SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS DURING EPILEPTIC SEIZURES BY USING FUZZY C-MEANS

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1 Amercan Journal of Appled Scences 11 (10: , 2014 ISSN: Abdy and Ahmad, Ths open access arcle s dsrbued under a Creave Commons Arbuon (CC-BY 3.0 lcense do: /aassp Publshed Onlne 11 ( (hp:// SEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS DURING EPILEPTIC SEIZURES BY USING FUZZY C-MEANS 1,2 Muhammad Abdy and 3 Tahr Ahmad opon n paens havng eplepc sezures refracory o of eplepsy (Bnadhnan and Ahmad, Correspondng Auhors: Tahr Ahmad, Ibnu Sna Insue for Fundamenal Scence Sudes, Unvers Tenolog Malaysa, Suda, Johor, Malaysa and Muhammad Abdy, Deparmen of Mahemacs, Unversas Neger Maassar, Indonesa 1 Deparmen of Mahemacs, Unversas Neger Maassar, Indonesa 2 Faulas MIPA, Unversas Sulawes Bara, Indonesa 3 Deparmen of Mahemacal Scences and Ibnu Sna Insue for Fundamenal Scence Sudes, Unvers Tenolog Malaysa, Suda, Johor Darul Tazm, Malaysa Receved ; Revsed ; Acceped Fundng: The Governmen of Souh Sulawes Provnce, Indonesa and Unvers Tenolog Malaysa Compeng Ineress: The auhors have declared ha no compeng neress exs ABSTRACT Elecroencephalogram (EEG s a recordng of elecrcal acvy of he bran. I conans valuable nformaon relaed o he dfferen physologcal saes of he bran. A quanave EEG analyss has been developed over he years ha nroduce obecve measure, reflecng no only he characerscs of he bran acvy self bu also gvng clues concernng he underlyng assocaed neural dynamcs. In hs sudy, he mage form of he EEG sgnals s segmened no pars ha have he nearly equal elecrcal curren srengh. Ths segmenaon uses fuzzy c-means. An example of EEG sgnal daa wll be provded and segmened usng he obaned mehod. Keywords: EEG Sgnals, Fuzzy-C-Mean, Segmenaon 1. INTRODUCTION Eplepc sezures resul from a emporary elecrcal dsurbance of he bran. Somemes sezures may go unnoced, dependng on her presenaon and somemes may be confused wh oher evens, such as a sroe, whch can also cause falls or mgranes (Subas, Eplepc sezures may cause negave physcal, psychologcal and socal consequences, ncludng loss of conscousness, nures and sudden deah (Guo e al., Predcon of sezures s challengng because here s very lle confrmed nowledge of he exac mechansm responsble for he sezure. Effecve algorhms for auomac sezure deecon and predcon can have a far reachng mpac on dagnoss and reamen of eplepsy (Adel, Surgcal reamen may be an 1830 medcaon (Engel e al., The man problem n eplepsy surgery s o resolve he sze and locaon of he eplepc foc. Therefore, s crucal o deermne an accurae echnque whch s capable o localze he eplepc focus n paen who s sufferng from eplepsy. The Elecroencephalogram (EEG s a resulan sgnal of he acve poenals of many nerve cells n he cerebral corex and capures he cerebral funcon. EEG s one of he rregular and feeble sgnals n he lvng body (Zhanga e al., I plays an mporan dagnosc role n eplepsy and provdes supporng evdence of a sezure dsorder as well as asssng wh classfcaon of sezures. Careful analyss of he EEG records can provde new nsghs no he eplepogenc process and may have consderable ulzaon n he dagnoss and reamen

