Decision support framework for Parkinson's disease based on novel handwriting markers.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TNSRE.2014.2359997, IEEE Transactions on Neural Systems and Rehabilitation Engineering JOURNAL OF LATEX CLASS FILES, VOL. 11, NO. 4, DECEMBER 2012

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Decision support framework for Parkinson’s disease based on novel handwriting markers Peter Drotár, Jiˇr´ı Mekyska, Irena Rektorová, Lucia Masarová, Zdenˇek Smékal, Senior Member, IEEE, and Marcos Faundez-Zanuy, Member, IEEE,

Abstract—Parkinson’s disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex- matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88.13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool. Index Terms—Parkinson’s disease, handwriting, tablet, decision support system, biomarker

I. I NTRODUCTION

P

ARKINSON’S disease (PD) is a progressive neurodegenerative disorder characterized by tremor, rigidity, bradykinesia, and loss of postural reflexes. With current prevalence rates, ranging from 10 to 800 people per 100,000, PD is one of the most common neurodegenerative disorders [1]. PD usually affects people at an average age of sixty [2], and prevalence rates are expected to increase further as the population ages. There is no objective quantitative method of clinical diagnosis. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. Clinicopathological studies show that up to 25% of the patients with PD are diagnosed incorrectly in the final stage of their disease, even by specialists in movement disorders [3]. Therefore, there is intensive effort to develop expert systems for the analysis and diagnosis of PD. There have been several attempts to approach the assessment of PD through technology and to develop decision support tools for accurately diagnosing PD. This usually P. Drotár, J. Mekyska and Zdenˇek Smékal are with the Department of Telecommunications, Brno University of Technology, Technická 12, 61200 Brno,Czech Republic. I. Rektorová and L. Masarová are with First Department of Neurology, Faculty of Medicine, Applied Neuroscience Research Group, CEITEC MU, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic. M. Faundez-Zanuy is with Escola Universitaria Politecnica de Mataro, Tecnocampus, Avda. Ernest Llunch 32, 08302, Mataro, Spain.

includes signal acquisition through wearable sensors during the Timed Up and Go clinical test [4], [5] or monitoring during free movement [6]. Another approach that attracted the attention of the speech processing community is based on recent findings that one frequent symptom of PD is significant vocal impairment [7], [8]. Research of automatic PD detection with machine learning tools using acoustic voice impairment measurement achieved promising results [9], [10]; the latest reported results showed as high as 98% overall classification accuracy [11]. It has been well documented that handwriting is affected in Parkinsons disease [12], [13], and some preliminary data suggest that handwriting might serve as a diagnostic marker for PD [14], [15]. Today there are wide range of high precision tablets and touch screen devices that make the acquisition and evaluation of handwriting signals very feasible. Moreover, there is no requirement for any special sensors and no need to solve the typical problems of acoustic signal acquisition and processing. Handwriting is a highly skilled and complex coordinated motor activity requiring the dynamic interaction of the lower arm, wrist, and finger muscles. The writing process involves accurate sequencing and online scaling of automated movements and planning of subsequent strokes [16]. In healthy subjects, these movements are automated and do not require additional attentional resources. The handwriting of patients with PD is often characterized by decreased letter size, changes in kinematic aspects of movement including decreased velocities and acceleration, an increased number of changes in velocity, and increased movement time [17]. A number of studies have been performed assessing the kinematic aspects of movement execution during handwriting and showing the potential of digitized handwriting analysis to be more sensitive than the UPDRS [17], [18]. However, a complete picture of the extent to which any one measurement or set of measurements is useful in discriminating PD has not yet been given. The most commonly measured parameters relate to the kinematics and dynamics of writing and drawing. To provide more insight and better understanding of the data, nonlinear, local structure-based measures can be used with advantage. Many measures based on time series analysis, different modeling techniques in frequency domain, and empirical mode decomposition were shown to be effective in various domains of biomedical research [9], [19]. We were therefore motivated to introduce several novel features describing the kinematic aspects of handwriting while also taking into account tremor, randomness, and hidden irregularities. These

