Arrhythmia Detection based on Morphological and Time-frequency Features of T-wave in Electrocardiogram ABSTRACT

Transcription

1 Orgnal Artcle Arrhythma Detecton based on Morphologcal and Tme-frequency Features of T-wave n Electrocardogram Elham Zeraatkar, Saeed Kerman, Alreza Mehrdehnav 1, A. Amnzadeh 2, E. Zeraatkar 3, Hamd Sane 4 Departments of Physcs & Bomedcal Engneerng and 4 Internal Medcne, 1 Medcal Image & Sgnal Processng Research Center, Isfahan Unversty of Medcal Scences, Isfahan, 2 Navgaton and Control, Marne Industres Organzaton, 3 Electronc and Computer Engneerng, Shraz Unversty, Shraz, Iran ABSTRACT As the T-wave secton n electrocardogram (ECG) llustrates the repolarzaton phase of heart actvty, the nformaton whch s accumulated n ths secton s so sgnfcant that t can explan the proper operaton of electrcal actvtes n heart. Long QT syndrome (LQT) and T-Wave Alternans (TWA) have mperceptble effects on tme and ampltude of T-wave nterval. Therefore, T-wave shapes of these dseases are smlar to normal beats. Consequently, several T-wave features can be used to classfy LQT and TWA dseases from normal ECGs. Totally, 22 features ncludng 17 morphologcal and 5 wavelet features have been extracted from T-wave to show the ablty of ths secton to recognze the normal and abnormal records. Ths recognton can be mplemented by pre-processng, T-wave feature extracton and artfcal neural network (ANN) classfer usng Mult Layer Perceptron (MLP). The ECG sgnals obtaned from 142 patents (4 normal, 47 LQT and 55 TWA) are processed and classfed from MIT-BIH database. The specfcty factor for normal, LQT, and TWA classfcatons are 99.89%, 99.9%, and 99.43%, respectvely. T-wave features are one of the most mportant descrptors for LQT syndrome, Normal and TWA of ECG classfcaton. The morphologcal features of T-wave have also more effect on the classfcaton performance n LQT, TWA and normal samples compared wth the wavelet features. Key words: ECG, feature extracton, morphology, neural network, T-wave, wavelet Introducton The long QT syndrome (LQTs), T-wave alternans (TWA), and ventrcular tachyarrhythma (VT) are some of the common cardac dseases whch cause sudden cardac death (SCD) n the world. [1,2] Many studes have been developed to detect an abnormal snus ECG based on the features of ECG sgnal. Most of these artcles use QRS complex to ndentfy the arrhythma of the heart. One of the tradtonal methods has been performed by Jan [3] that dgtzed and represented each ECG lead by ts z-doman modes to enhance the dscrmnaton of the subtle changes n P, QRS, and T sectons, the dervatves of the waves are employed for extracton of the modes. Ln et al. [4] used lnear predcton to extract features from QRS complexes. Osowsk et al. [5] appled fuzzy neural network to ECG beat recognton and classfcaton and the features drawn from the hgher order statstcs have been proposed n the study. Also Engn [6] performed smlar method and used autoregressve model coeffcents, hgher-order cumulant, and wavelet transform varances as features to enhance the performance. Jekova et al. [7] mplemented four dfferent classfers based on 26 morphologcal features whch have been extracted from lead I, II, and the Frank Leads or vector cardograph (VCG) trajectory sgnals, such as area, slopes, peaks, tme ntervals, and VCG dagram n QRS complex. Asl et al. [8] presented an effectve cardac arrhythma classfcaton algorthm based on the generalzed dscrmnant analyss (GDA) to reduce feature scheme usng support vector machne (SVM) classfer. Intally, 15 dfferent lnear and nonlnear features have been extracted from QRS complex and then reduced to only 5 features by the GDA technque. Vaglo et al. [9] and Couderc et