Handwriting - marker for Parkinson s Disease

Transcription

1 Handwriting - marker for Parkinson s Disease P. Drotár et al. Signal Processing Lab Department of Telecommunications Brno University of Technology 3rd SPLab Workshop, 2013 P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

2 Motivation Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

3 Parkinson s Disease Motivation Parkinson s Disease neurodegenerative disorder affecting mainly elderly high prevalence rates (4.1 mil) - second most common neurodegenerative disorder difficult diagnosis causes are unknown causes under research include: genetics, age, toxins syndroms: tremor, rigidity, bradykinesia P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

4 Motivation Handwriting - marker for PD Parkinson s Disease bradykinesia micrographia - characterized by decreased letter size and by changes in kinematic aspects of handwriting kinematic variables have been shown to be sensitive measures for alterations of handwriting movements decreased letter size, decreased velocities of writing, increased movement time,... complete picture of the extent to which any one measurement or set of measurements is useful in predict is still missing handwriting - sensitive measure of micrographia in PD advantages over other approaches include: avoid usual pickles of speech signals acquisition and processing no need for special sensors (wearable sensors) P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

5 Data Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

6 Data Data acquisition Recorded signals: x-y coordinates time stamp pressure button status azimuth altitude P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

7 Data Template P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

8 Data Subjects Altogether, 75 subjects were enrolled at the First Department of Neurology, St Annes University Hospital in Brno. 37 Parkinsonian patients (19 men/18 women) + 38 healthy controls (20 men/18 women) Table: Parkinson s handwriting dataset characteristics Age UPDRS (part V) Years since diag. mean std mean std mean std PD H P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

9 Handwriting features Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

10 Handwriting features In-air movement Hand movement during handwriting = on-surface movement + in-air movement Hypothesis: In-air trajectory during handwriting contain information reflecting syndromes of PD. P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

11 Handwriting features In-air movement Hand movement during handwriting = on-surface movement + in-air movement Hypothesis: In-air trajectory during handwriting contain information reflecting syndromes of PD. P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

12 Handwriting features In-air movement Hand movement during handwriting = on-surface movement + in-air movement Hypothesis: In-air trajectory during handwriting contain information reflecting syndromes of PD. P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

13 Handwriting features In-air movement Hand movement during handwriting = on-surface movement + in-air movement Hypothesis: In-air trajectory during handwriting contain information reflecting syndromes of PD.. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

14 Handwriting features P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

15 Handwriting features Handwrittig sample P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

16 Sample recordings Handwriting features 1600 In air movement On surface movement P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

17 Sample recordings Handwriting features 2000 In air movement On surface movement P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

18 Handwriting features Handwriting features Feature stroke speed speed velocity acceleration jerk horizontal velocity/acceleration/jerk vertical velocity/acceleration/jerk number of changes in velocity direction (NCV) number of changes in acceleration direction (NCA) relative NCV relative NCA in-air time on-surface time normalised in-air time normalised on-surface time in-air/on-surface ration Description trajectory during stroke divided by stroke duration trajectory during handwriting divided by handwriting duration rate at which the position of a pen changes with time rate at which the velocity of a pen changes with time rate at which the acceleration of a pen changes with time velocity/acceleration/jerk in horizontal direction velocity/acceleration/jerk in vertical direction the mean number of local extrema of velocity the mean number of local extrema of acceleration NCV relative to writing duration NCA relative to writing duration time spent in-air during writing time spent on-surface during writing time spent in-air during writing normalised by whole writing duration time spent on-surface during writing normalised by whole writing duration ratio of time spent in-air/on-surface P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

19 Handwriting features Feature stroke speed (on surface, standard dev.) velocity (in air, standard dev.) vert. jerk (in air, min.) acceleration (in air, standard dev.) horz. jerk (in air, range) jerk (in air, standard dev.) horz. acceleration (in air, range) horz. velocity (in air, range) horz. velocity (on surface, quantile 75%) vert. acceleration (in air, min.) Mutual Information Correlation Coefficient P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

20 Classification Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

21 Classification Support Vector Machines To learn non-linearly separable functions, the data are implicitly mapped to a higher dimensional space by means of a kernel function, where a separating hyperplane is found. New samples are classified according to the side of the hyperplane they belong to. RapidMiner P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

22 Classification Numerical results Feature selection SVM Relief algorithm Radial basis function kernel The parameters kernel gamma γ, penalty parameter C and convergence epsilon ɛ were optimized using grid search of possible values. Specifically, we searched over the grid (C, γ, ɛ) defined by the product of the sets C = [10 5, 10 4,..., 10 3, 10 4 ], γ = [10 5, 10 4,...,, 10 2, 10 3 ] and ɛ = [10 5, 10 4,..., 10 2, 10 3 ] Classifier validation was conducted using a leave-one-out approach The process was repeated a total of 50 times, where in each repetition the original dataset was randomly permuted prior to splitting into training and testing subsets P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

23 Classification Relief FS; SVM classification in air + on surface in air on surface Classification accuracy (%) Number of features used for classification P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

24 Classification Relief(50) + Sequential forward FS; SVM classification Results: Relief select subset of 50 features SFFS evaluating all feature subsets which consist of only one input attribute selects only the best k subsets copies of the attribute set are made and exactly one of the previously unused attributes is add to the attribute set iteration algorithm continues and next unused feature is added Sequential forward feature selection all features in-air+on-surface 85.61% 68.83% in-air 84.43% 68.83% on-surface 78.16% 71.31% P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

25 Additional handwriting analysis Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

26 Additional handwriting analysis P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

27 Feature analysis Additional handwriting analysis Feature stroke speed (task 8, standard dev.) stroke width (task 3, percentile 1st) horz. velocity (task 8, percentile 99th) stroke width (task 3, mean) stroke length (task 3, percentile 1st) stroke length (task 3, standard dev.) SVM prediction accuracy [%] Correlation Coefficient P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

28 Additional handwriting analysis evaluation of individual tasks (only on-surface) Task Accuracy task task task task task task task task overall 79.4 P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

29 Conclusions Outline 1 Motivation Parkinson s Disease 2 Data 3 Handwriting features 4 Classification 5 Additional handwriting analysis 6 Conclusions P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

30 Conclusions 1 Handwriting as an tool for monitoring and diagnosis of PD 2 In-air movement - new modality PD evaluation 3 In-air + on-surface movement = clinically relevant classification accuracy (> 85%) P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28

31 Appendix I ER Dorsey et al Projected number of people with Parkinson disease in the most populous nations, 2005 through Neurology, P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop / 28