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TABLE I PARKINSON ’ S HANDWRITING DATASET CHARACTERISTICS Age

UPDRS (part V) Years since diag.

mean std mean

LED

std

mean

std

mean std 1373.4 714

PD

69.3

10.9

2.27

0.84

8.37

4.8

H

62.4

11.3

-

-

-

-

-

-

new features are based on the measures of entropy, energy, and empirical mode decomposition of the handwriting data. A template for acquiring handwriting signals was proposed and used for diagnosis of PD. The support vector machines (SVM) algorithm was used to build a predictive model and identify subjects with PD. The reported results show that the proposed predictive model achieves medically relevant results in identifying subjects with PD. The remainder of this paper is organized as follows. The sections II and III present the dataset and the methods used, respectively. The IV section provides a statistical analysis of handwriting features and selects a subset of the most significant features. Finally, section V presents the experimental results, and conclusions are given in the VII section. II. DATA Altogether, 37 PD (19 men/18 women) and 38 (20 men/18 women) age and gender matched healthy controls were enrolled at the First Department of Neurology, St. Annes University Hospital in Brno, Czech Republic. All subjects were right-handed, had completed at least 10 years of education, and reported Czech as their first language. PD patients were examined only in their ON-state while on dopaminergic medication, i.e. 1-2 hours after taking their regular dose of dopaminergic medication. All patients were taking L-dopa COMT (catecholo-methyl transferase) inhibitor and/or a dopamine agonist. The mean daily levodopa-equivalent dose (LED) [20] was 1373.4±714 mg. Mean and standard deviation of age, Unified Parkinsons Disease Rating Scale-Part V Modified Hoehn and Yahr Staging score [21] and disease duration are summarized in Table I. The PD patients were diagnosed by an experienced neurologist following the clinical criteria of PD according to [3] and non-demented based on the clinicians judgment, caregivers interview, and the MMSE [22] score (> 27 points). Each subject was asked to complete a handwriting task according to the prepared template. The template was shown to patients and they were free to write the words without needing to follow the exact pattern. A completed task sheet is depicted in Fig.1. The template consists of seven handwriting tasks (part of a more detailed test battery). From the first to the third task, participants wrote cursive letters or bi/tri-grams of letters. Similar tasks (the letter l - or repetitions of it) are commonly used for handwriting analysis [23]. The next three tasks involved words that can be written in one long stroke, i.e. the writing device is in continuous contact with the writing surface while writing these words. The words were written in

Czech (the native language of participants) with the following translation to English: lektorka - teacher (female), porovnat to compare, nepopadnout - to not catch. The final task involved a longer sentence that also allowed the capture of the effect of fatigue during writing (Tramvaj dnes uˇz nepojede - The tram won’t go today). Handwritten signals were acquired using the digitizing tablet Intuos 4M (Wacom technology) in the x-y plane, and in the pressure axis. The tablet itself does not provide visual feedback; therefore, it was overlaid with paper so that the ink pen could be held in a normal fashion and allow for full visual feedback during writing. III. M ETHODOLOGY The recording starts when the pen touches the surface of digitizer and finishes when the task is completed. The digitizing tablet captures the following dynamic features (timesequences): x-coordinate (x[n]); y-coordinate (y[n]); time stamp (t[n]); and button status (b[n]). The x and y components are segmented into on-surface and in-air strokes and analyzed in terms of handwriting measures. The feature calculation stage involves the application of traditional and nonstandard measurement methods to process handwriting signals. Each method produces either a single value or a vector of numbers for each of 75 signals. If there is a resulting vector, we further compute six basic functionals (mean, median, standard deviation, 1st percentile, 99th percentile and 99th percentile 1st percentile). A. Kinematic Measures It has been shown that various kinematic aspects are affected in PD and several measures have been established to capture disruptions during handwriting [13]. Based on the literature survey, several kinematic and spatio-temporal parameters were considered for each task. These include: speed, number of changes in velocity(NCV)/acceleration(NCA), relative NCV/NCA, writing duration and length, stroke speed, velocity, acceleration, jerk, horizontal velocity/acceleration/jerk, vertical velocity/acceleration/jerk, and stroke height/width. Definition of these features can be found in [14] or in references therein. B. Measures of Entropy and Energy The digital representation of handwriting is a physiologically-based time series that is the result of several interacting physiological mechanisms. Such signals contains complex fluctuations which could provide information related to underlying processes and states of the physiological system. Entropy features have the potential to uncover hidden complexities in the handwriting process. For example, tremor and irregular muscle contractions introduce randomness to the movements during handwriting; however, this randomness is difficult to analyze using only kinematic measures. In order to uncover hidden complexities, two types of features were extracted separately from the x-coordinate (x[n]) and y-coordinate (y[n]) signals: signal entropy and signal energy based features.