al. [1] mplemented a computer algorthm to dentfy the dfferentaton of LQT1 and LQT2 carrers base on T-wave morphology features, such as the Q to T-peak (QT-peak), the T-peak to T-end nterval, T-wave magntude, and T-loop slopes n these studes. In recent works, [11,12] smulated and synthetc TWA sgnals were generated. These augmented beats were detected usng wavelets and 91% senstvty was acheved. In other studes, wavelet/fft [13] and correlaton/fft methods [14] were also consdered. The novelty of ths work can be explaned as follows. In Address for correspondence: Dr. Saeed Kerman, Department of Physcs and Bomedcal Engneerng, Isfahan Unversty of Medcal Scences and Health Servces(IUMS), Hezar Jarb Street, Isfahan, Iran. E-mal: kerman@med.mu.ac.r Vol 1 Issue 2 May-Aug

2 ths study, dseases are recognzed from the beats that seem normal (snus ECG), but n fact, they belong to LQT or TWA classes. However, other works only classfy heartbeat types. [11-14] Consequently, prevous studes are not comparable wth ths approach. In ths artcle, dseases are classfed by the followng procedures: Pre-processng, QRS-complex detecton, T-wave detecton, features extracton from T-wave secton (morphologcal and wavelet coeffcents), and classfcaton usng MLP artfcal neural networks. Method Arrhythma detecton algorthm s mplemented as follows: (a) recallng sutable ECG database; (b) pre-processng; (c) QRS-complex detecton; T-wave detecton; (d) feature extracton from T-wave; and (e) MLP classfer as shown n Fgure 1. ECG Database and Pre-processng In ths artcle, the MIT/BIH database [15-17] has been chosen wth 4 normal records, 47 LQT syndrome records, and 55 sets of TWA arrhythma from 142 ECG recordngs Lead I wth 128Hz, 25Hz, and 5Hz samplng rate, respectvely. Before applyng detectons, feature extracton and classfyng procedure n ths experment, several preprocessngs are necessary to obtan an approprate result and reduce errors n processng and detecton phases. Most common artfacts and drfts appear by 5 Hz of 6 Hz power lne nterface, muscle contractons of electromyography nose (EMG), baselne drft and ECG ampltude modulaton wth respraton, and ECG corrupton wth abrupt baselne shft. [18] To avod these dsturbances, the followng flters are appled: a. To elmnate power lne effect a notch flter [19] has been developed wth the followng transfer functon: ( z z1)( z z2) Hz ( ) = (1) ( z p1)( z p2) Where z And z = cos( ) + jsn( ) (2) 1 = cos( ) jsn( ) 2 are the zeros of transfer functon and p1 = k[cos( ) + jsn( )] (4) And 1 (3) ECG database T-wave detecton Feature extracton Classfcaton p = k[cos( ) jsn( )] (5) 2 are the poles of the transfer functon wth pole/zero rato k=.9, cutoff frequency ω =±[ f / f s ] (2πr), center frequency f = 5 Hz and samplng rate f s. b. To reduce the effect of EMG nose, a dscrete Butterworth flter wth order 8 and cutoff frequency f c = 7 Hz and samplng rate f s. c. For decreasng the amount of ECG baselne drft wth respraton the followng method s appled: [2] 1. Computng medan of the ECG 2. Shftng ECG by ths medan value 3. Fttng a 4 th degree polynomal to the shfted ECG 4. Shftng ECG by ths calculated polynomal 5. Detectng the R peaks of ECG 6. Computng medan of each RR nterval the ECG 7. Shftng each RR nterval by ts medan value d. For ECG corrupton wth abrupt baselne shft the algorthm mentoned n part(c) s also appled to the ECG sgnal. Nose reducton and robustness of mplemented algorthm wth above artfacts have been dscussed n another study. [21] QRS Complex Detecton In ths secton, the QRS complexes of the ECG are detected and elmnated form overall ECG to prepare the sgnal for T-wave detecton. Ths wll be mplemented by the followng steps: [22] 1. Recall ECG sgnal S(n) and compute square of ths sgnal after pre-processng: TS1(n) = S(n)*S(n) (6) 2. Evaluate the steepest wndowed gradent of TS1(n) by usng a rectangular sldng wndow wth 11 ponts from sample n-5 to n+5: G1(n) = TS1 max (w)-ts1 mn (w) (7) 3. Smooth the sgnal by usng a movng average method from sample n-5 to n+5 wth center n: n+ 5 1 FG1( n) = G1() (8) 11 = n 5 Pre-processng Fgure 1: Block dagram of algorthm QRS detecton Vol 1 Issue 2 May-Aug 211

