A deterministic approach for finding the T onset parameter of Flatten T wave in ECG

Transcription

1 A determnstc approach for fndng the T onset parameter of Flatten T wave n ECG Uzar Iqbal a, Teh Yng Wah a, *, Muhammad Habb ur Rehman a, Quratulan Masto a a Department of Informaton Systems, Faculty of Computer Scence and Informaton Technology, Unversty of Malaya, 50603, Kuala Lumpur, Malaysa. a Emal: uzar.qbal@sswa.um.edu.my, uzarqbal13@gmal.com, tehyw@um.edu.my, habbcomsats@gmal.com, quratulan.masto@hotmal.com Abstract: Identfcaton of the exact nature of flatten T wave n ECG sgnal s Classfcaton of normal and abnormal T wave epsodes especally, regardng Flatten T wave n electrocardography (ECG) sgnal s stll a complex phenomenon for cardologsts. Identfcaton of Flatten T wave depends on four parameters; Tme duraton (T dur), T onset (T on), T offset (T off) and T peak (T pk) values whch play a vtal role to dentfy the exact nature of Flatten T wave. The proposed approach s used to extract the T on value of Flatten T wave wth the detecton of R peaks and RR ntervals. The proposed approach s appled to ten dfferent subjects of Flatten. It s dvded nto three dstnct phases. Frstly, Flatten sgnals are segmented by lead wse. Secondly, nose fltraton s done to dentfy the peak values and removal of low-frequency components. A thrd phase computes the R peak values and RR ntervals wth the help proposed algorthm. By usng the R peak value as a fducal pont and consderng the last nterval of RR nterval nstead of complete T wave alternans detecton algorthm (TWA) for determnaton the T on parameter of Flatten T wave. The expermental evaluaton manfests that effcency factors are hgh n rate durng the operatonal nvestgatons (closest to the range of 100%). These effcency factors dscussed n the context of accuracy, senstvty, predcton and error rate. These operaton effcency factors wll play a benchmark role n future for calculaton the others parameters of Flatten T wave. - Keywords: Myocardal nfarcton, Flatten T wave, R peak detecton, Electrocardography 1. ITRODUCTIO Advancement n technology has always been a game changer n dfferent real-tme medcal dagnostcs such as MRI, X-Ray flms, varous blood tests and ECG etc. In partcular, research related to heart dseases always gan a specal attenton for medcal professonals due to false alarm detecton of cardovascular dseases (CVDs) n ECG. The rato of human deaths n nowadays s contnuously ncreasng due to these CVDs and the man reason for these dseases are due to unhealthy lfestyle [1]. In partcular case of accurate detecton of myocardal nfarcton (MI) helps the physcan to delver the nvaluable and tmely treatment to patents. There are stll some worst stuatons lke MI abnormaltes whch exst n electrcal actvtes of heart whch can be montored n ECG. ECG works for montorng the electrcal actvtes of human hearts by usng the 5 to 12 electrodes whch are attached to dfferent areas of human body. ECG sgnal s broken nto segments such are P wave, QRS complex, ST segment and T wave [1]. Each segment represents dfferent knds of heart actvtes [2]. There are some abnormaltes whch exst n heart s electrcal actvtes lke ST-segment elevaton MI (STEMI), non-st segment elevaton/depresson MI (STEMI), T wave nverson and Flatten anomales of ST segment and T wave [3]. Flatten anomales of ST segment and T wave n ECG sgnal are stll an open myth for physcans and cardologsts [4].In dagnostc purposes of MI affected patent requres specal attenton. Such dagnostc purposes are only possble by performng better and accurate classfcaton between normal and abnormaltes T wave especally correct dentfcaton of Flatten T wave. Classfcaton of these Flatten anomales s performed by a parametrc soluton. Such parametrc soluton urges to delver the better classfcaton results regardng hgh accuracy and senstvty along wth mnmal error rate. Accordng to lterature, the parametrc soluton s the best method for classfcaton the ECG abnormaltes. Analyss of beat-to-beat, heart rate varablty, detecton of atral fbrllaton (AF), detecton of premature ventrcular contracton (PVC) and detecton of myocardal nfarcton (MI) are dependent on such parametrc solutons [5][6]. Parameters lke ampltude, start tme, end tme and tme duraton of dfferent ECG features always become handy for the dagnostc purpose of varous dseases. Ths research artcle delvers the determnstc approach for fndng the T on parameter of Flatten T wave whch s helpful n recovery of a patent at a partcular stage. Accordng to ths research, frstly segmented the ECG sgnals and then apply the fltraton process on the sgnals for removal of unnecessary components n the sgnal. In detecton phase of R peaks values and RR ntervals dentfcaton the accurate number of R peaks along wth RR ntervals by usng our proposed algorthm. ext step s an ntegraton phase, our fndngs regardng detected R peak values and RR ntervals are embedded n a key

