ACCURATE TELEMONITORING OF PARKINSON’S DISEASE

SYMPTOM SEVERITY USING SPEECH SIGNALS

Schematic representationof the UPDRS estimation process

Athanasios Tsanas1,2, Max A. Little1,2,3, Patrick E. McSharry1,2, Lorraine O. Ramig4,5

1Systems Analysis, Modelling and Prediction (SAMP), Mathematical Institute, University of Oxford, Oxford, UK, 2Oxford Centre for Industrial and Applied Mathematics (OCIAM), University of Oxford, Oxford, UK,3Oxford Centre for Integrative Systems Biology, Department of Physics, University of Oxford, UK, 4Speech, Language, and Hearing Science, University of Colorado, Boulder, Colorado, USA, 5National

Center for Voice and Speech, Denver, Colorado, USA

Parkinson’spatientspeaks intomicrophone

Hometelemonitoringdevice recordsspeech signal

Speechtransferredto USB stick

Internet

Patient’s home Medical Centre

Data into patient’s personalcomputer

Speechsignalprocessingalgorithms

Statisticalmapping ofalgorithms toUPDRS

PredictedUPDRS reportto clinical staff

Data intodedicatedserver in the clinic

0 0.5 10

10

20

PPE

UP

DR

S

J=|F0,i-F0,i+1|

S=|A0,i-A0,i+1|

J=|F0,i-F0,i+1|

S=|A0,i-A0,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. 884-893, 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. 594-597, Dallas, Texas, US, 14-19 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. 457-460, 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

Approach DescriptionClassical 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

Measure Motor-UPDRS Total-UPDRS

Nonlinear dynamical systems theory1.62 ± 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