Handwriting - marker for Parkinson's...

Handwriting - marker for Parkinson’s Disease

P. Drotár et al.

Signal Processing LabDepartment of Telecommunications

Brno University of Technology

3rd SPLab Workshop, 2013

P. Drotár et al. (Brno University of Technology) Handwriting - marker for PD SPLab Workshop 2013 1 / 28

Motivation

Outline

1 MotivationParkinson’s Disease

2 Data

3 Handwriting features

4 Classification

5 Additional handwriting analysis

6 Conclusions


Motivation Parkinson’s Disease

Parkinson’s Disease

neurodegenerative disorder affecting mainly elderlyhigh prevalence rates (4.1 mil) - second most commonneurodegenerative disorder

difficult diagnosis

causes are unknowncauses under research include:genetics, age, toxins

syndroms: tremor , rigidity,bradykinesia


Motivation Parkinson’s Disease

Handwriting - marker for PD

bradykinesia ⇒ micrographia - characterized by decreased lettersize and by changes in kinematic aspects of handwriting

kinematic variables have been shown to be sensitive measures foralterations of handwriting movements

decreased letter size, decreased velocities of writing, increasedmovement time, . . .complete picture of the extent to which any one measurement orset 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 processingno need for special sensors (wearable sensors)


Data

Outline


2 Data


4 Classification


6 Conclusions


Data

Data acquisition

Recorded signals:x-y coordinatestime stamppressurebutton statusazimuthaltitude


Data

Template


Data

Subjects

Altogether, 75 subjects were enrolled at the First Department ofNeurology, St Annes University Hospital in Brno.

37 Parkinsonian patients (19 men/18 women) + 38 healthy controls (20men/18 women)

Table: Parkinson’s handwriting dataset characteristics

Age UPDRS (part V) Years since diag.mean std mean std mean std

PD 69.3 10.9 2.27 0.84 8.37 4.8H 62.4 11.3 - - - -


Handwriting features

Outline


2 Data


4 Classification


6 Conclusions



In-air movement

Hand movement during handwriting = on-surface movement + in-airmovement

Hypothesis: In-air trajectory during handwriting contain informationreflecting syndromes of PD.



Handwrittig sample



Sample recordings

1000 2000 3000 4000 5000 60001200

1300

1400

1500

1600

In−air movement

1000 2000 3000 4000 5000 60001000

1200

1400

1600

1800

On−surface movement

Figure: Handwriting sample of healthy control



Sample recordings

4500 5000 5500 6000 6500 7000 7500 80001600

1700

1800

1900

2000

In−air movement

4500 5000 5500 6000 6500 7000 7500 80001400

1600

1800

2000

On−surface movement

Figure: Handwriting sample of PD patient




Feature Description

stroke speed trajectory during stroke divided by stroke durationspeed trajectory during handwriting divided by handwriting durationvelocity rate at which the position of a pen changes with timeacceleration rate at which the velocity of a pen changes with timejerk rate at which the acceleration of a pen changes with timehorizontal velocity/acceleration/jerk velocity/acceleration/jerk in horizontal directionvertical velocity/acceleration/jerk velocity/acceleration/jerk in vertical directionnumber of changes in velocity direction (NCV) the mean number of local extrema of velocitynumber of changes in acceleration direction (NCA) the mean number of local extrema of accelerationrelative NCV NCV relative to writing durationrelative NCA NCA relative to writing durationin-air time time spent in-air during writingon-surface time time spent on-surface during writingnormalised in-air time time spent in-air during writing normalised by whole writing duration

normalised on-surface timetime spent on-surface during writing normalised by wholewriting duration

in-air/on-surface ration ratio of time spent in-air/on-surface



FeatureMutualInformation

CorrelationCoefficient

stroke speed(on surface, standard dev.)

6.09 -0.388

velocity(in air, standard dev.)

5.94 -0.387

vert. jerk(in air, min.)

5.7 0.383

acceleration(in air, standard dev.)

5.92 -0.38

horz. jerk(in air, range)

5.72 -0.379

jerk(in air, standard dev.)

5.96 -0.389

horz. acceleration(in air, range)

5.81 -0.375

horz. velocity(in air, range)

5.87 -0.371

horz. velocity(on surface, quantile 75%)

4.46 -0.37

vert. acceleration(in air, min.)

5.74 -0.369


Classification

Outline


2 Data


4 Classification


6 Conclusions


Classification

Support Vector Machines

To learn non-linearly separable functions, the data are implicitlymapped to a higher dimensional space by means of a kernel function,where a separating hyperplane is found. New samples are classifiedaccording to the side of the hyperplane they belong to.

RapidMiner


Classification

Numerical results

Feature selectionRelief algorithm

SVMRadial basis function kernelThe parameters kernel gamma γ, penalty parameter C andconvergence epsilon ε were optimized using grid search ofpossible values. Specifically, we searched over the grid (C, γ, ε)defined by the product of the sets C = [10−5,10−4, . . . ,103,104],γ = [10−5,10−4, . . . , , 102,103] and ε = [10−5,10−4, . . . ,102,103]

Classifier validation was conducted using a leave-one-outapproachThe process was repeated a total of 50 times, where in eachrepetition the original dataset was randomly permuted prior tosplitting into training and testing subsets


Classification

Relief FS; SVM classification

4 8 12 16 20 24 28 32 36 4055

60

65

70

75

80

85

Number of features used for classification

Cla

ssific

atio

n a

ccu

racy (

%)

in−air + on−surface

in−air

on−surface


Classification

Relief(50) + Sequential forward FS; SVM classification

Relief select subset of 50 featuresSFFS

evaluating all feature subsets which consist of only one inputattributeselects only the best k subsetscopies of the attribute set are made and exactly one of thepreviously unused attributes is add to the attribute setiteration algorithm continues and next unused feature is added

Results:Sequential forwardfeature selection

all features

in-air+on-surface 85.61% 68.83%in-air 84.43% 68.83%

on-surface 78.16% 71.31%


Additional handwriting analysis

Outline


2 Data


4 Classification


6 Conclusions



Feature analysis

FeatureSVM predictionaccuracy [%]

CorrelationCoefficient

stroke speed(task 8, standard dev.)

70.6 -0.39

stroke width(task 3, percentile 1st)

68.3 -0.40

horz. velocity(task 8, percentile 99th)

68.2 0.33

stroke width(task 3, mean)

66.7 -0.24

stroke length(task 3, percentile 1st)

65.3 -0.24

stroke length(task 3, standard dev.)

64 0.32



evaluation of individual tasks (only on-surface)

TaskAccuracy

task 1 65.4task 2 70.0task 3 72.3task 4 65.4task 5 66.7task 6 67.7task 7 67.1task 8 78.7overall 79.4


Conclusions

Outline


2 Data


4 Classification


6 Conclusions


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 classificationaccuracy (> 85%)


Appendix

I

ER Dorsey et alProjected number of people with Parkinson disease in the mostpopulous nations, 2005 through 2030..Neurology, 2007.


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