2 part of the TWA algorthm whch s the state-of-the-art work. By executon of these two phases on ten dfferent subjects of Flatten T wave and dscern the T on parameter of Flatten T wave. By usng our best knowledge, prevously no one works on the parameters of Flatten T wave and nature dentfcaton of Flatten T wave wth the help of s stll a pendng job. So far we are the frst one for gvng the soluton of a T on parameter of Flatten T wave. Such soluton s a baselne for openng the new debate regardng fndng the exact nature of Flatten T wave. Wth the help of our soluton n future, we wll hghlght the other parameters lke T dur, T off, and T pk values. Combnaton of these T wave parameters wll be a complete package for exact nature determnaton of Flatten T wave. Operatonal nvestgatons of T wave concernng the parametrc soluton are also helpful for other analyss lke the beat to beat analyss, heart rate varablty (HRV) and ST segment analyss of MI. A summary of our contrbutons s followed n below for further observatons. Intalzng step for dentfyng the exact nature of Flatten T wave n the context of T on parameter. Dervaton of T on parameter n ths approach reflects the nvolvement of external dependences factor n ECG features. Such soluton works under usage the state-of-the-art TWA detecton algorthm. After the operatonal nvestgatons, delverables regardng effcency factors are approx. 100% 1.1 Abbrevatons and Acronyms Abbrevatons and acronyms are hghlghted n table 1 and table 2 Table 1. Abbrevaton Secton Abbrevatons ECG MI STEMI STEMI WTMM PVC AF HRV CVDS TRA Descrpton Electrocardography Myocardal nfarcton ST Segment elevaton myocardal nfarcton on ST Segment elevaton myocardal nfarcton Wavelet Transform Modulus Maxma Premature Ventrcular Contracton Atral fbrllaton Heart Rate Varablty Cardovascular dseases Trapezum s area Table 2. Acronyms wth Descrpton Acronyms Tdur Tamp Ton RAD Se E TP FP Fn Descrpton Tme duraton of T wave Ampltude value of T wave T-onset parameter of Flatten T wave Raton of accurate detecton Senstvty Error rato or Error rate True postve R peak detecton False postve R peak detecton False negatve R peak detecton Rest of ths research artcle wll dscuss the prevous analyss of ECG along wth the related R peak analyss n secton 2. Secton 3 dscusses the systematc methodology of our proposed approach. Secton 4 shows the algorthmc flow of our approach. Secton 5 wll be the result, and secton 6 deals wth dscusson part of our fndngs regardng accuracy, senstvty, predcton and error rato n our approach. Secton v hghlght the concluson and future drectons of the artcle.