3 4. Normalze the followng values by ther respectve maxmum peak ampltude: S(n), TS1(n), G1(n) and FG1(n). 5. Transform the ECG sgnal by a sgmod functon: C Q 1, f FQ ( n) > 5. ( n) =, otherwse T-Wave Detecton (17) TS2( n) = 1 2 e Sn ( ) Evaluate the steepest wndowed gradent of TS2(n) by usng a rectangular sldng wndow wth 11 ponts from sample n-5 to n+5. G2(n) = TS2 max (w)-ts2 mn (w) (1) and smooth t to FG2(n) lke step Normalze the followng values by ther respectve maxmum peak ampltude: TS2(n), G2(n) and FG2(n). 8. Multply by ECG wth FG2(n): TS3(n) = FG2(n)*S(n) (11) 9. Evaluate the steepest wndowed gradent of TS3(n) by usng a rectangular sldng wndow wth 11 ponts from sample n-5 to n+5. G3(n) = TS3 max (w)-ts3 mn (w) (12) and smooth t to FG3(n). 1. Normalze the followng values by ther respectve maxmum peak ampltude: TS3(n), G3(n) and FG3(n). 11. Compute: TS4(n) = FG1(n)+ FG3(n) (13) 12. Shft the resultng sgnal by medan m : TS4m(n) = TS4(n)-m (14) 13. Normalze TS4m(n) as: Pre_F Q (n) = TS4m(n)/max(abs(TS4m(n))) (15) 14. F Q (n) s derved by retanng the ampltude values of Pre_FQ exceedng 5% of ts maxmum peak ampltude and reducng the remanng to zero: F Q Pr e_ FQ( n) f Pr e_ FQ( n) > 5. ( n) = otherwse (9) (16) 15. C Q s the proposed feature sgnal employed for dentfyng QRS out of ECG sgnal. Ths sgnal s dgtalzed verson of F Q (n): To extract features from T-wave secton of ECG sgnal the nterval of T-wave segments should be separated from other parts of sgnal. There are several methods to detect ths secton. [23-26] One of the latest approaches can be done by the followng steps after elmnatng negatve values and QRS parts of ECG sgnal n each of the followng steps: 1. fc1: Feature #1 s calculated from the frst dervatve of ECG sgnal 2. fc2: Feature #2 s calculated from fltered gradent, whch means the ECG sgnal passes through a sgmod functon, wndowed gradent, and smoothed by a movng average wndow 3. fc3: Feature #3 s calculated from the product of fltered gradent ECG and fc2 4. fc4: Feature #4 s calculated from the combnaton of fc1, fc2, fc3 and absolute value of ECG: [fc1+fc2+fc3+ S(n)]* S(n) 5. fc5: Feature #5 s calculated from another combnaton of fc1, fc2, fc3 and absolute value of ECG: fc1+fc2+fc3+ S(n). Then the summaton of these fve features s computed by the followng formula: Pre_F NQ (n)=fc1(n)+ fc2(n)+ fc3(n)+ fc4(n)+ fc5(n) (18) Fnally the man feature wll be computed by: F NQ Pr e_ FNQ( n), f Pr e_ FNQ( n) 1 ( n) = 1, f Pr e_ FNQ( n) > 1, f Pr e _ FNQ ( n )< 1 (19) The values greater than 2% of the maxmum of ths feature wll show the P-wave and T-wave regons n ECG whch s marked as pulses. The pulses occur before QRS complexes ndcate P-waves and the pulses after QRS shows T-waves as depcted n Fgure 2. The detals and exact formulaton of ths procedure can be found n artcles. [27] Feature Extracton Detectng T-waves from ECG sgnal prepares the feld to extract necessary descrptors from these parts of sgnal. Totally, 22 features have been consdered and extracted from T-wave whch consst of two fundamental types of features; There are 17 morphologcal features and 5 wavelet features. Morphologcal features nclude ampltude of T-wave, statc and dynamc rsng and fallng slopes, areas of rsng and fallng segments, fve slopes and fve areas respect to splt Vol 1 Issue 2 May-Aug

