3Department of physics, Suiz Canal University, Suiz canal, Egypt

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1 206 IJSRSET Volume 2 Issue 4 Prnt ISSN : Onlne ISSN : Themed Secton: Engneerng and Technology An ntellgent System for Dagnosng Schzophrena and Bpolar Dsorder based on MLNN and RBF M. I. Elgohary, Tamer. A. Alzohary 2, Amr. M. Essa, sally. Eldeghady 3, Hussen. M * * Department of physcs, Al-azhar unversty, Caro, Egypt huscholes@gmal.com 2 Department of computer scence, Al-azhar Unversty, Caro, Egypt 3Department of physcs, Suz Canal Unversty, Suz canal, Egypt ABSTRACT Ths paper s concerned e problem of dscrmnatng e patents sufferng from schzophrena and bpolar dsorder versus control based on EEG rhyms. The EEG rhyms are used to extract a feature vector for each patent. In s paper, e large set of features ncluded n e EEG rhyms s reduced nto smaller set of features usng FFT segmentaton. To classes of classfers hch are mult-layer perceptron and radal bass functon are used to dscrmnate e patent data based on features vector. Expermental studes have shon at e proposed algorms gve excellent results hen appled and tested on e ree classes. The multlayer neural netor bacpropagaton acheved a hgh performance rate equal to % compared to radal bass functon netors hch acheved a performance rate equal to 87.33%. Keyords: EEG, schzophrena, Bpolar dsorder, artfcal neural netors, Bacpropagaton Algorm, Radal bass functon netor. I. INTRODUCTION An essental crteron for successful treatment of a mental llness s a correct dagnoss. The clncal dagnostc gudelnes are ell establshed usng e Amercan Psychatrc Assocaton to dfferentate varous psychatrc condtons []. Hoever, e dagnostc process s a more dffcult tas an t may frst appear because specfc symptoms can appear n more an one dagnostc category, and dagnostc crtera can overlap to e pont here confdent dfferentaton s often mpossble. Also e psychatrc expert can have dffculty dstngushng certan psychatrc condtons, e.g. psychotc depresson from schzophrena or dfferentatng maor depressve dsorder from bpolar depresson (BD) [2]. Dagnoss of psychatrc condtons may prove problematc due to e overlappng nature of er symptoms. Offerng antdepressant medcaton to a maor depressve dsorder (MDD) patent can be qute helpful hle e same medcaton mght nduce mana n a BD patent. It s very mportant erefore at a correct psychatrc dagnoss be obtaned before treatment s ntated. [3] There are prevous studes based on e EEG for dagnoss. For example, e study [4] based on a sample of depresson and normal control subects reported at frontal bran asymmetry s a potental marer for depresson. In [5], EEG data s analyzed to compare normal subects versus subects sufferng varous mental dsorders. L and Fan [6] use an artfcal neural netor (ANN) fed EEG data to dfferentate schzophrena and depresson patents. [7] M. Lu employed Prncpal Components Analyss (PCA) nonlnear SVM, [8] Alba-Sanchez, F. utlzed Self-Organzng Maps (SOM) n pattern recognton of mental dsorder dsease. Hesh, M. use Support Vector Machne (SVM) to analyze Gamma band synchronzaton test data [9]. In s paper e propose an automated machne learnng procedure at can dagnose specfc forms of psychatrc llness based on e patent s restng electroencephalogram (EEG). The classes of psychatrc llness classfed are schzophrena (SCZ), and bpolar dsorder (BD). A comparson sample of healy or IJSRSET62430 Receved: 09 July 206 Accepted: 4 July 206 July-August 206 [(2)4: 7-23] 7