3 2. LITERATURE SURVEY In ECG sgnal each segment and duraton of each segment have some nformaton, such nformaton can be easly understandable by cardologsts. In the normal routne, 12-leads are used for measurng the electrcal actvtes of the human heart. For reducng the computatonal complexty, fve lead ECG s used nstead of 12 lead ECG [7]. Parameters of 5-lead ECG are (RA) rght arm, (RL) rght leg, (LA) left arm (LL) left leg and septal lead V1 or lead V5 typcally chosen for analyzng of the fve lead sgnals. Each electrode on human body captures heart s electrcal actvty from dfferent angles. These angles are represented n the form of dfferent wavelet segments. These segments are represented as a P wave, ST segment, QRS complex and T wave on ECG Wavelet. The most sgnfcant segment n ECG s T wave, whch ndcates the repolarzaton of ventrcular [8]. Such repolarzaton of ventrcular ndcates the healthy or unhealthy status of the heart. In partcular MI case, Flatten ST segment and Flatten T wave are hot ssues for cardologst experts [9] [10]. Robotc detecton of the ST segment and T wave changes may cause the survval of human lfe. ormally we can skp the mnor detal n heart analyss lke the ntensty of MI, whch means changes regardng ampltude and tme duraton of ST segment and T wave. One approach s trapezum s area (TRA) approach whch works to locate the T peak through the maxma and mnma value by usng the wndow based method. In TRA method, three vertex s are fxed and one s moble whch s shfted over the ECG sgnal [11]. TRA approach s a handy tool for detecton of T wave n ECG sgnal wth the presence of dfferent nose factor. one of the drawbacks n ths method actually works only for the detecton of bphasc T wave whereas, dentfcaton of the tme duraton (T dur) and ampltude of Flatten T wave s stll an open job. Lterature reflects the parametrc mportance of ECG features regardng classfcaton and dentfcaton. Despte for understandng the ECG feature parametrc mportance whch reveals equally n each feature. Parametrc dscusson of T wave always surround over the four parameters such are T onset, T offset, Tdur and T amp whch s hghlghted n Table3 Table 3.Key Parameters of T wave n ECG Parameters T-onset T offset Tamp Tdur DIFFERET T WAVE PARAMETERS Descrpton Start tme of the T wave n ECG End tme of the T wave n ECG Peak value between T onset and T offset T wave duraton between T onset and T offset Table 4 hghlghts the baselne readngs of dfferent feature parts of ECG; such baselne readngs play a vtal role for any parametrc dervaton [12].The scope of ths artcle clears to hghlght the parametrc soluton of Flatten T wave regardng T on Parameter. Determnaton the exact nature of Flatten T wave s only possble by adoptng the parametrc soluton, but unfortunately, no standard baselne values for T wave parameters exst n lterature, so comparson of derved results wth any standard or baselne values s not possble [13][14]. ature markng of Flatten T wave s an unconcevable task n such crcumstances. Under such stuaton n ths research, we have adopted the novel base state-of-the-art soluton for the dervaton of T on by fndng detecton of R peaks and RR ntervals [15][16]. To dagnose dfferent cardac dsease we normally use the ECG sgnal analyss wth varous approaches. One of them s a beat-to-beat analyss whch deals wth QT nterval, the tme nterval between Q wave onset (start tme of Q wave) and T wave offset (end tme of T wave) [17][18]. Another approach s mathematcal modelng whch s appled on ECG sgnal for calculatons the tme duraton and ampltude of dfferent segments lke ST segment, QT nterval, RR nterval and T wave. Smlarly, wavelet analyss whch ntegrates Wavelet Transform Modulus Maxma (WTMM). WTMM represents the characterstc behavors of heart sgnals n ECG and s a part of T wave detecton algorthm whch works wth the combnaton of wavelet transforms regardng ampltude and slope of T wave [19][20][21]. Table 4. Standard Readngs of ECG Features [12]