4 fallng segment of T-wave nto fve sectons. The other type of features s varances of Daubeches wavelet coeffcents whch decomposed T-wave segment nto fve levels. These features have been summarzed n Table 1 and shown n Fgure 3a-d. As t s evdent n Fgure 4, the T-wave of three Separatng T wave parts and QRS complexes sample Fgure 2: QRS-Complex and T-Wave separatng pulses classes (normal, LQT, and TWA) are very smlar together and cannot recognze smply. For ths reason, 22 features are selected to classfy these types from each other. Classfcaton The classfcaton of dseases s based on Mult Layer Perceptron (MLP) usng Artfcal Neural Network (ANN) [28,29] wth 22 neurons at nput, 14 at hdden layer, and one at output whch generates 3 nteger numbers for 3 classes. All Table 1. Feature descrpton extracted from T-wave Feature No. Notaton Descrpton 1 m sr Statc rsng slope 2 m sf Statc fallng slope 3 m dr Dynamc rsng slope 4 m df Dynamc fallng slope 5 T p Maxmum peak of T-wave 6 A r Rsng segment Area 7 A f Fallng segment Area 8-12 A f1,...,a f5 Fallng segment area splt nto fve parts m f1,...,m f5 Fallng segment slopes splt nto fve parts σ w1,...,σ w5 Varance of wavelet coeffcents Statc rsng and fallng slopes of T Wave msr msf a b.3 Dynamc rsng and fallng slopes of T wave mdr mdf Inflecton pont T p.2.2 mf mf2.1.1 Af1 A r A f.5.5 Af2 mf3 Af3 mf4 mf5 Af4 Af Sample 1 4 Sample c d Fgure 3: Some morphologcal features extracted from T-Wave. (a) Dynamc slopes and nfecton ponts of T- wave; (b) Statc slopes of T-wave; (c) Rsng and fallng areas of T-wave; (d) Fallng area and slopes of T-wave separatng to seqments 12 Vol 1 Issue 2 May-Aug 211

5 Fgure 4: Typcal wave forms fromt-wave secton of (a) Normal (b) LQT and (c) TWA of the 22 features have been scaled and appled to the nput of ANN are used and mplemented to an approprate ANN archtecture for tranng and testng as shown n Fgure 5. The output of network s determned as normal, LQT, and TWA abnormaltes. Results (a) (b) The dscussed approach s smulated and appled to normal and abnormal TWA and LQT databases of MIT/BIH arrhythma (c) Fgure 5: MLP neural network archtecture recordngs. The nput vectors, whch are developed from T-wave features of ECG, are collected and separated nto two parts for tranng and testng the MLP neural network. There are 22 scaled and extracted features contanng 17 morphologcal and 5 wavelet features. Snce the T-wave part of an ECG, specally fallng nterval, llustrates the repolarzaton phase of heart actvty, the nformaton whch s accumulated n ths secton s so sgnfcant that t can explan the proper operaton of electrcal actvtes n heart. Therefore, these features are rch descrptors for heart performance. The MIT-BIH database [15-17] has been chosen for mplementaton of the algorthm n ths study. The samples have been taken from three ECG types totally 142 records wth the followng propertes: MIT-BIH Normal Snus Rhythm Database (4 records, 128Hz samplng rate); The QT Database (47 records, 25Hz samplng rate); T-Wave Alternans Challenge Database (55 records, 5Hz samplng rate). The learnng process to tran MLP neural network has been mplemented wth three dfferent learnng sets: 5, 6, and 7% heartbeats of total recordngs. For estmatng the performance of dscussed approach, three dfferent feature vectors have been developed and tested: Only wavelet features, only morphologcal features, and both features together. Fnally, four performance ndces based on ROC (Recever Operatng Characterstcs) were computed for normal and abnormal classes: senstvty (Se ), specfcty (Sp ), postve predctve value (PPV ) and negatve predctve value (NPV ). They are calculated accordng to the followng relatons: [3] TN Sp =, TN + FP TN NPV = TN + FN TP Se = TP + FN TP (2), PPV = TP + FP Vol 1 Issue 2 May-Aug