3

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Fig. 1. Handwriting sample of healthy and PD subject

Entropy is a numerical measure of the randomness or uncertainty of a signal. The widely established and well known Shannon entropy of random variable X is defined as: X HS (X) = − p(x) log2 p(x), (1)

source, but the irregular movement resulting from muscle contractions and irregularities caused by cognitive impairment as a result of PD. Therefore, the noise variance estimation method of [25] is used to estimate noise variance N (s[n]). Then, signal-to-noise ratios are defined as

x∈X

where p(x) is probability density function computed using kernel density estimation with a Gaussian kernel. The generalization of Shannon entropy, describing the diversity and randomness of the system is Rényi entropy. The Rényi entropy is defined as follows: ! n X 1 HR,r (X) = log pri , (2) 1−r i=1 where r ≥ 0 is order of Rényi entropy. In summary, Shannon entropy HS and second and third order Rényi entropy HR,2 , HR,3 were calculated for both x[n] and y[n]. In order to obtain signal to noise ratios of handwriting, conventional energy (CE) and Teager-Kaiser (T KE) energy operators were computed as follows. The conventional energy operator of signal s[n] is defined as CE(s[n]) =

N −1 X

s[n]2

(3)

n=0

where N is the length of series s[n]. Similarly, Teager-Kaiser energy operators [24] T KEr (s[n]) =

NX −r−1

s[n]2 − s[n + r] · s[n − r],

(4)

n=0

where r ≥ 0 is the order of the T KE operator. Having defined equations for signal energy, we still need to obtain the variable describing noise that is present in the signal. Note that what we refer to as noise is not unwanted interference from an external

SN RCE =

CE(s[n])/N N (s[n])

(5)

T KEr (s[n])/N N (s[n])

(6)

and SN RT KEr =

In the equations above, s[n] is the signal under evaluation, i.e. x or y coordinates of handwriting. C. Intrinsic features 1) Empirical mode decomposition: Empirical mode decomposition (EMD) is an intuitive data-dependent decomposition of a time series that allows decomposition of non-linear and non-stationary data into intrinsic mode functions (IMF). EMD is conducted by the iterative extraction based on the local representation of the signal as the sum of a local oscillating component and a local trend. The first IMF, representing the oscillations of the entire signal, is extracted in the first iteration. The difference between the original signal and the IMF time series is the residual. The same procedure is then applied on the residual to extract the second IMF and so on. The entire procedure can be summarized as follows [26]: 1) For a given signal s[n], identify all the local extrema (both minima and maxima) 2) Construct upper envelope supper [n] by using a cubic spline to connect all the local maxima. In the same way, the lower envelope slower [n]is obtained by connecting all the local minima.