4 . Peak Values Ampltude Intervals Duraton P-Wave 0.75mV P_R Interval 0.12 to 0.20 Sec R-Wave 1.60mV QT Interval 0.35 to 0.49 Sec Q-Wave 25% of R-Wave ST Segment 0.05 to 0.15 Sec T-Wave 0.1 to 0.5mV P wave Interval QRS Complex PR Segment T wave 3. SYSTEMATIC METHODOLOGY 0.11 Sec 0.09 Sec 0.06 to 0.15 Sec Vares In the context of drllng towards the soluton of the problem, fndng the T on parameter of Flatten T wave by consderng the ten dfferent subjects of Flatten T wave. Such fndng s a frst step towards hghlghtng the nature of Flatten T wave. Below showcase n fgure 1 hghlghts the system model n the form of a block dagram. Such system model s a representaton of proposed approach towards markng the T on parameter of Flatten T wave under usage of R peak analyss and then state-of-the-art TWA detecton algorthm by consderng the R peak as a fducal pont. Below equaton 1 s a core part of our approach that s adopted from TWA detecton algorthm [15][16]. T on RR. (1) Accordng to the flow of model, the segmentaton process s appled to ten dfferent subjects of ECG data (Flatten T wave) whch splts data nto lead wse, lead II and lead III. The onwards mplcaton the nose fltraton by usng the hgh-pass flter for removal of low-frequency components and domnate the peak values. These hghlghted peak values are further nvestgated by a proposed algorthm whch deals wth the detected R peak values along wth the RR ntervals. The second part of the algorthm sets the threshold values for measurement the operatonal effcency. These threshold values work for accuracy calculaton regardng the rato of accurate detecton (RAD) of R peaks, Senstvty (S e), Postve predcton ndex (P +) of T on by detected R peaks and error rate calculaton (E). After the executon of the algorthm, at the fnal step of the proposed approach s takng the R peak as the fducal pont and nsert the fnal derved value of RR nterval (RR ) n (1) In the context of operatonal effcency of ths approach, threshold values are the key bulder ponts. Three threshold levels of R peaks are detected and assgned ranges. True postve detected R peak (TP) set a range values th1= 0.3mV to 0.4mV, false postve detected R peak (FP) range s th2= 5.1mV to 5.2 mv, and fnally false negatve detected R peak (F) under the range of th3=0.04mv to 0.1mV. Usage of below equatons regardng measurement the operatonal effcency of our approach under the umbrella of these threshold values S e = TP TP + F * 100 (2) Sum = TP + FP + F RAD = TP * 100 (3) Sum P + = TP TP + FP * 100 (4) E = FP + F TP + F * 100 (5) These above from (2) to (5) works for the dervaton of senstvty, the rato of accurate detecton, postve predcton ndex and error rate or rato.

5 Dervaton of these operatonal factors for tracng the T on parameter of Flatten T wave that totally depends on the detected R peak values. Such dervaton clearly, mposes the nvolvement of dependences factor of ECG features. Reflecton of ths above dscusson regardng proceedng ths effcent approach. Such effcent approach uses ten dfferent subjects of UMMC (Unversty of Malaya Medcal Center) dataset whch have to Flatten T. Hghlght the average value of T on of Flatten T wave wth the help of analyss the T on values of ten dfferent subjects by usng such approach. The frst stage detects the number of R peaks n the fltered segmented sgnal of ECG wth the help of wndow fltraton. Whereas, the second stage hghlghts the maxmum RR ntervals and the next stage s usng the methodology of TWA detecton algorthm by consderng the last RR nterval. Such RR nterval embeds on equaton 1 for the dervaton of T on value. The mplcaton of these two stages on both leads work for comparatve analyss, wth the help of such analyss further qualtatve mprovement wll also be possble. Before proceedng to smulaton work, adjust the setup of dfferent parameters. Table 5 hghlghts the complete package of smulaton settng before proceedng to operatonal actvtes. FIG. 1. Representaton of system model for hghlghtng the (Ton) of Flatten T wave Table 5. Smulaton settng before proceedng the Operatonal Work Sr # Flatten T Record M_tag Lead wse Segmentaton Sgnal Sample Wndow sze(w) R peak Threshold values(mv) 1 me me me09426 th1 th2 th3

6 4 me me me me me me me ALGORITHMIC STRUCTURE Coverage of algorthmc structure of proposed approach s a jont venture of few structural components. Under the usage of these components, ths approach works and delvers the T on parameter of Flatten T wave. Core or heart part of the approach s takng R peak as a fducal pont that already dscussed n above methodology secton. Before proceedng towards smulaton work, frstly dscuss the key bulders of the proposed algorthm. These key bulder ponts are already markup n fgure1 that represents the system model of proposed approach. 4.1 Segmentaton Consder the ten dfferent subjects of Flatten T wave whch are recorded by taskforce montor. In taskforce, montor machne leads II and lead III s perfect for analyss of the ECG due to maxmum features extracted. The frst step of our algorthmc modelng s to pck the raw ECG sgnals one by one n a subject manner and then segmented the sgnals by lead II and lead III by usng manual spltter technque. 4.2 R-Peak Analyss Such component works after the nose fltraton of low-frequency components of ECG sgnal, R-Peaks value s dentfed by locatng the R peak (R_Loc), compute and store the located R peaks (pk_r). To determne the maxmum ampltude of R peak these detected numbers of R peaks (pk_r) are used the sort functon of Matlab. 4.3 ose Fltraton At ths phase, take the segmented sgnals by a lead wse and use hgh pass flter for removal of lowfrequency components n the ECG sgnals. Wth the help of hgh-pass flter dentfy the R peak very easly by usng the wndow fltraton technque n R peak analyss phase. Equaton,(6) represents the fltraton method for removal of dfferent low-frequency components and 1000 s a samplng rate of the ECG sgnal. H= (1000, 1/1000*2. Hgh ) (6) Above structure of the algorthm ndcates that soluton of T on parameter of Flatten T wave reles on the accurate detecton of R peaks and RR ntervals. Such algorthm breaks nto two core parts that are detecton process of R peaks along wth last RR nterval, then mark the postve and negatve R peaks for measurng the accuracy, senstvty, postve predcton ndex and error rate. Accordng to system