2 Muhammad Abdy and Tahr Ahmad / Amercan Journal of Appled Scences 11 (10: , 2014 In recen years, some researchers have suded on sezure deecon and predcon from EEG analyss usng wo dfferen approaches: (1 Examnaon of he waveforms of he EEG sgnals such as spes, whch may be precursors o sezures; (2 Analyss of he nonlnear spaoemporal evoluon of he EEG sgnals o fnd a governng rule as he sysem moves from a sezure-free o sezure sae (Adel, All of he mehods used o analyze he neuronal acves n he bran durng eplepc sezures usng EEG sgnals n he wave form. In he wor of Abdy (2014, he neuronal acves n he bran durng eplepc sezures dsplayed on fla EEG were ransformed no mage form. The fla EEG s a new mehod o map hgh dmensonal EEG sgnal no low dmensonal space (Zaara, Sezure and he fla EEG ha are modeled as a dynamcal sysems share he same dynamcs. Besdes, her augmened dynamc raecory s lnearly ordered and order somorphc o each oher by he relaon nduced from her moon (Ahmad and Ken, In hs sudy, mage form of he EEG sgnals durng eplepc sezures s segmened no regons usng fuzzy c-mean. 2. MATERIALS AND METHODS 2.1. Image form of EEG Sgnals Le C be all he cluser ceners a me,.e.: C = {c 1, c 2,,c m }; m = he number of cluser cener a me. Each c carres he poson a fla EEG and elecrcal poenal, n shor: ( c = (( x, y, V for cluser cener c a each pxel p a me. The fuzzy neghborhood s represened by a membershp funcon µ c. The membershps funcon µ c a me proposed by Abdy (2014 as Equaon 1 and 2: ( V µ c ( p = ( V + d( p, c where, (V s he elecrcal poenal c a me. And: 2 2 ( c c (1 d ( p, c = ( x x + ( y y (2 s dsance beween pxel p and cluser cener c a me. Sandardzaon wll be done o V and d(p, p so hey wll free from un (dmensonless quany. Sandardzaon of V and d(p, p as follows (Abdy, 2014 Equaon 3: V V d( p, c d( p, c ( V = and d( p, c = (3 σ ( σ d ( p, c V Due o (V and (d(p, c mus be posve and greaer or equal o 0, respecvely, we ransform hem no (0, a] and [0, b], respecvely. Ths ransformaon can be accomplshed by applyng a lnear mappng funcon as gven below (Abdy, 2014 Equaon 4 o 6: ( V + ( V ( V " = ( V ( V + ε; ε > 0 mn max ( V max ( V mn ( mn (4 V = The elecrcal poenal of he cluser cener h, so C = {((x, y, V x, y, V R} where, = 1, 2,, m. Le P be he enre pxels of he fla EEG,.e.: { } ( P = p, p, p such ha p = x, y, hen C P 1 2 n A fuzzy neghborhood can be defned as a degree µ x (y o whch wo pons x and y are neghbors. Several defnons have been proposed (Demo and Zahzah, 1995; Bloch e al., 1997, whch are ypcally decreasng funcons of he dsance beween boh pons. For purpose of formng mage a fla EEG, we consder a fuzzy neghborhood of elecrcal poenal 1831 And: ( d( p, c " Hence: µ ( p = c = ( d( p, c + ( d( p, c mn ( d( p, c ( d( p, max c ( d( p, c ( d( p, c max ( mn " ( V ( V + ( d( p, c " " mn (5 (6 If s consdered ha he fuzzy neghborhood for each cluser cener c a me form a fuzzy regon of

