researcharticle arrhythmia classification of ecg signals...

Research ArticleArrhythmia Classification of ECG Signals Using Hybrid Features

Syed Muhammad Anwar 1 Maheen Gul 2 Muhammad Majid 2

and Majdi Alnowami 3

1Department of Software Engineering University of Engineering and Technology Taxila Pakistan2Department of Computer Engineering University of Engineering and Technology Taxila Pakistan3Department of Nuclear Engineering King Abdulaziz University Jeddah Saudi Arabia

Correspondence should be addressed to Syed Muhammad Anwar sanwaruettaxilaedupk

Received 17 August 2018 Accepted 15 October 2018 Published 12 November 2018

Guest Editor Dominique J Monlezun

Copyright copy 2018 Syed Muhammad Anwar et al is is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in anymedium provided the original work isproperly cited

Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiacconditions In this paper a novel method for classification of various types of arrhythmia using morphological and dynamicfeatures is presented Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features Itprovides better time and frequency resolution of the electrocardiogram (ECG) signal which helps in decoding importantinformation of a quasiperiodic ECG using variable window sizes RR interval information is used as a dynamic feature enonlinear dynamics of RR interval are captured using Teager energy operator which improves the arrhythmia classificationMoreover to remove redundancy DWT subbands are subjected to dimensionality reduction using independent componentanalysis and a total of twelve coefficients are selected as morphological features ese hybrid features are combined and fed toa neural network to classify arrhythmia e proposed algorithm has been tested over MIT-BIH arrhythmia database using13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats e proposed methodology resulted in animproved average accuracy of 9975 and 9984 for class- and subject-oriented scheme respectively using three-foldcross validation

1 Introduction

Cardiac arrhythmias are a type of irregular heartbeats inwhich the heart rhythm is either too fast (tachycardia) or tooslow (bradycardia) A small change in electrocardiogram(ECG) morphology or dynamics may lead to severe ar-rhythmia attacks which can reduce the ability of the heart topump blood and causes shorting of breath pain in chesttiredness and loss of consciousness ere are several typesof arrhythmia and some of these are dangerous which maylead to cardiac arrest and sudden death if not detected andmonitored in time [1] ere are other types of arrhythmiaswhich are not essentially life-threatening but still requireproper analysis to avoid future clinical problems A fewcategories of arrhythmia appear infrequently in the ECGsignal and hence require long electrocardiogram recordingsin order to be detected A manual analysis of longer ECGrecordings requires time and great effort e automatic

detection and classification of these arrhythmias offer greatassistance to physicians [2 3] Moreover early diagnosis ofarrhythmias would help in proper treatment and supportsustained life erefore several techniques have beenproposed for automatic detection and classification ofvarious types of arrhythmia [4ndash10]

A method for classification of sixteen types of ar-rhythmia based on ECG morphology and dynamics wasproposed in [11] Morphological features were extractedusing discrete wavelet transform (DWT) and independentcomponent analysis (ICA) while ECG dynamic featureswere extracted by calculating RR interval ese featureswere then classified using support vector machine with anaverage accuracy of 996 Five types of heartbeats namelynormal premature ventricular contractions (PVC) atrialpremature contractions (APC) left bundle branch block(LBBB) and right bundle branch block (RBBB) were rec-ognized in [4] Stationary wavelet transform (SWT) was

HindawiComputational and Mathematical Methods in MedicineVolume 2018 Article ID 1380348 8 pageshttpsdoiorg10115520181380348

applied on each ECG signal to make it noise free e high-order statistics of ECG signals and three timing intervalswere considered as features which were then fed to a hybridbees algorithm for training the radial basis function andresulted in an average accuracy of 9518