6 where TP are the number of true postves, TN are true negatves, FP are false postves, and FN are false negatves. The results are representaton lsted n Tables 2-4 accordng to dfferent learnng sets and descrptor vectors. As t s evdent n the results, the network performance dffers by changng learnng sets and changng the types of features. The statstcal measurements for three features have been depcted n Fgure 6 to show the ablty of features for the classfcaton of dseases. Ths descrbes that the morphologcal propertes, such as fallng area and fallng slope of T-wave, have dfferent dstrbuton, mean, and varance and can be used for ECG classfcaton. 5 th fallng area feature 5 th fallng slope feature 1 st wavelet feature Fgure 6: Statstcal dstrbuton for three features: 5 th fallng area, 5 th fallng slope and 1 st wavelet coef Normal TWA LQT 14 Normal TWA LQT Normal Mean value ±Standard error: o TWA LQT ±Standard devaton: I Conclusons In prevous bomedcal studes, detectng normal and abnormal beats are consdered by applyng several methods. These procedures verfy the varatons of one Table 2: Testng results for normal ECG sgnal Normal Feature set #1 5 descrptors (Wavelets) Feature set #2 17 descrptors (Morphology) Feature set #3 22 descrptors (Total) Learnng set #1 (5% of data) Sp Se NPV PPV* Learnng set #2 (6% of data) Sp Se NPV PPV Learnng set #3 (7% of data) Sp Se NPV PPV Sp Specfcty; Se Senstvty; NPV Negatve predctve value; * PPV Postve predctve value; Fgures are n percentage Table 3: Testng results for LQT ECG sgnal LQT Feature set #1 5 descrptors (Wavelets) Feature set #2 17 descrptors (Morphology) Feature set #3 22 descrptors (Total) Learnng set #1 (5% of data) Sp Se NPV PPV Learnng set #2 (6% of data) Sp Se NPV PPV Learnng set #3 (7% of data) Sp Se NPV PPV Sp Specfcty; Se Senstvty; NPV Negatve predctve value; * PPV Postve predctve value; Fgures are n percentage Vol 1 Issue 2 May-Aug 211

7 Table 4: Testng results for TWA ECG sgnal TWA Feature set #1 5 descrptors (Wavelets) beat aganst others to fnd out abnormal beats. Regardng the new suggestons of cardologsts, some dseases, such as LQT and TWA, have mperceptble effect on tme and ampltude of T-wave nterval. In contrary to prevous artcles, n ths work, dsease detecton (LQT syndrome and TWA) has been performed usng apparently normal beats. In some researches, several algorthms have been developed for T-wave detecton. The accuracy reported n these artcles s satsfactory. [24-26,28,29,31] In ths study, the T-wave detecton based on threshold method developed n [28] wth 96.98% accuracy s used. Ths performance s more accurate compared wth other methods. Accordng to the acheved results, t s obvous that T-wave features n snus ECG sgnals have the capablty to separate these dseases. Snce the fallng slope of the T-wave s assocated wth the repolarzaton phase of heart actvty and preparng of heart muscles for next oscllaton, ths secton contans sgnfcant morphologcal descrptors and has the necessary nformaton to classfy heartbeats. The specfcty of mentoned approach depends on the quantty of learnng set and feature types for neural network tranng. The morphologcal features of T-wave have also more effect on the classfcaton performance n LQT, TWA, and normal samples compared wth wavelet features. Acknowledgment Feature set #2 17 descrptors (Morphology) Feature set #3 22 descrptors (Total) Learnng set #1 (5% of data) Sp Se NPV PPV Learnng set #2 (6% of data) Sp Se NPV PPV Learnng set #3 (7% of data) Sp Se NPV PPV Sp Specfcty; Se Senstvty; NPV Negatve predctve value; * PPV Postve predctve value; Fgures are n percentage Ths study was supported by Isfahan Unversty of Medcal Scences and Shraz Kowsar Heart Clnc, wth specal thanks to Dr. M.H. Nkoo, Dr. P. Setoodeh, Ms. N. Karam and Mr. A. Fakhrpour. REFERENCES 1. Takag M, Yoshkawa JT. Wave alternans and ventrcular tachyarrhythma rsk stratfcaton: A Revew. Indan Pacng Electrophysol J 23;3: Bernardo D, Murray A. Explanng the T-wave shape n the ECG. Nature 2;43:4. 