2 normal (N) subects s gven. The proposed meodology could n prncpal be extended to oer forms of llness. Classfcaton of psychatrc dsorders usng artfcal neural netors s a maor focus n s paper. The use of to classfer Bacpropagaton and Radal bass functon neural netors for dstngushng beteen e to classes of dagnostc llness: SCZ, BD aganst N s demonstrated. Ths paper s structured as follos. In Secton II, subects and meods. Neural netors as classfers are presented n secton III. In Secton V, e results and dscusson are gven. Fnally, some conclusons are remared n secton VI. A. Subect II. METHODS AND MATERIAL The EEG data ere obtaned from DR. ABOU ELAZAYEM PSYCHIATRIC HOSPITAL n Egypt [0]. The subects ncluded 70 normal persons ho have no hstory of neurologcal or psychatrc dsease, 80 schzophrenc and 80 bpolar patents. All patents ere hosptalzed and dagnosed schzophrena or bpolar accordng to e crtera of Dagnostc and Statstcal Manual of Mental Dsorders by ndependent psychatrsts. In an acoustcally and electrcally shelded room here e subects ere seated comfortably n a reclnng char, e EEG data ere obtaned from 6 surface electrodes placed on e scalp accordng to e standard nternatonal 0/20 system, namely e 6 channels, Fp, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O, O2 reference to lned earlobes. The dgtzaton of 6 channels EEG as performed a samplng rate of 00Hz usng a 2 bt ADconverter and e data ere recorded on a hard ds. For each subect, recordngs covered e EEG actvty of a restng condton for tme rangng from 3 to 5 mnutes approxmately. B. Feature Extracton Feature selecton s a very mportant step n pattern recognton. The dea of feature selecton s to choose a subset of features at mprove e performance of e classfer especally hen e are dealng hgh dmenson data. Fndng sgnfcant features at produce hgher classfcaton accuracy s an mportant problem. The man steps for vector feature extracton of each subect are usng e EEGLAB toolbox [] to flter e orgnal spontaneous EEG tme seres and remove e artfacts. A sze of clear 2000 tme-samples s selected hch represents a 20 seconds tme nterval from each subects. Fast Fourer Transform (FFT) for 2000 ponts of all-tme seres obtaned from 6 channels ere calculated. The 2000 ponts of each channel are parttoned nto 8 ntervals and e mean value of each nterval s computed hch results n 8 ponts. So, for each subect, ere s 8 ponts vector for each one of e 6 channels hch results n a sngle 28 feature vector at ll be used as e ANN nput vector for each subect. III. Artfcal Neural Netors Artfcal Neural Netors (ANNs) are e nterconnecton of smple processng nodes hch functonalty s modeled from e neuron n e bran. The ANN conssts of an nput layer, an output layer and at least one hdden layer. The nput values to e netor are fed from e nput layer rough e hdden layer to e output layer. The values obtaned as nputs are processed n hdden layer and forarded to eer e nodes of e next hdden layer or nodes of e output layer. There are dfferent types of NNs, n s paper e are nterested hch are feedforard neural netor (FFNN) bacpropagaton tranng algorm and RBF netor means clusterng algorm. They are sutable for e purpose of classfyng conflct n e to dseases schzophrena and bpolar. A. Multlayer feed-forard neural netor (MLFN) and ts dynamcs The MLFF netor archtecture s shon n fg., n hch e nput layer s composed of n nodes and an addtonal node for nput bas, output layer of m nodes for e m outputs of e netor and 2 hdden layers. Follong s e summary of e BPN [3]: Step : Intalze eghts and offsets. Set all eghts and node offsets to small random values. Step 2: Present nput and desred outputs. Present a contnuous valued nput vector x 0, x,, xn- Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 8