Some techniques rely on fiducial features which aretemporal and dynamic features that directly depend uponthe ECG characteristics eg wave onset point peaks(maximaminima) and offset [5] Nonfiducial features thatare directly derived from the fiducial features or obtained bysegmenting the ECG signal into several parts have also beenused [6] ese features are not found good enough whenused independently for accurate classification of ECG ar-rhythmia A combination of both fiducial and nonfiducialfeatures was used for defining a biological marker for personidentification [7 12]

e extracted features have usually been analyzed eitherin time domain or frequency domain Some of the mostimportant time domain features including RR interval STsegment and T height require identification of key timepoints within the signal [13] An approach for heartbeatclassification with dynamic rejection thresholds was pro-posed using QRS morphology frequency information ACpower of QRS detail coefficients and RR intervals as featuresto represent ECG beats [8] A support vector machine(SVM) was used to classify with an improved accuracy of972 and minimum rejection cost A combination of timeand frequency information has also been used in[9 11 14ndash16] giving better extraction of information froma quasiperiodic ECG signal using wavelet transform whichprovides a good time and frequency resolution Five im-portant types of arrhythmia namely nonectopic ventricularectopic supraventricular ectopic unclassifiable beats andfusion betas were analyzed and detected in [9] Featureswere extracted using DWT and only significant coefficientswere selected by applying independent component analysiswhich in combination with neural network yielded an ac-curacy of 9928

All methods discussed above have the followingshortcomings

(1) Performances of most of the methods have beentested only on smaller data sets and there is a need toverify their performance on larger databases

(2) Selected classes of arrhythmia have been evaluatedand there is a need to test all arrhythmia classes

(3) e classification accuracy on sparsely occurringarrhythmia classes is not good

In this paper a novel technique for ECG beat classifi-cation of arrhythmia is proposed that considers a hybrid ofenhanced morphological and dynamic features to overcomethese shortcomings Morphological features are obtainedusing DWTon each heartbeat e resulting features consistof DWT approximation coefficient and detail coefficients atlevel 4 Independent component analysis is applied on bothapproximation and detail coefficients independently to ex-tract only important coefficients In addition four types ofRR interval features are calculated to represent the dynamic

features of ECG heartbeats Moreover to enhance the dy-namic features of ECG heartbeats corrupted with Gaussiannoise Teager energy operator (TEO) [17] is used All thesefeatures are combined and fed to a neural network (NN) forautomatic classification of heartbeats into different ar-rhythmia types according to both class-oriented scheme (18classes) and subject-oriented scheme (5 classes)

e rest of the paper is organized as follows Firstly theproposed methodology is explained in detail in Section 2Class- and subject-oriented evaluation of the proposedsystem is presented in Section 3 followed by conclusion inSection 4

2 Methodology

e proposed methodology is presented in Figure 1 whichconsists of four major phases namely preprocessingheartbeat segmentation feature extraction and featureclassificatione details of these phases are presented in thefollowing subsections

21 ECG Signal Preprocessing Mostly ECG signals are af-fected by baseline wander or power line interface (PLI)Different methods were introduced to remove these types ofnoises from the ECG signal [18] Experiments showed thatbaseline wander significantly affects the detection of ar-rhythmia and makes the ECG signal analysis difficult for anexpert [19] PLI is a type of noise that occurs due to brokenelectrodes offset voltages in the electrodes movement ofpatient respiration errors or electrode resistance whilerecording the ECG signal It is a low frequency noise andtypically exists in the frequency range of 0ndash03Hz Forbaseline wander removal the expected value of the signalwas subtracted from the raw ECG signal to obtain the noise-free ECG signal using

x[n] xr[n]minus μ (1)

where x[n] is the denoised signal xr[n] represents the rawECG signal and μ is the expected value of the raw EEG signal

22 Heartbeat Segmentation ree basic constituents ofa heart cycle are QRS complex T wave and P wave termedas fiducial pointse correct splitting of the ECG signal intoheartbeat segments involves recognition of borders and peaklocations of these fiducial points e information about theR-peak locations given in the dataset was used to obtainthese heartbeat segments A single heartbeat consisted of 200samples including the R-peak and the samples around thepeak is segment size contained maximum information ofa single heartbeat and is shown in Figure 2

23 Feature Extraction In feature extraction improvedfeatures based on DWT RR interval and Teager energyoperator were selected which were able to represent themorphological and dynamic changes in the ECG signal withmore significance