3 Muhammad Abdy and Tahr Ahmad / Amercan Journal of Appled Scences 11 (10: , 2014 he elecrc curren a fla EEG, hen P s a fuzzy se whose membershp value µ P ( p of each s elemens,.e., Equaon 7: {((,, µ (,, µ ( [0,1]} P = x y p x y R p (7 P P The fuzzy se P consues unon of he fuzzy neghborhood of he cluser ceners a me. Hence, can be obaned he membershp value µ P (p of each pxel p by usng he operaon of unon (max operaor,.e., Equaon 8: µ ( p = max[ µ ( p ] (8 P c Each pxel whn a fuzzy regon of fla EEG can be nerpreed as degree o whch ha pxel s a curren source a fla EEG. Nex, he membershp value of each pxel p s ransformed no he mage daa I. Ths s done so we can see he sources of elecrcy n fla EEG as grey levels. Each pxel wll be represened by an neger value n he nerval 0 o 255. The membershp value 0 s mapped o 0 n he mage daa and he membershp value 1 mapped no he mage daa 255. The ransformaon ha s used a each pxel p no he mage daa I s a lnear funcon (Abdy, 2014, namely Equaon 9: I = 255 µ ( p (9 P Hence, s obaned he mage form of EEG sgnals a he fla EEG durng eplepc sezure Segmenaon Segmenaon, one of he bolenecs of medcal mage processng, s a fundamenal buldng bloc for hgher-level mage analyss; allows a compac descrpon of he mage no conours, regons. The segmenaon acually aemps o dscover assocaons beween subclasses of a populaon o reduce he dmenson (Demo and Zahzah, There are wo man approaches used n segmenaon whch are based on crsp and fuzzy mehods. Crsp segmenaon algorhm generaes parons such ha each obec s assgned o exacly one class; on he oher hand, fuzzy mehods provde a much more adequae ool for represenng real daa srucures. In hs sudy, we use Fuzzy C-Mean (FCM algorhm, gven by Bezde (1981. The FCM algorhm mnmzes he obecve funcon for he paron of daa se, x = [x 1, x 2,...,x n ] T, gven by Equaon 10 (Sayad e al., 2007: c n m 2 m(, =, (10 = 1 = 1 J u v u x v where, c s he number of he cluser (1 c n; n s he number of samples n he vecor X; µ s he elemen of he paron marx U of sze (c n, n whch each member ndcaes he degree of membershp of daa vecor n cluser ; v s he cluser cener of he h cluser and m s he fuzzfer, m>1, whch conrols he fuzzness of he mehod. The elemens of he paron marx U s consraned o conan elemens n he range [0, 1] and should sasfy he followng condons Equaon 11: c = 1 and µ = 1;1 n n 0 µ n;1 c = 1 (11 In he framewor of he segmenaon of an mage of sze (N M, he vecor X conans all he gray level of he mage, scanned lne by lne,.e., n = NM. The fuzzy c- means algorhm performs he paron of he vecor X no c fuzzy subses where µ represens he membershp value of x n cluser h. The FCM cluserng echnque can be summarzed by he followng seps (Sayad e al., 2007: Sep 1: Inalzaon (Ieraon 0: Scan he mage lne by lne o consruc he vecor X conanng all he gray level of he mage Randomly nalze he ceners of he classes vecor V (0 From he eraon =1 o he end of he algorhm: Sep 2: Calculae he membershp marx U ( of elemen µ usng Equaon 12: u 1 2 c m 1 x v = = 1 x v (12 Sep 3: Calculae he vecor V ( = [v 1, v 2,...,v c ] usng Equaon 13: v = n = 1 n µ x = 1 m µ m (

