biology, Department of Physics, University of Oxford, UK, 4 Speech, Language, and Hearing Science, University of Colorado, Boulder, Colorado, USA, 5 National Center for Voice and Speech, Denver, Colorado, USA  Parkinson’s patient speaks into microphone  Home telemonitoring device records speech signal  Speech transferred to USB stick Internet Patient’s home Medical Centre  Data into patient’s personal computer  Speech signal processing algorithms  Statistical mapping of algorithms to UPDRS  Predicted UPDRS report to clinical staff  Data into dedicated server in the clinic J=|F 0,i -F 0,i+1 | S=|A 0,i -A 0,i+1 | J=|F 0,i -F 0,i+1 | S=|A 0,i -A 0,i+1 | Project background Parkinson’s disease (PD) claims lives at an epidemic rate (affecting ~20/100,000 people every year) There is no treatment, but drugs can alleviate some of the symptoms Clinical metric used to quantify average symptom severity: Unified Parkinson’s Disease Rating Scale (UPDRS). Motor-UPDRS range: 0-108, Total-UPDRS range: 0-176, where 0 denotes healthy control. Currently, UPDRS is estimated by clinical raters (subjective, inter-rater variability) PD affects speech, and there is empirical evidence of degrading speech performance with disease progression We propose novel nonlinear signal processing algorithms mining the information in speech We demonstrate that by using speech signals we can accurately replicate the clinicians’ UPDRS assessment References A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests”, IEEE Transactions Biomedical Engineering, Vol. 57, pp , 2010a A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson’s disease progression”, IEEE Signal Processing Society, International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp , Dallas, Texas, US, March 2010b A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity", International Symposium on Nonlinear Theory and its Applications (NOLTA), pp , Krakow, Poland, 5-8 September 2010c (invited) A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Remote tracking of Parkinson’s disease progression by extracting novel dysphonia patterns from speech signals”, Journal of the Royal Society Interface, (in press) 2010d ApproachDescription Classical dysphonia measures (Tsanas et al. 2010a) Capture fundamental frequency changes, amplitude, variability Improved classical dysphonia measures (Tsanas et al. 2010b) Power transformation of classical schemes leads to improved results Wavelets, entropy and energy operators (Tsanas et al. 2010c) Wavelet decomposition + entropy + linear and nonlinear energy concepts Nonlinear dynamical systems theory (Tsanas et al. 2010d) Invoke nonlinear concepts for signal decomposition and signal to noise ratios Features – various approaches Feature selection MeasureMotor-UPDRSTotal-UPDRS Nonlinear dynamical systems theory 1.62 ± 0.17 (males) 1.72 ± 0.16 (females) 1.96 ± 0.23 (males) 2.20 ± 0.21 (females) Parkinson’s disease symptom tracking Curse of dimensionality (failure to adequately populate the feature space) Reducing number of features enables a) improved performance, b) more accurate inference of the underlying characteristics of the modelled system Many approaches: LASSO, elastic net, Random Forests (work in progress) Conclusions Statistical mapping Results Speech signals convey clinically useful information Fast, accurate, remote, objective monitoring of Parkinson’s disease is shown to be possible using simply speech signals Results are considerably better than the inter-rater variability (which is about 5 UPDRS points). This technology could facilitate large-scale clinical trials into novel Parkinson’s disease treatments Statistical machine learning algorithm maps the feature matrix on the response Experimented with various state of the art classification and regression algorithms Random Forests seems to work particularly well in this problem, probably because there are many features contributing towards the response variable Use 10-fold cross validation with 100 repetitions for statistical confidence Report the out of sample mean absolute error (MAE). The findings are given in the form mean ± standard deviation Use the one-standard-error rule to determine the most parsimonious model