2 Computational and Mathematical Methods in Medicine

231 Discrete Wavelet Transform (DWT) Statistical fea-tures of biomedical signals usually change over position ortime Wavelet transform oers signal representation in bothtime and frequency domains which makes it capable foranalyzing quasiperiodic signals like ECGWavelet transformwas employed in processing of the ECG signals for featureextraction [1] denoising [11] and heartbeat recognition[20] In the proposed method DWT was used as a featureextraction technique After applying DWT the ECG signaldecomposed into low-frequency approximation compo-nents and high-frequency detail components

e most commonly used wavelets which provide or-thogonality properties are Daubechies Coiets Symlets andDiscrete Meyer [21] Each heartbeat was disintegrated usingthe nite impulse response (FIR) approximation of theDiscrete Mayers wavelet transform e frequency range offourth-level approximation subband was 01125Hz and thefrequency range for fourth level detail subband was1125225Hz A total of 200 coecients were extracted aswavelet features which were processed using ICA for di-mensionality reduction Six major ICA components wereselected from each of the two DWT subbands resulting ina total of twelve morphological features from the twosubbands

232 RR Interval Features R is a point corresponding to thehighest peak of the ECG waveform and RR interval is thetime between the successive QRS complexese ECG signalhas a nonlinear dynamic behavior and during arrhythmianonlinear dynamic components change more signicantlythan the linear counterparts RR interval is simple easy tocalculate and less prone to noise Four types of RR intervalfeatures namely previous-RR post-RR average-RR andlocal-RR interval were derived from the RR sequence tocharacterize the dynamic features of the heartbeat ecalculation of these features uses the following equations

RRpre(i) R(i) minusR(iminus 1)

RRpost(i) R(i + 1)minusR(i)

RRlocal 110sum5

iminus5RR(i)

RRave 1NRR

sumNRR

i1RR(i)

(2)

where i shows the location of the current R-peak and RRpreRRpost RRlocal and RRave represent the previous post localand average RR interval respectively R(i) is the currentR-peak R(iminus 1) and R(i + 1) represent the previous andpost R-peaks respectively andNRR shows the total numberof RR intervals in an ECG segment

233 Teager Energy Operator (TEO) An independentanalysis of RR interval does not capture the nonlinear natureof RR interval inconsistency TEO was utilized to representthe nonlinear behavior of the RR interval which is a non-linear operator for energy tracking [17] It measures theinstantaneous frequency amplitude envelope and the en-ergy of the system that generated the signal e energyrequired by a source to generate signals with dierent fre-quencies and same energy and amplitude would be dierentMore energy is needed to generate a high-frequency signal ascompared with a low-frequency signal TEO reects thesource energy hence any instability in the conduction pathand impulse generation gets revealed in the Teager energy

Preprocessingx(n)

Heartbeatsegmentation

Feature extraction

Discrete wavelettransform

ICA

RR interval

Teager energyoperator

Heartbeatclassification

ECGsignal

Figure 1 Block diagram of the proposed arrhythmia classication scheme using hybrid features

0ndash06

ndash04

ndash02

0

02

04

06

08

1

100 200 300 400 500 600 700

TP

SQ

R R

Pre-Rsegment

Pro-Rsegment

Figure 2 Heartbeat segmentation of ECG signal from MITDBdatabase

Computational and Mathematical Methods in Medicine 3

function For a discrete time signal x[n] the TeagerndashKaisernonlinear energy (NE) and the average nonlinear energy(ANE) in the time domain are given as

NE x[n] x2[n]minusx[nminus 1]x[n + 1] (3)

ANE 1N

1113944NE x[n] (4)

where N represents the total number of samples in ECGheartbeat

24 Neural Network (NN) Classifier An artificial neuralnetwork consists of interconnected neurons which send andreceive messages between each other ese in-terconnections are assigned weights which representa network state and are updated during the learning processA feedforward neural network with 10 hidden layers wasused for the classification of arrhythmia in this study enetwork was implemented on MATLAB R2013a enumber of neurons in each hidden layer was limited to 50which allowed training this network on a core-i5 CPU-basedsystem with a RAM of 8GB An activation function based onrectified linear unit (ReLU) was used for the hidden layersand a sigmoid function was used at the output layer Backpropagation with stochastic gradient decay was used forupdating the network weights e learning rate was opti-mized to a value of 063 using grid search for accuracy andto avoid over fitting

