4th. generally by. Since most. to the. Borjkhani 1,* Mehdi. execution [2]. cortex, computerized. tomography. of Technology.

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1 4th Internatiional Conferene of Fuzzy Information & Engineering Otober 0100 Shomal University, Amol, Iran Diagnosiss of Parkinson diseases based on handwriting kinematis using fuzzy lassifier Mehdi Borjkhani 1,*, Mehdi Fallahnezhad, Farzad Towhidkhah 3, Mohammad Hasan Moradi 4 1-Eletrial Engineering Department, Urmia University of Tehnology, Borjkhani@gmail.om -Biomedial Engineering Department, Amirkabir University of Tehnology, Mehdi.fallah@gmail.om 3-Biomedial Engineering Department, Amirkabir University of Tehnology, Towhidkhah@aut.a.ir 4-Biomedial Engineering Department, Amirkabir University of Tehnology, mhmoradi@aut.a.ir Abstrat Parkinson Diseases (PD) is a neurodegenerative disorder haraterized generally by slowness of movement, rigidity and tremor. After emerging the disease s symptoms, the usual way for reognizing the disease and its monitoring during the treatment is a omputerized tomography (CT) san of the brain. Proposing novel methods instead of CT san of brain is highly of great signifiane, due to the problems like side effets, high ost and leak of high aessibility. Sine most symptoms of the disease are motor symptoms, so handwriting kinematis is mostly suggested as PD diagnose. In order to lassify handwriting kinematis, we use a reently presented fuzzy information gain measure, whihh has high average lassifiation auray rate. In this method onsidering eah lass and alulating the fuzzy entropy of eah subset of training instane, membership funtions are onstruted; Then lass degree of eah training subsets are alulated. Finally fuzzy lassifier results are ompared with those of an artifiial neural network (ANN). Aording to the ahieved results, to detet PD from handwriting the fuzzy lassifier has some advantages in ompare with those of ANN. Keywords: Parkinson Diseases, Handwriting, Fuzzy, Classifiation. 1. Introdution Parkinson s disease (PD) is a motor disorder whih is haraterized by tremor, rigidity and akinesia [1]. It is well established that disturbanes of motor ontrol in PD are aused by dopamine redution aused by a derease in the dopaminergi projetion from the substantia nigra to the striatum. Thesee disturbaness involve proessing of motor planning, motor programming, motor sequening, movement initiation and movement exeution []. Neuroanatomially, the brain soures of PD are mostly related to premotor ortex, motor ortex, somatosensory ortex and basal ganglia. However, interplay of geneti, biologial, environmental, and psyhologial fators are thought to be involved [3]. The ommon method of disease reognition is a omputerized tomography (CT) san of the brain. Beause diverse brain photographingg ause side effets in * Corresponding author: aademi member of Eletrial Engineering Department, Urmia University of Tehnology 1

2 Author 1 and Author addition to high ost and not easy aess, finding a new manner for deteting the disease quikly and easily, along with low ost and without side effets seems to be useful [4]. Previous studies have demonstrated that fmri ativations in individual anatomi regions an be used as a basis for lassifying subjets for PD [5, 6]. Also, it s shown that it is possible to differentiate between patients suffering from PD and normal healthy persons on the basis of EMG rhythms [7]. There are two ways of handwriting measurement and analysis: off-line tehniques (measurement and analysis of handwriting images) and on-line tehnique (measurement and analysis of handwriting dynamis). On-line tehniques ertainly provide more valuable information for biomedial appliations [8]. A ommon method for investigating motor disorder in patients affeted with the diseases suh as Parkinson and Shizophrenia is investigating extrated features of pen movements in their handwritings [9, 10, 11, 1]. The features suh as veloity, saling, and jerk of handwriting in PD patients are different from healthy persons. Analyzing of handwriting for diagnosing the PD has been pioneered by Marquard and Mai and had been widely aepted by researh [13, 14, 15]. Reently, Unlu and his olleagues' have experiened as a first step into this diretion by demonstrating how the handwriting dynamis of healthy person an automatially be distinguished from that of PD by using a fore sensitive devie [10]. In following of their work, we have lassified Shizophrenia and healthy individuals by using Artifiial Immune System, Modified Artifiial Immune System and Support Vetor Mahine from extrating handwriting kinemati features [16]. Many different methods have been used for handling different lassifiation problems. With regard to the kind of hallenging lassifiation problem, different lassifiers provide superior speifiations to the others. Some examples of suh lassifiers are the linear funtion method [17], the instane-based method [18], the deision trees method [19], the rule-based method [0], the naive Bayes method [1], the artifiial neural networks method (ANN), the support vetor mahines (SVM) method [], the sequential minimal optimization (SMO) method [3], the geneti algorithm method [4, 5], Higher order neural networks [6] and et. In some lassifiation problems a preproessing stage over the data set is unavoidable. Some data sets may be presented by nominal features whih must be presented in numeri interpretation firstly. In some ases, feature dimensions need to be dereased. In suh problems, a feature subset seletion method must be applied before. In this paper, we use a newly presented method based on fuzzy information gain measure [7, 8], for lassifying malignant who suffering from Parkinson disease and nonmalignant persons.. Methodology In this part, dataset that is used in simulations and the struture of ANN are disussed..1. Data olletion The dataset used is omposed of 3 different experiments, the patients is asked to write the word hello in 3 different sales, in 3 different stroke height ondition sizes (1m, m and 4m) after the tests were arried on various patients, 8 different features were extrated from these experiments. Third of the features are the vertial Peak Veloity (PV) in writing the word hello from the 1m, m and 4m stroke height ondition respetively. The other feature is the Veloity Saling Slope (VSS) in writing the word hello. The others are the Normalized Jerk (NJ) in writing the word hello from the 1m, m and 4m stroke height ondition respetively [9].The last feature is the Average of Normalized Jerk (ANJ). In fat, the seleted features potentially an reveal at least some aspets of bradykinesia, rigidity and tremor that are main haraters of PD... Fuzzy lassifier based on fuzzy information gain measures In this setion, we introdue the newly presented fuzzy information gain measure method and desribe the training and testing algorithms. In fuzzy systems, features are desribed in the terms of fuzzy sets whih indiate the linguisti terms (Zadeh, 1975). These linguisti terms are mathematially presented via membership funtions whih define fuzzy degree of membership to fuzzy sets. Features an be desribed by several