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3) Compute the mean of the upper and lower envelope as m[n] =

supper [n] − slower [n] 2

(7)

and extract m[n] from s[n] to obtain: s[n] ← s[n] − m[n]

(8)

4) Check if updated s[n] conforms to the properties of IMF. Otherwise repeat all the above steps. 5) Subtract the IM F [n] from s[n] to get the residue r1 [n] and then iterate on the residue. P IM Fj [n]−IM Fj−1 [n] 2 ) The whole process is stopped if ( IM Fj−1 [n] n

is less than 0.2 or 0.3. Given a time series s[n], combining all the IMFs gives the original signal, s[n] =

N X

IM Fj [n] + rN [n]

(9)

j=1

where N is the total number of IMFs obtained from the time series and rN [n] is the residual. The number of IMFs depends on the nature and length of the signal. Each IMF satisfies two basic assumptions [26]: (1) The number of maxima, which are strictly positive, and the number of minima, which are strictly negative, for each IMF, are either equal or differ by no more than one. (2) The mean value of the envelope, as defined by the maxima and the minima, for each IMF, is zero. 2) Extracting intrinsic features: From several algorithms for EMD computation, we used the freely available algorithm [27]. A number of discriminative temporal and spectral features were extracted from IMFs obtained from both normal and PD-affected handwriting signals. Intrinsic entropies IHS (X) and IHR,r (X) were obtained by computing the entropies defined in section III-B of the first and second IMF. The motivation for using only the first two IMFs is, that in practice, the first few IMFs contain only time-varying high spectral components representing the noise [9], [26], [27]. Since for PD patients the noise represents underlying irregularities, it is anticipated that the first pair of IMFs carries significant discriminative information with regard to handwriting of control and PD subjects. It is possible to compute intrinsic energies ICE and IT KEr similarly to intrinsic entropies by applying Eq. (5) and (6) to the first and the second IMF. Based on the assumption that higher order IMFs contain a main trend, or useful signal, different SNR measures are introduced as k=N Pimf

SN RΓ =

k=3 k=2 P

Γ(IM Fk [n]) .

(10)

D. Feature selection In order to reduce the dimensionality of input data and remove the non-relevant features, the first stage was a statistical analysis of the data using the Mann-Whitney U-test. The Mann-Whitney U test is nonparametric statistical test used to assess whether two independent groups are significantly different from each other. The features that passed the MannWhitney U-test with a significance level less than 0.05 level were kept. After this preprocessing stage, 203 features were selected as the candidate subset for further processing and classification. Feature selection algorithms aim to choose a small subset of features that ideally are necessary and sufficient to describe the target concept. From many feature selection algorithms, we decided to use the Relief algorithm [28], which has been shown to achieve promising results in problems similar to ours [11]. Relief is a feature-weighting algorithm that relies entirely on statistical analysis and employs only a few heuristics. It selects most of the relevant features even though only a small number of them are necessary for prediction. In most cases it does not help with redundant features. Since we want all the relevant features to be included for prediction, even at the cost of higher dimensionality, Relief was a promising candidate. E. Support Vector Machines The effectiveness of the selected subset of features in classifying PD and non-PD subjects was evaluated using nonlinear SVM. The underlying idea of SVM classifiers is to calculate a maximal margin hyperplane separating two classes of the data. To learn non-linearly separable functions, the data are implicitly mapped via nonlinear mapping ϕ(x) to a higher dimensional space by means of a kernel function, where a separating hyperplane is found. The equation of the hyperplane separating two differential classes is given by the relation y(x) = W T ϕ(X) =

K X

ωj ϕj (x) + ω0 = 0

(11)

j=1

where W = [ω0 , ω1 , . . . , ωK ] is the weight vector of the network. New samples are classified according to the side of the hyperplane they belong to. We used the Radial Basis Function (RBF) kernel [29]. The RBF kernel is defined as K(x, xi ) = e

−kx−xi k2 2γ 2

(12)

where γ controls the width of RBF function. The parameters kernel gamma γ and penalty parameter C were optimized using a grid search of possible values. Specifically, we searched over the grid (C, γ) defined by the product of the sets C = [2−7 , 2−6 , . . . , 26 , 27 ], γ = [2−7 , 2−6 , . . . , 26 , 27 ].

