School Of Biomedical Engineering, Institute Of Technology, Banaras Hindu University,. Varanasi (India) - 221005. E-mail: [email protected].
Publis hed Quar ter ly Mangalor e, S outh I ndia I S S N 0972- 5997 Volume 1; I s s ue 4; October - December 2002
S h or t Com m u n icat ion
B ackpr opagat ion Ar t if icial N eu r al N et w or k T o D et ect H yper t h er m ic S eiz u r es I n R at s R akes h K u m ar S i n h a S chool Of B iomedical Engineer ing, I ns titute Of T echnology, B anar as Hindu Univer s ity, Var anas i (I ndia) - 221005. E- mail: r ks inha_ r es @ r ediffmail.com Cor r es pon din g Addr es s : Rak es h K umar S inha, C/O S r i D. P. S inha, S ector 5 ‘D’/2003, B okar o S teel City, Jhar khand (I ndia) – 827006.Email: r ks inha_ r es @ r ediffmail.com Cit at ion : S inha RK , B ackpr opagation Ar tificial Neur al Networ k T o Detect Hyper ther mic S eiz ur es I n Rats . Online J Health Allied S cs . 2002; 4: 1 U R L : http: //www.oj has .or g/is s ue3/2002- 4- 1.htm Open Acces s Ar ch ive: http: //cogpr ints .ecs .s oton.ac.uk/view/s ubj ects /OJHAS .html Abs t r act A thr ee- layer ed feed- for war d back- pr opagation Ar tificial Neur al Networ k was us ed to clas s ify the s eizur e epis odes in r ats . S eizur e patter ns wer e induced by s ubj ecting anes thetized r ats to a B iological Ox ygen Demand incubator at 45- 47º C for 30 to 60 minutes . S elected fas t Four ier tr ans for m data of one s econd epochs of electr oencephalogr am wer e us ed to tr ain and tes t the networ k for the clas s ification of s eizur e and nor mal patter ns . T he r es ults indicate that the pr es ent networ k with the ar chitectur e of 40- 12- 1 (input- hidden- output nodes ) agr ees with manual s cor ing of s eizur e and nor mal patter ns with a high r ecognition r ate of 98.6% . K eyw or ds : Ar tificial Neur al Networ k, fas t Four ier tr ans for m, electr oencephalogr am, Hyper ther mic s eizur es I n t r odu ct ion Heat s tr oke or hyper ther mia is one of the mos t s er ious of the dis or der s that may caus e s eizur es . Liter atur es s ugges t that continuous ex pos ur e to high envir onmental heat as well as by hot water pour over the head gener ate s eizur es in both man and animals .(1,2) S ever al computer algor ithms and pr ogr ams for automatic detection of epileptic tr ans ients wer e developed but thes e methods wer e found unable to r ecognize the ex ceptions and minimize the number of fals e detections .