3. Jan VK. ECG waveform feature extracton and ts applcaton to automated prognoss. Int J Parallel Programmng 1973;2: Ln KP, Chang WH. QRS Feature Extracton Usng Lnear Predcton. IEEE Trans Bomed Eng 1989;36: Osowsk S, Lnh TH. ECG beat recognton usng fuzzy hybrd neural network. IEEE Trans Bomed Eng 21;48: Engn M. ECG beat classfcaton usng neuro-fuzzy network. Pattern Recognt Lett 24;25: Jekova I, Bortolan G, Chrstov I. Assessment and comparson of dfferent methods for heartbeat classfcaton. Med Eng Phys 28;3: Asl B, Setarehdan SK, Mohebb M. Support vector machne-based arrhythma classfcaton usng reduced features of heart rate varablty sgnal. Artf Intell Med 28;44: Vaglo M, Couderc JP, McNtt S, Xa X, Moss AJ, Zareba W. A quanttatve assessment of T-wave morphology n LQT1, LQT2, and healthy ndvduals based on Holter recordng technology. Heart Rhythm 28;5: Couderc JP, McNtt S, Xa J, Zareba W, Moss AJ. Repolarzaton morphology n adult LQT2 carrers wth borderlne prolonged QTc nterval. Heart Rhythm 26;3: Box M, Cantó B, Cuesta D, Mcó P. Usng the Wavelet Transform for T-wave alternans detecton. Math Comput Model 29;5: Romero I, Grubb NR, Clegg GR, Robertson CE, Addson PS, Watson JN. T-wave alternans found n preventrcular tachyarrhythmas n CCU patents usng a wavelet transform-based methodology. IEEE Trans Bomed Eng 28;55: Je Zhao, Je Tan, Yan-Na Zhao, J-Ku Wang, Jan-Gong Xu, Fang-Zhou Xu. Detecton of T-wave alternans based on the maxmum of T waves. 3rd Internatonal Conference on Bonformatcs and Bomedcal Engneerng (ICBBE): Bejng; 29. p Ghaffar A, Homaenezhad MR, Atarod M, Rahman R. Detectng and quantfyng T-wave alternans usng the correlaton method and comparson wth the FFT-based method. Conference on Computers n Cardology: Bologna; 28. p The MIT-BIH Normal Snus Rhythm Database. Avalable from: [Last accessed on 21 Jun 1]. 16. Laguna P, Mark R, Golberger A, Moody GB. A database for evaluaton of algorthms for measurement of QT and other waveform ntervals n the ECG. Comp Card 1997;24: Avalable from: physonet.org/physobank/database/qtdb/ [Last accessed on 211 Sept 26]. 17. T-Wave Alternans Challenge Database. Avalable from: physonet.org/physobank/database/twadb/, [Last accessed on 21 Jun 1]. 18. Fresen GM, Jannett TC, Jadallah MA, Yates SL, Qunt SR, Nagle HT. A comparson of the nose senstvty of nne QRS detecton algorthms. IEEE Trans Bomed Eng 199;37: RM Rangayyan. Bomedcal Sgnal Analyss: A Case Study Apprach. John Wley & Sons, Inc., Rangayyan RM. Bomedcal sgnal analyss: A case study apprach. New York: JohnWley and Sons, Inc.; 22. Vol 1 Issue 2 May-Aug

8 21. Chouhan VS, Mehta SS. Total removal of baselne drft from ECG sgnal, n Proceedngs of Internatonal Conference on Computng: Theory and Applcatons: Kolkata; 27. p Zeraatkar E, Kerman S, Mehrdehnav AR, Amnzadeh A. Improvng QRS detecton for artfacts reducton. ICBME21: 17th Iranan Conference on Bomedcal Engneerng: Isfahan; 21. p ChouhanVS, Mehta SS. Detecton of QRS complexes n 12-lead ECG usng adaptve quantzed threshold. Int J Computer Sc Netw Secur 28;8: Krm S, Oun K, Ellouze N. T-Wave detecton based on an adjusted wavelet transform modulus maxma. Int J Bol Sc 26;1: L NQ, Wang ZS. T-Wave detecton n electrocardogram sgnal based on ndependent sub-band functon. Intellgent Syst Appl 29;2: Tan