Γ(IM Fk [n])

k=1

Here, Γ is a symbolic representation of the intrinsic entropies and energies described above and Nimf is the number of intrinsic mode functions that were extracted.

IV. P RELIMINARY STATISTICAL ANALYSIS To obtain some preliminary insight into the statistical properties of handwriting features, we computed Spearman’s correlation coefficients ρ and mutual information (MI) between



feature vectors and associated responses. MI is a measure of the amount of information shared by two random variables X and Y . It is defined as: XX p(x, y) (13) I(X; Y ) = p(x, y) · log2 p(x)p(y) x∈X y∈Y

where x and y are possible variable values with a joint probability distribution function p(x, y) and marginal distribution functions p(x) and p(y), respectively [30]. We computed MI by evaluating the marginal entropies H(X), H(Y ) and joint entropy H(X, Y ) as I(X; Y ) = H(X) + H(Y ) − H(X, Y ), where entropies are defined eq (1). Table II summarizes fourteen handwriting measures with the largest relevance to response sorted according to an absolute correlation coefficient. All of the correlations are statistically significant (p < 0.005). Most of the listed features are newly proposed features, providing some initial confidence in the relevance of the selected features. Additionally, every feature was used separately as an input to the SVM classifier to evaluate its classification accuracy in separating PD and HC. The resulting individual classification accuracies of the features listed in Table II are listed in last column of the table. Note that these classification accuracies represent only the discriminative power of a single feature to separate PD from healthy subjects; any possible combination of features or causal relationships among features are not taken into account. The features that are among the ten features with the highest classification accuracy are marked in bold font. As can be seen from Table II, the highest classification accuracy of a single feature is over 76%, indicating that the classification task has a good chance of success. The probability density functions of the twelve most highly ranked features from Table II are shown in Fig. 2. The vertical axes are the probability densities of the normalized measures estimated using kernel density estimation with Gaussian kernels. The curves of the major part of the handwriting measures for PD show a clear difference from the probability densities of healthy subjects. V. E XPERIMENTAL RESULTS The classification test performance was determined by the computation of accuracy, sensitivity and specificity. The accuracy (Pacc ), sensitivity (Psen ) and specificity (Pspe ) are defined as TP + TN · 100% (14) Pacc = TP + TN + FP + FN TN Pspe = · 100% (15) TN + FP TP Psen = · 100% (16) TP + FN where true positive (TP) and false positive (FP) represent the number of correctly classified PD subject and the number of actually healthy subjects diagnosed as PD, respectively. Similarly, true negative (TN) and false negative (FN) represent the total number of correctly classified healthy controls, and the PD patients incorrectly classified as healthy controls,respectively.

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TABLE II F OURTEEN FEATURES WITH LARGEST RELEVANCE TO CLASS LABEL SORTED ACCORDING TO THE S PEARMAN ’ S CORRELATION COEFFICIENT. A DDITIONALLY, MI AND SVM PREDICTION FOR PARTICULAR FEATURES ARE DISPLAYED . Feature

|ρ|

x , std, task 2 HR,2

0.455 4.94 68.9

x , HR,2

0.448 5.02 69.0

percentile 99th - percentile 1st, task 2

y SN RICE ,

MI

SVM [%]

0.445 3.55 77.2

scalar, task 7

x , HR,3

percentile 1st, task 2

0.435 5.54 73.2

x , HR,3

std, task 2

0.423 5.26 70.4

SN Rx , y HR,3 ,

std, task 2

x SN RICE , x SN RICE y HR,3 ,

0.421 4.99 76.2

std, task 7

0.421 5.26 69.2

scalar, task 7

0.417 4.68 72.1

(dB), scalar, task 7

0.417 5.79 72.1

percentile 99th - percentile 1st, task 2

0.411 5.51 62.6

stroke length, percentile 99th - percentile 1st, task 2 0.411 4.91 67.2 stroke length, std, task 2