7 modelng, the structure of proposed algorthm s map up wth the model. The frst phase of the algorthm reflects segmentaton process n whch raw data of ECG breaks nto leads wse (lead II and lead III). ext step mplcaton of hgh-pass fltraton for removal of low-frequency components and hghlght the values of R peak. After onwards normalze the lead wse ECG sgnal and then locate the R peak values (R_Loc). These R peaks locaton s computed and store (pk_r) for further operatonal nvestgatons. To determne the operatonal effcency of such approach by usng the wndow fltraton (sze of wndow W=300). Along wth wndow fltraton, sets the three threshold values whch already dscussed n system modelng secton 5. RESULT Identfyng the T on parameter of Flatten T wave s the frst step towards hghlghtng the exact nature of Flatten T wave. In ths determnstc approach raw sgnals are segmented nto lead wse(lead II and lead III for maxmum feature extracton) and then apply hgh pass flter for removal of low-frequency components n the sgnal by settng the Frequency generaton fs=1000.after the removal of lowfrequency components then set the three threshold values(th1=0.3mv to 0.4mV,th2=5.1mV to 5.2mV and th3=0.04mv to 0.1mV)for determnng the true postve number of R peaks (TP),false postve number of R peaks(fp) and false negatve number of R peaks (F). Implementaton of ths effcent approach on ten dfferent M_Tag_patent records whch have Flatten T wave and summarzed the results of T on values, number of detected R peaks and last part of the RR nterval (RR ) n Table 6. Showcase of three threshold values, senstvty, rato of accurate detecton, postve predcton ndex and error rate hghlght n Table 7. ext step s to hghlght the mean values of T on values, senstvty, and the rato of accurate detecton, postve predcton ndex and error rate of lead II and lead III by fndngs n Table 7. Wth the help of these tables, comparatve analyss between both leads s a qute smple job and productve one n terms of future mprovement. Table 6. Fndngs OF dfferent paramters of ten subjects (mtag_patents) OF UMMC dataset Sr # M_Tag_ patents Segmentaton T-onset (mll sec) T-onset (sec) Detected R peaks Detected RR nterval RR 1 me10233 LII LIII me10235 LII LIII me10236 LII LIII me09426 LII LIII me1002 LII LIII me09934 LII LIII me09847 LII LIII me09824 LII LIII

8 9 me09154 LII LIII me09470 LII LIII Table 7. Representaton of factors Senstvty (Se), Rato of accurate detecton (RAD), Postve predcton ndex I (P+), Error Rate(E) Sr # M_Tag_ patents Segmentaton TP FP F Se (%) RAD (%) (P+)(%) E (%) 1 me10233 LII LIII me10235 LII LIII me10236 LII LIII me09426 LII LIII me1002 LII LIII me09934 LII LIII me09847 LII LIII me09824 LII LIII me09154 LII LIII me09470 LII LIII LEAD II Values Let s consder the lead II for dervng mean value T on of Flatten T wave n terms of mllseconds and seconds. In below equatons 7 to 11, represents the ten dfferent subjects of Flatten T wave (=10). Lead II T (T on)= 1/ T on Usage the results of table 7, equaton 7 s mplemented on T on values of lead II. Get the result n the context of mllseconds and seconds, lead II T (T on) =43.73 ms and lead II T (T on) = sec. The (7)