3 O and f s e transformaton functon beteen 2nd hdden layer and output layer hch s a lnear functon. Step 4: compute e error sgnal e ( ) d ( ) yˆ ( ) (5) Fg.. MLFF Netor archtecture. and specfy e desred output d0, d,, dm-. Because e netor s used as a classfer en all desred outputs are typcally set to zero except for at correspondng to e class e nput s from. That desred output s +. The nput from e tranng set are presented cyclcally untl stablzaton. Step 3: Calculate actual output. Frst calculate e formulas I Z f h f r h x b r I h b r Where x s e netor nput, () (2) h s e connecton eght from e nput to e neuron n e st hdden layer, r s e connecton eght from e neuron n e st hdden layer to e r neuron n e 2nd hdden layer, h b s e eght from e bas to e neuron n e st hdden layer, b r s e eght from e bas to e r neuron n e second hdden layer, f (.) and f (.) are nonlnear sgmod actvaton h functons defned as r f ( netnput) (3) netnput e The netor output s calculated by e follong equaton here O O O y ˆ ( ) f rzr b (4) O r s e eght connecton of e neuron n e output layer to e layer, O b s e bas eght for e r neuron n e 2nd hdden output neuron, Step 5: Adapt eghts. Use a recursve algorm startng at e output nodes and orng bac to e frst hdden layer. Adust eghts by ( ) ( ) x (6) In s equaton ()` s e eght from hdden node or from an nput to node at tme, x s eer e output of node or s an nput, η s e learnng rate, and s an error term for node J. If node J s an output node en d yˆ ) (7) here ( d s e desred output of node and actual output. If node s an nternal hdden node en x ( x ) (8) ŷ s e Where s over all nodes n e layers connected to node. Internal node resholds are adapted n a smlar manner by assumng ey are connecton eghts on lns from auxlary constant-valued nputs equal to. Step 6: Repeat by gong to step 2. B. Radal bass functon neural netor (RBFN) and ts dynamcs ) Radal Bass Functon netor archtecture Fg.2 shos a typcal RBF netor, q nputs ( x,, x ), and p outputs y,, y ). The hdden q ( p layer conssts of h computng unts connected to e output by h eght vectors, ) (, h. Response of one hdden unt to e netor nput at e nstant, x can be expressed by ( x) exp 2 ( ) x 2, ( : h) (9) Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 9

4 output. The follong are e ree steps of e hybrd learnng meod for an RBF neural netor: a) Fnd e cluster centers of e radal bass functon; use e -means clusterng algorm. b) Fnd e d of e radal bass functon usng p- nearest neghbor. c) Fnd e eght; use LMS. a) Calculaton of Centers here at Fg. 2. RBF Netor archtecture. s e center vector for hdden unt nstant, s e d of e Gaussan functon at at tme, and. denotes e Eucldean norm. The overall netor response s gven by h 0 ( x) yˆ f ( x ) (0) here p y R, x R q. The coeffcent vector s e connectng eght vector of e hdden unt to output layer, hch s n e vector form of T [,, l,, p] matrx of e netor can be expressed as A p h l p. Thus e coeffcent h l lh p ph T and e bas vector s,,,, ]. 0 [ 0 l0 p0 2) Tranng Radal Bass Functon Netor Tranng RBF neural netor conssts of determnng e locaton of centers and ds for e hdden layer and e eghts of e output layer. It s traned usng a to-phase approach: n e frst phase, unsupervsed learnng occurs, hch man obectve s to optmze e locaton of center and d. In e second phase, e output layer s traned n a supervsed mode usng e least mean-square (LMS) algorm to adust e eght so as to obtan e mnmum mean square error at e To calculate e centers of e radal bass functon e use e -means clusterng algorm. The purpose of applyng e -means clusterng algorm s to fnd a set of clustered centers and partton e tranng data nto subclasses. The center of each cluster s ntalzed to a randomly chosen nput datum. Then each tranng datum s assgned to e cluster at s nearest to tself. After tranng data have been assgned to a ne cluster unt, e ne center of a cluster represents e