4 Muhammad Abdy and Tahr Ahmad / Amercan Journal of Appled Scences 11 (10: , 2014 Sep 4: Convergence es: f V ( V ( 1 <ε hen sop, oherwse, ncrease by one and reurn o Sep 2. ε s a chosen posve hreshold. 3. RESULTS In hs secon, we presen a few resuls of he exensve smulaons carred ou n order o segmenaon of he EEG sgnals mage by usng fuzzy c-mean. Three cluser ceners of he fla EEG gven n Table 1. These daa are ransformed no mage form a every me by usng (9. The obaned mage forms are segmened no regons by usng FCM n Malab roune program (Fg. 1 and DISCUSSION We have shown ha he EEG sgnals durng eplepc sezure can be ransformed no mage form a every me and hen s segmened no regons. The same color on he segmenaon a me show he locaons of he source of elecrc curren whch has nearly equal curren srengh. Table 1. Poson and elecrcal poenal of he cluser ceners a wo dfferen mes Poson Tme Elecrcal poenal (second x y (µv h h Fg. 1. Segmenaon of EEG sgnals a me 9h 1833

5 Muhammad Abdy and Tahr Ahmad / Amercan Journal of Appled Scences 11 (10: , 2014 Fg. 2. Segmenaon of EEG sgnals a me 14h 5. CONCLUSION 8. REFERENCES Ths sudy wll enable us o predc e locaons of curren sources whch have smlar srenghs. Furher research can use oher segmenaon mehod o compare he resuls of hs sudy. 6. ACKNOWLEDGMENT The researchers graefully acnowledge he revewers for he consrucve commens. 7. ADDITIONAL INFORMATION 7.1. Fundng Informaon Ths research wor was funded by he Governmen of Souh Sulawes Provnce, Indonesa and Unvers Tenolog Malaysa 7.2. Auhor s Conrbuons Muhammad Abdy prepared, developed and publshed of hs manuscrp, and Tahr Ahmad revsed and approved he fnal verson of hs manuscrp Ehcs There are no ehcal ssues nvolved because hs s our orgnal research wor Abdy, M., Fuzzy opologcal dgal space and mage form of feeg durng eplepc sezures. PhD Thess, Unvers Tenolog Malaysa, Johor, Malaysa. Ahmad, T. and T.L. Ken, 2010, Topologcal conugacy beween sezure and fla elecroencephalography. Am. J. Appled Sc., 7: DOI: /aassp Adel, H., A wavele-chaos mehodology for analyss of EEGs and EEG Sub bands o deec sezure and eplepsy. IEEE Trans. Bomed. Eng., 54: DOI: /TBME Bezde, J.C., Paern Recognon wh Fuzzy Obecve Funcon Algorhms. 1s Edn., Plenum, New Yor, pp: 256. Bnadhnan, F.A.M. and T. Ahmad, 2010, EEG sgnals durng eplepc sezure as a semgroup of upper rangular marces. Am. J. Appled Sc., 7: DOI: /aassp Bloch, I., H. Maur and M. Anvar, Fuzzy adacency beween mage obecs. In. J. Uncerany Fuzzness Knowledge-Based Sys., 5: DOI: /S Demo, C. and E.H. Zahza, Image undersandng usng fuzzy somorphsm of fuzzy srucures. Proceedngs of he IEEE Inernaonal Conference on Fuzzy Sysem, Mar , IEEE Xplore Press, Yoohama, Japan, pp: DOI: /FUZZY

6 Muhammad Abdy and Tahr Ahmad / Amercan Journal of Appled Scences 11 (10: , 2014 Engel, J.J.R., P.C. Van Ness, T.B. Rasmussen and L.M. Oemann, Oucome wh Respec o Eplepc Sezures. In: Surgcal Treamen of he Eplepses. J. Engel Jr., (Ed., Raven Press, New Yor, ISBN-10: , pp: Guo, L., D. Rvero and A. Pazos, 2010, Eplepc sezure deecon usng mulwavele ransform based approxmae enropy and arfcal neural newors. J. Neusc. Meh., 193: DOI: /.neumeh , PMID: Sayad, M., L. Tlg and F. Fnaech, A new exure segmenaon mehod based on he fuzzy c-mean algorhm and sascal feaures. Appled Mah. Sc., 1: Subas, A., Eplepc sezure deecon usng dynamc wavele newor. Exper Sys. Applc., 29: DOI: /.eswa Zaara, F., Dynamc proflng of EEG daa durng sezure usng fuzzy nformaon. PhD Thess, Unvers Tenolog Malaysa, Johor, Malaysa. Zhanga, Z., H. Kawabaab and Z. Lu, Elecroencephalogram analyss usng fas wavele ransforms. Compu. Bol. Medcne, 31: DOI: /S (