Alter natively, Ar tificial Neur al Networ k (ANN) has been s ucces s fully implemented for many patter n clas s ification pr oblems including detection of epileptic s eizur es .(3- 5) However , mos t of the pr evious ANN bas ed methods us e meas ur es of the electr oencephalogr am (EEG) s uch as amplitude, width, s lope and s har pnes s of s er ies of cons ecutive waves , meas ur es thought to r eflect in a gener al s ens e what ex per t clinicians attempt dur ing EEG inter pr etation. I n the pr es ent wor k, ins tead of us ing the phys ical char acter is tics of EEG s ignals , fas t Four ier tr ans for m (FFT ) has been us ed for the tr aining and tes ting of the ANN as it conveys mor e infor mation with r es pect to conventional analog EEG r ecor ds .(6) T he ex per iment was car r ied out on male Char les Fos ter r ats weighing 200- 250 gr ams . Rats wer e hous ed in the animal r oom that was ar tificially illuminated with a 12 light cycle (7.00 A.M. to 7.00 P. M.) and the ambient r oom temper atur e maintained at 24± 1º C. Rats wer e anaes thetized with Ur ethane anaes thes ia (1.6gm/kg, I .P.) and thr ee s tainles s s teel s cr ew electr odes wer e as eptically fix ed on the r at’s head under s ter eotax ic guidance. T wo electr odes wer e placed on bilater al fr onto- par ietal r egion and one gr ounding electr ode at the anter ior mos t r egion of the s kull to r ecor d the differ ential EEG patter ns . Anaes thetized r ats after electr ode implantation wer e s ubj ected to the ther mal envir onment in the B iological Ox ygen Demand (BOD) incubator with pr es et temper atur e at 45- 47º C.(1)
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S eizur e patter ns in EEG r ecor ding wer e obs er ved after 30 to 60 minutes on s tar t of incubation. S ingle channel analog EEG was r ecor ded with the s tandar d amplifier s etting.(7) S ignals wer e s imultaneous ly r ecor ded in the computer har d dis k following digitization of the tr aces at 256 Hz with help of an analog to digital conver ter (ADLiNK, 8112HG, NuDAQ, T aiwan) and its s uppor ting s oftwar e (VI S UAL LAB - M, Ver s ion 2.0c, B lue Pear l labor ator y, US A). T he digitized data wer e fr agmented in 1 s econd epochs (256 data points ) and s tor ed in s epar ate files . Each epoch was pr epr oces s ed for nois e r eduction befor e final FFT or power s pectr um analys is . At fir s t, the DC value was s ubtr acted fr om the data and then the bas e line movement was r educed. I n the final s tep of pr epr oces s ing, the data wer e band pas s filter ed with cutoff fr equencies of 0.25 and 30 Hz, as the max imum fr equency component of inter es t in anes thetized animal is les s than 25 Hz.(8) T hes e filter ed data epochs wer e pr oces s ed for FFT or power s pectr um calculation befor e being us ed as input for ANN. T hr ee layer ed feed- for war d back- pr opagation networ k was us ed for detecting the s eizur es . T he networ k was implemented via s oftwar e by us ing C+ + pr ogr amming language on a computer .(9) T he individual computational elements that make up mos t ar tificial neur al s ys tems models ar e mor e often r efer r ed to as pr oces s ing elements (PEs ). Like a neur on, a PE has many inputs but only s ingle output, which can fan out to many other PEs in the networ k. T he input ith r eceives fr om the j th PE is indicated as x j . Each connection to the ith PE has as s ociated with a quantity called weight or connection s tr ength. T he weight on the connection fr om the node j th to ith node is denoted as wij . Each PE deter mines a net input value bas ed on all it’s input connection .(10) T he net input is calculated by s umming the input values , gated (multiplied) by their cor r es ponding weights . I n other wor ds , the net input to the ith unit can be wr itten as :
net i =
x j wij
B ackpr opagat i on n et w or k: T he back- pr opagation lear ning involves pr opagation of the er r or backwar ds fr om the output layer to the hidden layer s in or der to deter mine the update for the weights leading to the units in a hidden layer . I t does not have feedback connections , but er r or s ar e back pr opagated dur ing tr aining by us ing leas t mean s quar e (LMS ) er r or . Er r or in the output deter mines meas ur es of hidden layer output er r or s , which ar e us ed as a bias for adj us tment of connection weights between the input and hidden layer s . Adj us ting the two s ets of weights between the pair of layer s and r ecalculating the outputs is an iter ative pr oces s that is car r ied on until the er r or falls below a toler ance level. Lear ning r ate par ameter s s cale the adj us tments to the weights . T he input of a par ticular element was calculated as the s um of the input values multiplied by connection s tr ength (s ynaptic weight).(11) ANN was tr ained by FFT data of s elected EEG data files . Dur ing tr aining, the networ k was pr ovided the inputs and the des ir ed outputs , and the weights wer e adj us ted accor dingly s o as to minimize the er r or between ex pected and des ir ed outputs . After the tr aining, the networ k was tes ted with unknown input patter ns that wer e not pr es ent in the tr aining s et. R es u l t s T he par ameter s of the ANN wer e s et to get optimized per for mance of the networ k pr ogr am over the entir e s et of EEG data. T he tr aining of the ANN was tr ied with var iable number of hidden neur ons as well as by as s igning differ ent lear ning r ates par ameter s between the r anges of 0.01 to 0.5. T he optimized per for mance of the ANN was found with s tr uctur es of 40- 12- 1 (nodes of input, hidden and output) and with the lear ning r ate of 0.1. T he s chematic diagr am of the neur al networ k us ed in the pr es ent s tudy is s hown in Fig.- 1.