9 Same methodology s executed on Senstvty (S e), Rato of Accurate Detecton (RAD), Postve predcton ndex (P +) and Error rate (E) by usng the results of table 7. Lead II (S e) = 1/ S e (8) represents the summaton of ten senstvty values Lead II (RAD) = 1/ In above (9) represents the mean rato of accurate detecton (RAD) of ten Flatten subjects Lead II (P +) = 1/ p Dervaton the mean value of the postve predcton ndex n (10) Lead II (E) = 1/ Showcase of error rato n (11) represents n the form of Extracted mean values from (8) to (11) are Senstvty (S e) =99.29%, Rato Accurate Detecton (RAD) =99.29%, Postve Predcton Index (P +) = 100% and Error rate (E) =0.74%. (8) (9) (10) (11) 5.2 LEAD III Values The Same scenaro s appled on lead III values as appled to fndng the lead II values. At frst, fnds the mean value of Flatten T wave T on.. Lke lead II, represents ten subjects of Flatten T wave from (12) to (16) Lead III T (T on) = 1/ T on By usng (12) we get the value Lead III T (T on) =40.54 ms, Lead III T (T on) = sec. Extracton the effcency factors of lead III are gettng on sane way lke the lead II, get the mean value of Senstvty (S e), Raton accurate detecton (RAD), Postve predcton ndex (P +) and Error rate (E) by usng below equatons Lead III (S e) = 1/ S e Above (13) hghlghts lead III to mean value of ten dfferent subjects of Flatten T under the factor of senstvty Lead III (P +) = 1/ p Indcaton of (15) s the lead III mean value of postve predcton ndex of ten dfferent Flatten T subjects Lead III (E) = 1/ (16) Error rato n (16) represents n the form of Mean values of Senstvty (S e), Rato Accurate Detecton (RAD), Postve Predcton ndex (P +) and Error rate (E) are of Lead III are S e=99.56%,rad=99.56%, (P +)= 100% and E= (12) (13) (15) 6. DISCUSSIO 6.1 Comparatve Analyss of Lead II and Lead III Dervaton of Mean values of parameter T on and operaton effcency factors are hghlghted n table 8 for comparatve analyss. Fgure2 s a graphcal representaton of comparatve summary that plays a vtal role for further nvestgaton regardng qualtatve mprovement n ths approach. Fgure 2 reflects that lead II and lead III both are too close for calculaton of T on of Flatten T wave. In the context of the

10 dfference between mean values of lead II and lead III s a further step towards the nvestgaton.table 9 and graphcal vew n fgure 3(a) and fgure3 (b) represent the dfference between two fndngs. In case of further observaton and nvestgaton fgure, 3(a) accounts for the fndng the mean values of T on parameter and effcency factors of lead II, lead III and dfference between two leads.fgure3(b) pe chart representaton of percentage composton of T on parameter and effcency factors that specfes that error raton between two leads s bt low. Table 8. Compartve Summary of both leads Result. T-onset Parameter and Effcency factors Lead II Lead wse Comparson Lead III Ton ms, s 40.54ms, s Se (%) RAD (%) P+(%) Error rate (%) Senstvty (%) Compartve result of Effcency factors Rato of Accurate Detecton (%) Postve Predcton ndex (%) Error rate (%) Lead II Lead III FIG.2. Comparson vew of Lead II and Lead III Table 9. Dfference between lead II and lead III Leads Combnaton T-onset(ms) T-onset(s) Se (%) RAD (%) (P+)(%) E(%) Lead II Lead III Dfference

11 Status Dfference Between lead II and lead III Lead II Lead III Dfference FIG. 3(a). Graphcal vew of dfference status between lead II and lead III Se +P 21% 0% 29% 50% Lead II Lead III Dfference 21% 0% 29% 50% Lead II Lead III Dfference RAD E 21% 0% 29% 50% Lead II Lead III Dfference 21% 0% 29% 50% Lead II Lead III Dfference FIG. 3(b). Percentage composton of lead II and lead III n terms Ton and effcency factor 7. COCLUSIO AD FUTURE DIRECTIO Ths research artcle delvers the determnstc approach for calculatng the T on parameter of Flatten T wave. Ths effcent approach s an ntal step towards nature determnaton of Flatten T wave. Durng