0.407 4.82 66.2

x , HR,2

0.407 5.27 71.6

percentile 1st, task 2

HSy ,std,

task 2

0.405 4.13 71.4

Classifier validation was conducted using stratified tenfold cross-validation. The process was repeated ten times; in each repetition the original dataset was randomly permuted prior to splitting into training and testing subsets. Classification accuracy, sensitivity, and specificity over the ten repetitions were averaged. Training and testing features were normalized before classification on a per-feature basis to have zero mean and a standard deviation of one. We computed classification accuracy, specificity, and sensitivity as the number of features is varied (Fig. 3). The features were selected by applying the Relief algorithm in each run. The highest accuracy Pacc = 85.6% was achieved using 168 features. However, as can be seen from the figure, only slightly lower accuracy can be obtained when employing a considerably smaller number of features. To provide another feature selection approach, we employed the strategy from section IV. The features are ranked according to their classification accuracy, i.e. only n features that provided the highest individual classification accuracy are included in the SVM classifier. The highest classification accuracy using the SVM ranking approach was Pacc = 88.1% for N = 162 features (see Fig. 4). As in the previous case, a smaller subset of features results in slightly worse classification accuracy. VI. D ISCUSSION Disturbances of motor control in PD, caused by the depletion of dopamine, affect the handwriting of PD patients. These disturbances involve the processing of motor planning, sequencing, movement initiation, and execution, and they



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Fig. 2. Probability density functions for selected handwriting parameters. The solid blue lines are for PD patients, the dashed red lines for HC subjects. Distribution of feature values is shown in bottom half of each figure.

result in hypometric movements, tremor and alterations in handwriting kinematics [13]. Modern technology makes it possible to capture handwriting so it can be incorporated into medical decision support systems. The acquisition of handwriting does not require any high quality controlled conditions and can be carried out at a clinic or at the patient’s home. All of the data used in this study were collected in a clinic environment with a tablet connected to a notebook computer without any previous preparation in a room or special environment. The task performance is quite simple and natural, and does not require any timing or exhaustive repetitions. The proposed approach, based on handwriting, can be alternative or complementary to other approaches, such as currently popular speech assessment for PD classification [9], [10]. Recent studies show very encouraging results, providing as high as 97 % PD classification accuracy. However, it should be noted that the first results in this area were significantly lower, identifying PD with around 90 % accuracy [31]. The 90 % classification accuracy presented by Little [31] is very similar to results presented in this study. We believe that further research will improve the performance of decision support systems based on handwriting. The application of this approach based on handwriting avoids the additional processing steps connected with speech processing, such as speech segmentation, noise removal, and requirements for a quality recording environment without external noises and disturbance. Other approaches, such as diagnosing PD from breath, do not achieve clinically relevant results and require dedicated sensors [32]. There is also huge potential in neuroimaging techniques, but these require expensive equipment

and cannot be used for monitoring at a patient’s home [33]. One of the main findings is that the features proposed in this study are relevant to PD diagnosis. They represent the majority of features that were ranked as the most correlated with class label. Additionally, when compared to our previous research [34], the inclusion of new features substantially improved classification accuracy. Since all of the PD subjects were on medication to correct symptoms of PD such as visible tremor, we had to focus on features that capture subtle signs of tremor. The tremor signals with irregularities are more similar to random signals. We used entropy to evaluate signals, where higher entropy means more irregular movement. We similarly computed the noise variance of a signal and obtained SN R indicating the amount of noise present in a signal. We assume that these features are related to the tremor; however, handwriting is a complex coordinated activity, where other factors can influence motor movement. In order to avoid or at least minimize other sources that could affect handwriting, only subjects without any history or presence of psychiatric symptoms or any disease affecting the central nervous system (other than PD in the PD cohort) and without impairment of the right upper extremity were included in this study. The fact that the proposed methodology is able to detect alterations in the handwriting of PD subjects on medication indicates the high sensitivity of the approach and its potential to discover even subtle changes in early stage PD. We did not find any significant correlations (p < 0.005) between handwriting features and daily LED of medication. The features with the highest relevance come mainly from two tasks: the second and the seventh ones. The seventh task



0.95 Pacc P

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Fig. 3. Classification accuracy, sensitivity and specificity based on the features selected by Relief method. SVM with nonlinear kernel was used with 10-fold cross-validation.