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Figur e- 1: S chematic diagr am of patter n r ecognition by ANN. For the pr es ent wor k, the er r or toler ance was as s igned as 0.001 to activate the networ k and the networ k was tr ained for 1 million of iter ations with differ ent tr aining s ets having var iable number of tr aining patter ns . T he ANN was tr ained with a tr aining data file containing 100 tr aining patter ns (s ame number of s eizur es and nor mal patter ns ) ar r anged r andomly. After tr aining, the networ k was tes ted for other files having patter ns which wer e not pr es ent dur ing tr aining s es s ion. T he per for mance of the networ k in detecting thes e events (nor mal and s eizur e) was calculated with help of following for mula. Per for mance of ANN(% ) =
Number of cor r ectly clas s ified patter ns T otal number of patter ns tes ted
X 100
T he r es ults of the s eizur e and nor mal events detected by the networ k compar ed with thos e detected manually ar e s ummar ized in the T able- 1. Manually detected events wer e taken as s tandar d and agr eement per centage r epr es ent the per centage of epochs in which ANN detected s eizur e or nor mal events agr eed with manually detected ones .
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T abl e 1 : Per centage agr eement of the ANN in the r ecognition of s eizur e and nor mal patter ns in compar is on with manual s cor ing. F i l e N o. N o. of N u m ber of cor r ect ly det ect ed pat t er n s T es t pat t er n s S ei z u r e N or m al T ot al 1. 200 98 100 198 2. 200 98 99 197 3. 200 100 98 198 4. 200 99 98 197 5. 200 97 99 196 T otal patter ns 1000 492 494 986 tes ted (% agr eement = 98.6) D i s cu s s i on Ackn ow l edgem en t s I n the pr es ent wor k, an appr oach of detection of T he author is gr ateful to Dr . Amit Kumar Ray, hyper ther mia induced s eizur e and nor mal EEG Reader , S chool of B iomedical Engineer ing, I ns titute patter ns thr ough ANN has been s ucces s fully of technology, B anar as Hindu Univer s ity, Var anas i implemented and ex per imentally tes ted. Featur es (I ndia) for pr oviding neces s ar y facilities for EEG data calculated fr om the FFT s uch as r elative power in collection and pr oces s ing for the ex per iment. var ious fr equency bands and then us ing an ANN to R ef er en ces 1. Mor imoto T , Nagao H, S ano N, et al. gener ate a s ingle number that indicates the degr ee E lectr oencephalogr aphic s tudy of r at hyper ther mic of which the event is a s eizur e (3, 12) was us ed s eizur es . E pileps ia. 1991 ; 32 (3): 289- 9 3. pr evious ly to clas s ify s eizur e patter ns . I ns tead of the 2. Ullal GR, S atis hchandr a P, S hankar S K. featur es fr om the FFT of the EEG s ignals , in the Hyper ther mic s eizur es : an animal model for hot pr es ent wor k, the s elected fr equency band of digital water epileps y. S eizur e. 1 996; 5 (3): 221 - 28. values of the FFT fr om one s econd epochs of the EEG 3. Jandó G, S eigel RM, Hor và th Z et al. Patter n r ecognition of the electr oencephalogr am by s ignals for the tr aining and tes ting of the ANN wer e ar tificial neur al networ ks . E lectr oencephal clin us ed. T he EEG s pike patter ns r epr es ent ver y good Neur ophys iol. 1993 ; 86 : 100 - 9. agr eement with the human manual s cor ing.