KF, Chan KL, Cho K. Detecton of the QRS-complex, P-wave, and T-wave n electrocardogram. Proceedngs of the Frst Internatonal Conference on Advances n Medcal Sgnal Processng and Informaton Processng, IEE conference publcaton, No p Daskalov IK, Chrstov 2 nd. Automatc detecton of the electrocardogram T-wave end. Med Bol Eng Comput 1999;37: ChouhanVS, Mehta SS. Threshold-based Detecton of P and T-wave n ECG usng New Feature Sgnal. (IJCSNS) Int J Computer Sc Netw Secur 28;8: Haykn S. Neural Networks: A Comprehensve Foundaton. New York: McMllan; Mehrdehnav AR. Classfcaton of the dfferent cancerous anmal tssues on the bass of ther 1H NMR spectra usng dfferent types of Artfcal Neural Networks. RPS Res Pharm Sc 27;2: Metz CE. Basc Prncples of ROC Analyss. Semn Nucl Med 1978;8: Goutas A, Ferd Y, Herbeuval JP. Dgtal fractonal order dfferentaton based algorthm for P and T-waves detecton and delneaton. ITBMRBM 25;2: How to cte ths artcle: Zeraatkar E, Kerman S, Mehrdehnav A, Amnzadeh A, Zeraatkar E, Sane H. Arrhythma Detecton based on morphologcal and tme-frequency features of T-wave n electrocardogram. J Med Sgn Sens 211;2:99-16 Source of Support: Nl, Conflct of Interest: None declared BIOGRAPHIES Elham Zeraatkar receved her B.S. Degree n Electrcal Engneerng from Sharat Techncal Unversty (Iran) n 24 and her M.Sc. degree n Bomedcal Engneerng from Departments of Physcs and Bomedcal Engneerng, Isfahan Unversty of Medcal Scences, Isfahan. Her nterest s n bomedcal sgnal processng, Fuzzy logc and Neural Network. Saeed Kerman obtaned hs BS from the Department of Electrcal Engneerng of Isfahan Unversty of Technology n Isfahan, Iran, 1987, and he receved the MS n Boelectrc Engneerng from Sharf Unversty of Technology, n 1992 and hs PhD n Boelectrc Engneerng at AmrKabr Unversty of Technology, Tehran, Iran, n 28. He s Assstant Professor of Medcal Engneerng at the Department of Medcal Physcs and Medcal Engneerng n the School of Medcne of Isfahan Unversty of Medcal Scences, Iran. Hs research nterests are n bomedcal sgnal and mage processng technques Alreza Mehrdehnav was born n Isfahan provnce at He had educated n Electronc Engneerng at Isfahan Unversty of Technology at He had fnshed Master of Engneerng n Measurement and Instrumentaton at Indan Insttute of Technology Roorkee (IIT Roorkee) n Inda at He has fnshed hs PhD n Medcal Engneerng at Lverpool Unversty n UK at He currently s an Assocate Professor of Medcal Engneerng at Medcal Physcs and Engneerng Department n Medcal School of Isfahan unversty of Medcal Scences. A. Amnzadeh receved hs B.S. Degree n Electrcal Engneerng from Shraz Unversty (Iran) n 2 and hs M.Sc. degree n Control Engneerng from Shraz Unversty n 23. He has worked n a number of projects related to Real-Tme Control, Multple-Modelng and Heatng-Plants. Hs current research nterests nclude Classfcaton of Bomedcal Sgnals, Neural Networks, Electronc Control Unt of Internal Combuston Engnes and Navgaton Systems. E. Zeraatkar receved hs B.S. Degree n Control Engneerng from Shraz Unversty (Iran) n 28 and hs M.Sc. degree n Control Engneerng from Shraz Unversty n 212. Hs nterest s n optmzng convergence n numercal methods such as Neural Networks. Recently he s focused on bomedcal sgnal processng and classfcaton methods for such sgnals. Hamd Sanee obtaned hs MD n 1987, then graduated as a specalst n nternal medcne and got hs fellow n cardovascular dsease respectvely n 199 and 1996 Isfahan Unversty of Medcal Scences, Iran. He s Assocate Professor of cardology and head of nternal medcne department n the School of Medcne of Isfahan Unversty of Medcal Scences, Iran. Hs research nterests are cardac sgnal, mage and nterventonal cardology. 16 Vol 1 Issue 2 May-Aug 211