average of e tranng data assocated at cluster unt. After all e ne centers have been calculated, e process s repeated untl t converges. The recursve - means algorm s gven as follos: ) Choose a set of centers {,,, } arbtrarly and gve e ntal learnng rate 2 γ(0). h 2) Compute e mnmum Eucldean dstance L ( ) x( ) ( ) r argmn L ( ) :h 3) Adust e poston of ese centers as follos: ( ) ( ) ( )( x( ) ( )) ( r) ( ) 4), ( ) ( ) and go to 2. b) Wd Calculaton ( r) () (2) After e RBF centers have been found, e d s calculated. The d represents a measure of e spread of data assocated each node. Calculaton of e d s usually done usng e P-nearest neghbor algorm. A number P s chosen and for each center, e P nearest centers are found. The root-mean squared dstance beteen e current cluster and ts P nearest neghbors s calculated, and s s e value chosen for. So, f e current cluster center s, e value of d s gven by: Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 20

5 P 2 ( ) (3) P A typcal value of P s 2, n hch case s set to be e average dstance from e to nearest neghborng cluster centers. c) Weght Estmaton Learnng n e output layer s performed after calculaton of e centers and ds of e RBF n e hdden layer. The obectve s to mnmze e error beteen e observed output and desred one. It s commonly traned usng e LMS algorm [2] and s summarzed as follos: Tranng sample: Input sgnal vector = () Desred response = d() User-selected parameter: 0 Intalzaton: Intalze e eghts ŵ (0). Computaton: For =, 2,. Compute T e( ) d( ) ˆ ( ) ( ). ˆ( ) ˆ( ) () e(). classfers, selected 50 samples are used to test e to classfers (50 from Normal, 50 from schzophrenc patents and 50 from bpolar dsorder patents). IV. RESULTS AND DISCUSSION Before applyng e to models of ANN for dscrmnaton, e netor needs to be traned to optmze e netor performance. The effect of e number of hdden neurons for MLP s presented n Table. W a learnng rate of 0.05, e best performng hdden node setup s 30 for frst hdden node and 5 for second hdden node compared oer combnatons of hdden nodes. Table 2 shos e effect of e number of dfferent RBF clusters a fxed learnng rate, e best performance s obtaned hen e number of clusters s 0. Table : Effect of e Number of Hdden Nodes on Performance of MLP ANN (testng results) 3) Classfyng schzophrena and bpolar usng NNs Multlayer perceptron bacpropagaton and radal bass functon means clusterng algorm are programmed usng C++ programmng language [4]. The nput layer for bo neural netors conssts of 28 source nodes as mentoned before. The ntal eghts for multlayer perceptron BP and RBF neural netors are taen randomly from e nterval [0, ]. The learnng rate gven e value 0.05.Choosng e number of e hdden layer nodes for MLP and RBF s shon n e secton 4. The number of output layer nodes depends on dseases needed to be dscrmnated. In our paper e correspondng desred outputs n bo MLP and RBF are (,0,0),( 0,,0), (0,0,) for Normal, schzophrena and bpolar dsorder respectvely. The number of samples s 230 EEG poer spectra for all cases 80 samples representng BP, 80 samples representng Sc and 70 samples representng normal. 80 samples (20 normal, 30 BP and 30 Sc) have been used as tranng samples for e to classfers used hle 50 samples have been used for testng. After tranng to Table 2: EFFECT of e Number of clusters n Hdden layer on Performance of RBF based ANN Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 2