3 Diagnosis of Parkinson diseases based on handwriting kinematis using fuzzy lassifier linguisti terms and eah linguisti term an be represented by a fuzzy set (Zadeh, 1965) haraterized by a membership funtion. In summary, in the training stage we firstly apply k-means lustering method to provide the luster enters in eah updating stage. We use the luster enters as fuzzy sets enters and apply fuzzy entropy measure for onstruting orresponding membership funtions. In the following we review some definitions given by hen and shie (009) whih is used for measuring the lass degree and entropy of a subset of training instanes belonging to a lass in the training stage. Class degree of a subset of training instanes whih shows the degree of belonging to the speified lass is given by: CD ( v) = x X C µ ( x) V x X µ ( x) V (1) where X denotes the set of values of the feature f i, X denotes the set of values of the feature f i whih belong, and µ v (x) is the membership grade of value x belonging to the fuzzy set v. Fuzzy entropy of a subset of training instanes is given by: FE CD ( v) ( v) = CD ( v) log C () where C is set of lasses, and CD (v) is the lass degree of a subset of training instanes belonging to a lass of a fuzzy set v. Finally, fuzzy information gain of a feature is given by: n n sv FIG = ) ( FE( v)) n s ( R, f ) ( log C n v V (3) where n is the total number of instanes, n is the number of instanes of a lass, V denotes the fuzzy sets of the feature f, s denotes the summation of the membership grades of the values of the feature f belonging to eah fuzzy set of the feature f, S v is the summation of the membership grades of the values of the feature f belonging to a fuzzy set v of the feature f. When the lass degree, fuzzy entropy and fuzzy information of the training instanes alulated, we an use the ahieved information for testing the lassifier on the testing instanes. In this method, dynami weights are speified for different feature at testing time with regard to the fuzzy information obtained during the training time. In fat, regarding the importane of eah feature for indiating eah lass instanes, a feature seletion is applied. The following definitions are given to form an evaluation algorithm of lassifying testing instanes [8]. The estimated fuzzy entropy of a testing instane is given by: EFE ( x) = ( µ ( x) FE( v)) f v V f v (4) where V f is the set of fuzzy sets of the feature f, µ v (x) is the membership grade of value x belonging to a fuzzy set v alulated at the training time, lv(x) denotes the membership of value x, and FE(v) is the fuzzy entropy of the subset of training instanes alulated at the training time whose values of the feature f fall in the support of the fuzzy set v of the feature f. 3

4 Author 1 and Author The fuzzy entropy weight of a feature f of a testing instane is given by: n ( 1/ n log 1/ n ) EFE f ( x) FEW f ( x) = [ n ( 1/ n log 1/ n ) EFE ( x )] g F g g (5) where x is the value of a feature f of the testing instane r, EFEf(x) is the estimated fuzzy entropy of the testing instane, F denotes a set of features, n denotes the number of lasses, x g denotes the value of a feature g of the testing instane, and EFE g (x g ) is the estimated fuzzy entropy of the testing instane. The estimated lass degree of a testing instane is given by: ECD ( x, f ) = ( µ ( x) CD ( v)) v V f v (6) where V f denotes the fuzzy sets of the feature f, µ v (x) is the membership grade of value x belonging to the fuzzy set v, and CD (v) is the lass degree of the subset of training instanes belonging to a lass. The evaluating funtion for lassifying a testing instane is given by: WECDC( r) = arg maxwecd ( r) C (7) where C denotes a set of lasses, WECD (r) is the weighted estimated lass degree of the testing instane belonging to a lass. 3. Results E We used 75% of total instanes as training and the remaining as the testing instanes. For obtaining a reliable auray rate, we iterate the training and testing proess for 00 times and we report the mean value and standard deviation of ahieved results for both training and testing instanes as bellows: 4. Conlusion Nowadays a great deal of effort is foused on the development of reliable lassifiers on new appliations. In this paper, a reently presented fuzzy lassifier based on fuzzy information gain measure was used to diagnosis PD. It s shown that fuzzy lassifier is a powerful tool to lassify suh data. Finally the results indiate that brain photographing ould be replaed by using extrated patterns of persons handwritings as a suitable manner for deteting Parkinson's disease. Adding the data related to attentional disorder hyperativity disease (whih is similar to Parkinson's disease data, in some aspets) and lassifying a new set of data that is a harder problem are our next goals. Aknowledgments The authors would like to thank Dr. Mihael P. Caligiuri from UCSD for provision of the dataset used in this artile. 4

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