12 the operatonal actvtes of ths determnstc approach, usng a core part of the TWA algorthm by takng the R peak as a fducal pont. Such operatonal process reflects the nvolvement of dependences factor durng the nvestgaton of T on parameter of Flatten T wave. A hgh rate result of Ton parameter after the operaton actvtes ndcates the bgger achevement because the parametrc soluton of Flatten T wave s stll a pendng work for researchers. By usng our best knowledge, the lterature ndcates prevously no one work on the parametrc soluton of Flatten T wave. A parametrc soluton of Flatten T wave opens the door of nature determnaton whch plays a vtal role n a dagnostc purpose of MI affected patents. Evaluaton of ths determnstc approach manfests the hgh-effcency factors and T on parameter after expermental nvestgatons. Showcase of these factors n terms of dervaton of the mean (T on) Senstvty (S e ), Rato Accurate Detecton (RAD), Postve predcton ndex (P +) and Error rate (E). These factors are hghlghted n form of lead II T on=43.73 ms, s, S e=99.29%, RAD= 99.29%, P +=100%, E=0.74% and lead III T on=40.54 ms, s, Se=99.56%, RAD= 99.56%, P +=100%, E=0.428 %. These fndngs clearly reflect that error rate between two leads s negotable but further operatonal qualtatve mprovements are hghly desrable for further nvestgatons n the context of dervaton the other parameters of Flatten T wave. In future, one may nvestgate the use of our proposed approach to calculate other key parameters such as T dur, T peak, T off. Combnng our method wth wavelet analyss to further mprove the performance can be another ntrgung area for researchers. Secondly, such algorthmc soluton wll also be helpful for other areas lke the beat to beat analyss (QT nterval detecton), premature ventrcular contracton (PVC) and atral fbrllaton (AF). Acknowledgments: All the valuable work n hstory belongs to a support of a partcular person n your surroundngs.in ths research project of feature analyss of ECG, we are very thankful to Dr. TA MAW PI for contnued support and sharng ECG dataset for the research project of anomales detecton n ECG sgnals. Specal thanks to Mr. Sheraz Alam for help and consderaton. REFERECES 1. M. Bahoura, M. Hassan, M. Hubn. DSP mplementaton of wavelet transform for real-tme ECG waveforms detecton and heart rate analyss Computer Methods and Programs n Bomedcne 52,1997,pages do: /S (97)01780-X. 2. Har,M.R., Anurag,T, Shalja,S., ECG sgnal processng for abnormaltes detecton usng mult-resoluton wavelet transform and Artfcal eural etwork classfer Measurement, Vol 46,2013, Issue 9, Pages Do: /j.measurement S. Suave, L, ECG Patch Montors for Assessment of Cardac Rhythm Abnormaltes Progress Cardovascular Dseases, Vol 56, 2013, Issue 2, Pages Do: /j.pcad Davd E. P, Andrew M, Irfan M, Andrew M.W, Irfan M, Anthony, Spencer D, Mchael D. J, Electrocardography-nclusve screenng strateges for detecton of cardovascular abnormaltes n hgh school athletes Heart Rhythm, Vol 11, 2013, Issue 3 Pages Do: /j.hrthm Kmberly, G. H.,Monca, Z.,Jonathan, A. D., The effectveness of screenng hstory, physcal exam, and ECG to detect potentally lethal cardac dsorders n athletes: A systematc revew/meta-analyss Journal of Electrocardology, Vol 48, 2015, Issue 3, Pages Do: /j.jelectrocard Hesham, R. O, Devanand, M., Enrco, M. C., A woman wth recurrent chest pan and STsegment elevaton European Journal of Internal Medcne, Vol 26, 2016 pages e3-e4. Do: /j.ejm