6 After tranng bo netors, 50 selected test sample are used (50 from Normal, 50 from schzophrenc patents and 50 from bpolar dsorder patents). The BP ANN as tested structure of four layers bacpropagaton netors (28 nput nodes, 30 frst hdden nodes, 5 second hdden nodes and 3 output nodes). The radal bass functon neural netor used conssts of (28 nput node 0 clusters and 3 output nodes).the performance of MLP bacpropagaton neural netor s gven n Table3; t shos at The MLP neural netor performed best average classfcaton accuracy of 98.67% respect to all test samples. For RBF netor means clusterng algorm, Table4 shos e performance of netor of RBF neural netor here classfyng accuracy reached 87.33%. Table 3: Performance of MLP BP algorm n Classfcaton of EEG Poer Spectra from e ree classes N, SCZ, and BD Table 4: Performance of RBF algorm n Classfcaton of EEG Poer Spectra from e ree classes N, SCZ, and BD V. CONCLUSION The dscrmnaton of Schzophrena and bpolar dsorder s a sgnfcant problem hch requres e use of ntellgent algorms. To classfers used for dscrmnaton hch are MLP BP neural netor and RBF neural netor. Features extracted from EEG of 230 subects (70 normal, 80 schzophrenc patents and 80 bpolar patents) are used. Applyng e to classfers on 230 subects have shoed at performance rate for e MLP BP s % hle for radal bass functon netors s 87.33%. The performance rates obtaned from e study have shon at feedforard neural netor BP s preferred to solve e conflct n e dagnostc process beteen schzophrena and bpolar dseases. VI. REFERENCES [] Amercan Psychatrc Assocaton, & Amercan Psychatrc Assocaton. (994). Dagnostc and statstcal manual of mental dsorders (DSM). Washngton, DC: Amercan psychatrc assocaton, [2] V. Kusumaar, Antdepressants and antpsychotcs n e long-term treatment of bpolar dsorder, Journal of Clncal Psychatry, vol. 63, Suppl 0, pp , [3] Sachs, G. S., Prntz, D. J., Kahn, D. A., Carpenter, D., & Docherty, J. P. (2000). The expert consensus gudelne seres: medcaton treatment. -04 Med,, of bpolar dsorder. Postgrad [4] A. J. Nemec and B. J. Lgo, Alpha-band characterstcs n EEG spectrum ndcate relablty of frontal bran asymmetry measures n dagnoss of depresson, n Proceedngs Int. Conf. of e IEEE Eng. n Medcne and Bology Socety, pp , Sept [5] P. Coutn-Churchman, et al., Quanttatve spectral analyss of EEG n psychatry revsted: drang sgns out of numbers n a clncal settng, Clncal Neurophysology, vol. 4, no. 2, pp , Dec [6] L, Y. J., & Fan, F. Y. (2006, January). Classfcaton of Schzophrena and depresson by EEG ANNs. In 2005 IEEE Engneerng n Medcne and Bology 27 Annual Conference. IEEE (pp ). Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 22

7 [7] M. Lu, A Study of Schzophrena Inhertance rough Pattern Classfcaton, 2nd Internatonal Conference on Intellgent Control and Informaton Processng, Changsa, 20. [8] Alba-Sanchez F., Asssted Dagnoss of Attenton-Defct Hyperactvty Dsorder rough EEG Bandpoer Clusterng Self-Organzng Maps, 32nd Annual Internatonal Conference of e IEEE EMBS, Argentna, September 200. [9] Hesh, Mng-Hsen, et al. "Classfcaton of schzophrena usng genetc algorm-support vector machne (ga-svm)." Annual Internatonal Conference of e IEEE Engneerng n Medcne and Bology Socety (EMBC). IEEE,. 203 [0] tm [] Arnaud Delorme, Scott Maeg. EEGLAB: an open source toolbox for analyss of sngle-tral EEG dynamcs ncludng ndependent component analyss. Neurosc Meods. Vol. 34. pp [2] El-Gohary, M. I., Al-Zohary, T. A., Essa, A. M., Eldeghady, S. M., & El-Hafez, H. M. A. (205). EEG Dscrmnaton of Rats under Dfferent Archtectural Envronments usng ANNs. Internatonal Journal of Computer Scence and. 24 3(2), Securty, Informaton [3] Haste, T., Tbshran, R., & Fredman, J. (2009). Unsupervsed learnng. In The elements of statstcal learnng (pp ). Sprnger Ne ISO. Yor 690 [4] Rao, V., and Rao, H., (996). C+ + Neural Netors and Fuzzy Logc, st edn. BPB Publcatons, Ne Delh. Internatonal Journal of Scentfc Research n Scence, Engneerng and Technology (srset.com) 23