(3) T he 4. Webber WRS , Les s er RP, Richar ds on RT et al. An per for mance of the detector was obs er ved with appr oach to s eizur e detection us ing an ar tificial moder ately high r ecognition r ate of 98.6% in neur al networ k. E lectr oencephal clin Neur ophys iol. r ecognizing nor mal and s eizur e patter ns . T he r es ults 1996; 9 8: 250 - 72. s ugges t that ANN is capable of clus ter ing the input 5. Gabor AJ, Leach RR, Dowla FU. Automated s eizur e detection us ing a s elf or ganiz ing neur al networ k. infor mation with gr eater r eliability s imilar as s hown E lectr oencephal clin Neur ophys iol. 1996; 99 : 257 by Hopfield and T ank (13) and thes e analys es can 66. s ubs tantially incr eas e the power of analys is . Once 6. S ar badhikar i S N. A Neur al networ k confir ms that the ANN is tr ained, the conver ged weights wer e phys ical ex er cis e r ever s es E E G changes in s tor ed and r e- us ed to obtain ins tantly the r es ult of depr es s ed r ats . Med E ngg & Phy. 1995; 17 (8): s eizur e detection. T he accur acy of r ecognition 579- 82. however , was found s ens itive to s ever al par ameter s 7. S ar badhikar i S N, Dey S , Ray AK. Chr onic ex er cis e alter s E E G power s pectr a in an animal model of s uch as the r ecor ding envir onment, the type of depr es s ion. I ndian J Phys iol Phar macol. 1996; s ignals us ed, s ample s ize, tr aining method, the 40(1): 47- 5 7. choice of networ k model and pr epr oces s ing of 8. Goel V, B r ambr ink AM, B aykal A et al. Dominant s ignals . Although in this wor k, online s eizur e fr equency analys is of E E G r eveals br ain’s r es pons e detection has not been done, which may be pos s ible dur ing inj ur y and r ecover y. I E E E T r ans B iomed with the help of fas t computer and dedicated E ngg. 199 6; 43(11 ): 1 083- 92 . s oftwar e. 9. Rao V, Rao, H. C+ + Neur al networ k s and fuz z y logic. F ir s t E dition. New Delhi: B PB Publications ; T he advantages and dis advantages of ANN in the 1996. p. 123 - 76. clinical diagnos is have not been ex tens ively ex plor ed 10. Fr eeman JA, S kapur a, DM. Neur al Networ k s : yet. However , by application of thes e r es ults , the Algor ithms , Applications and Pr ogr amming futur e s cope can be outlined. T he ANN can be us eful T echniques . Addis on Wes ley: F ir s t I S E r epr int; in differ ential diagnos is becaus e the networ k can be 1999. tr ained with lar ge data s ets der ived fr om patients 11. Rumelhar t DE , Hinton GE , Williams RJ. Lear ning with clear - cut, but clinically differ ent dis eas es . S ince r epr es entations by back- pr opagating er r or s . Natur e. 1986 ; 32 3: 533 - 36. only 1- 5% of long ter m r ecor ding of EEG s ignals ar e 12. S har ma A, Wils on S E , Roy R. E E G clas s ification for of inter es t in clinical diagnos is ,(3) the ANN can es timating anes thetic depth dur ing halothane become us eful for online monitor ing of pathological anes thes ia. I n Pr oceedings of 14 th annual events . Fur ther mor e, the technicians can eas ily be inter national confer ence I E E E E ngineer ing in tr ained for the manual s election of the alr eady Medicine and B iology S ociety, New Yor k. 1992. p. detected events , wher eas r ecognition of abnor mal 2409- 10. patter ns in the backgr ound of ongoing EEG r equir es 13. Hopfield JJ, T ank DW. Computing with neur al s ubs tantial ex per ience. cir cuit: a model. S cience. 1 986; 223 : 625- 33 .
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