13 7. Masash K., Yota K, Dak I., Harukazu I., Yuj I. A case of ST segment-elevated myocardal nfarcton wth less common forms of sngle coronary artery Cardovascular Interventon and Therapeutcs, Vol 31, 2016, Issue 4, pp Do: /s x. 8. Tngtng,Z ; Alstar,E. W. J; Joachm,B; Gar,D. C. Crowd-Sourced Annotaton of ECG Sgnals Usng Contextual Informaton Annals of Bomedcal Engneerng, Vol 42, 2014, Issue 4, pp Do: /s André E. Aubert, Drk Ramaekers, Frank Beckers, Rk Breem, Carl Denef, Frans Van de Werf, Hugo Ector. The analyss of heart rate varablty n unrestraned rats. Valdaton of method and results Computer Methods and Programs n Bomedcne, Vol 60, Issue 3, ovember 1999, Pages do: /S (99) Dorothée, C. V.T.; Ilse, F.; Petra, B.; cole, d. L.;Jos, M.Th. D. Cardac anomales n ndvduals wth the 18q deleton syndrome;report of a chld wth Ebsten anomaly and revew of the lterature European journal of medcal genetcs Vol 56, 2013, Issue 8, Pages Do: /j.ejmg Carlos R Vazquez-Sesdedos, Joao Evangelsta eto, Enrque J Maranon Reyes, Aldebaro Klautau and Roberto C Lmao de Olvera ew approach for T-wave end detecton on electrocardogram: Performance n nosy condtons BoMedcal Engneerng OnLne 2011 do: / X C. Sartha, V. Sukanya, Y. arasmha Murthy ECG Sgnal Analyss Usng Wavelet Transforms Bulgaran Journal of Physcs Saurabh, P.; Madhuchhanda, M. Emprcal mode decomposton based ECG enhancement and QRS detecton Computer Bology and. Medcne, Vol 42, 2012, Issue 1, Pages Do: /j.compbomed Deboleena, S; Madhuchhanda, M. R-peak detecton algorthm for ECG usng double dfference and RR nterval processng Proceda Technology, Vol 4, 2012 Pages do: /j.protcy XangKu Wan, Kanghu Yan, Dehan Luo and Yanjun Zeng A combned algorthm for T- wave alternans qualtatve detecton and quanttatve Measurement Journal of Cardothoracc Surgery 2013 do: / ak Romero, el R. Grubb, Gareth R. Clegg, Coln E. Robertson, Paul S. Addson, and James. Watson T-Wave Alternans Found n Preventrcular Tachyarrhythmas n CCU Patents Usng a Wavelet Transform-Based Methodology IEEE TRASACTIOS O BIOMEDICAL EGIEERIG, VOL. 55, O. 11, OVEMBER Tanveer A. B; Claus,G; Jørgen K. K;Jmm, ; Jacob,M; Jørgen, M; Egon, T, J; J. Strujk. The T-peak T-end Interval as a marker of repolarzaton abnormalty: a comparson wth the QT nterval for fve dfferent drugs Clncal Drug Investgaton,Vol35, ov 2015, Issue 11 pp do: /s Sona Rezk, C_edrc Jon, Sadok El Asm. Inter-beat (R-R) ntervals analyss usng a new tme delay estmaton technque 20th European Sgnal Processng Conference, EUSIPCO 2012 Bucharest, Romana. 19. Danel.L; Chantal.P; Jean-Claude. R; Bruce D; A. Robert,L. Wavelet Tme Entropy, T Wave Morphology and Myocardal Ischema IEEE TRASACTIOS O BIOMEDICAL EGIEERIG, Vol. 47, 2000, O Bensujn; Kez,.s.v.;Cyntha, H. Detecton of ST Segment Elevaton Myocardal Infarcton (STEMI) Usng Bacteral Foragng Optmzaton Technque IJET, 2014 Vol 6.

14 21. Samar Krm, Kaïs Oun, and oureddne Ellouze T-Wave Detecton Based on an Adjusted Wavelet Transform Modulus Maxma Internatonal Journal of Medcal, Health, Bomedcal, Boengneerng and Pharmaceutcal Engneerng Vol:1, 2007, o:3. Uzar Iqbal s a PhD student of FSKTM, Unversty of Malaya, Kuala Lumpur, Malaysa. He s workng on the research project of feature analyss of ECG whch s related to behavoral analyss of dfferent cardovascular dseases and anomales n ECG. Dr. Habb s now workng as Assstant Professor at COMSATS Insttute of Informaton Technology, Wah Cantt, Pakstan. He receved hs PhD n moble dstrbuted analytc systems from FSKTM, Unversty of Malaya, Kuala Lumpur, Malaysa. Qurat-ul-an Masto s a PhD student of FSKTM, Unversty of Malaya, Kuala Lumpur, Malaysa. Her workng research project s classfcaton of premature ventrcular contracton cardac arrhythma for dfferent cardo vascular dseases.

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