0.95 Pacc Psen

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0.85

Accuracy

(Tramvaj dnes uˇz nepojede.) is specific in the sense that it cannot be written as one long stroke; it is also the longest task. It is well known that PD patient handwriting depends on a visual closed loop, whereas normal handwriting is automated and the movements are so fast that the normal feedback loop of visual perception and muscle control is disabled, resulting in an open loop configuration [17]. We assume that sequenced writing requires a higher programming load than more simple tasks. This probably poses more challenging conditions for PD patients and amplifies the influence of the disease on handwriting. The seventh task is probably the most representative task in terms of the observed clinical symptoms of PD. The second task (le) is similar to the tasks previously used in other studies to analyze handwriting. It usually consists of loops like llll and was proven to reflect handwriting alteration due to PD [13], [35]. When we compare the second task with two similar tasks, i.e. the first one and the third one, handwriting features obtained from the second task appear to be more contributing to overall classification performance. Even though these tasks are similar, there are significant differences that have to be considered. The second task contains letters of different sizes that may pose more demanding requirements for cognition. Comparing the second task to the other tasks, there is another important difference. The second task is a pseudoword not existing in Czech language, whereas tasks from 3 to 6 include words used in everyday language. Therefore, the performance of the second task is not automatized. The lateral premotor cortex is additionally activated when attention must be paid during a motor task. This notion may partially explain the fact that the second and the seventh tasks are more relevant to the class labels than the other tasks. This should be taken into consideration when preparing handwriting experiments, and besides conventional tasks such as ellipses or llll writing, one should include also more difficult tasks. This study shows that handwriting is a promising biomarker that might be used as an early marker of PD. Our analysis showed that there is a link between the proposed features such as entropies and signal-to-noise ratios and motor symptoms of PD. Further studies in drug-naive, newly diagnosed cases of PD are warranted. Future studies performed in PD and atypical Parkinson syndromes will show whether handwriting could also serve as a possible biomarker for differential diagnosis of parkinsonism and help to explain how non-motor features such as cognitive impairment relate to specific handwriting alterations. From implementation point of view, there is no small subset of features for binary classification task as in the case of speech processing for PD diagnosis [11]. However, the results presented in Fig. 3 and Fig. 4 illustrate that most of the prediction performance comes from the group of 40-50 features. Adding more features increase prediction accuracy only by few perceptual points. This smaller group of features can be more conveniently implemented and used for monitoring. Especially entropy and signal-to-noise ratio features appear to sufficiently reflect alterations of PD handwriting. Finally, future studies could incorporate the prospective acquisition of samples from PD subjects to monitor the progression of handwriting impairment with the disease. Intraindividual variability (test-re-test variability) should also be evaluated.

7

0.8

0.75

0.7

0.65

0.6

0

50 100 150 200 Number of features included in SVM classifier

250

Fig. 4. Classification accuracy, sensitivity and specificity based on the features selected by SVM ranking method. SVM with nonlinear kernel was used with 10-fold cross-validation.

VII. C ONCLUSION We proposed an assessment of the practical clinical value of kinematic and novel handwriting features for identifying subjects with PD by using simple, easy-to-perform handwriting tasks. Newly introduced handwriting measures show significant relevance to the PD-score, a binary score indicating whether a sample belongs to a person with PD. The accuracy using our method is over 88% with very similar values for specificity and sensitivity. We emphasize that all PD subjects were examined in their ON motor state while on their regular dopaminergic treatment. This confirms that handwriting is a medically relevant bio-marker for classifying of Parkinson’s disease. We believe our approach can be complementary to numerous speech or movement based discriminant analyses of PD patients and deserves further attention. Future studies should further explore the associations between cogni-



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