3 Experimental Results

e details of the dataset used and the experimental resultsare presented in the following subsections

31 Dataset e MIT-BIH arrhythmia database (MITDB)[22] and MIT-BIH supraventricular arrhythmia database(SVDB) [23] from physionet were used for evaluation of theperformance of the proposed algorithm e MITDB in-cludes 48 ECG recordings of 47 subjects whereas the SVDBcontains 78 half-hour ECG recordings e data in SVDBwere recorded to increase the supraventricular arrhythmiasexamples in MITDB e databases contain an annotationfile with locations of the ldquoQRSrdquo complex and the type of theheartbeat for each record ese class annotations forheartbeats were exploited as reference annotations forevaluation purpose of the proposed model

32 Evaluation Strategy Two different types of evaluationstrategies were considered namely class-oriented [24] andsubject-oriented [9] In class-oriented strategy all signalsfrom both databases were segmented down using alreadyannotated QRS locations e resulting segments were di-vided into 18 different types of beats namely normal beat(NOR ldquoNrdquo) atrial premature contraction (APC ldquoArdquo) fusionof ventricular and normal beat (FVN ldquoFrdquo) left bundle branchblock (LBBB ldquoLrdquo) unclassifiable beat (UN ldquoQrdquo) prematureventricular contraction (PVC ldquoVrdquo) right bundle branch

block beat (RBBB ldquoRrdquo) ventricular flutter wave (VF ldquordquo)atrial escape beat (AE ldquoerdquo) fusion of paced and normal beat(FPN ldquofrdquo) nodal (junctional) premature beat (NP ldquoJrdquo)isolated QRS-like artifact (mdash) aberrated atrial prematurebeat (AP ldquoardquo) ventricular escape beat (VE ldquoErdquo) nodal(junctional) escape beat (NE ldquojrdquo) nonconducted P-wave(blocked APB ldquoxrdquo) paced beat (PACE ldquordquo) and supraven-tricular premature beat (SP ldquoSrdquo)

In addition subject-oriented strategy was also evaluatedAll 126 records from both the datasets were divided intoa similar training and testing ratio as for the class-orientedscheme but performance was reported according toANSIAAMI EC571998 standard [25] e original 18classes of heartbeats were grouped into five bigger classesnamely nonectopic beats (N) ventricular ectopic beat (V)supraventricular ectopic beat (S) unknown beat (Q) andfusion beat (F) e mapping from the MITDB and SVDBclasses to the ANSIAAMI heartbeat classes is presented inTable 1

e ECG signal from MITDB and SVDB datasets arefirst denoised to remove baseline wander e denoisedsignal was segmented into different heartbeats of samelength (200 samples each) by using R-peak location in-formation in the given annotations In total 35875 beatsform both databases (13724 from MIT-BIH arrhythmiadataset and 22151 beats from MIT-BIH supraventriculararrhythmia dataset) were considered FIR approximation ofMayers wavelet was applied on each heartbeat segment ICAwas applied on the resulting 4th level approximation and 4thlevel detail coefficients independently to remove the re-dundancy between feature coefficients In addition dynamicfeatures of the ECG signal were represented by four types ofRR interval features and Teager energy operator was used torepresent the nonlinear dynamics of the ECG signal

A 3-fold cross-validation method was used for trainingand testing of the classifier e complete dataset (35 875beats) was subsampled into two sets one having 70 of thetotal samples and the other with the remaining heartbeatsfrom each of the classes (50 data was selected for trainingfrom atrial escape beat (e) due to very low number of beats)70 of total heartbeats were utilized for training purposeand the rest of the heartbeats were used in testing andevaluation of the performance of the classifier e processwas repeated three times with different heartbeats used fortraining and testing e average results of the three foldswere calculated to assess the general performance of theproposed method Sensitivity (Se) specificity (Sp) positivepredictive value (PPV) and accuracy (Acc) were calculatedto analyze the performance

321 Class-Oriented Evaluation In class-oriented evalua-tion scheme a neural network was trained to predict the classof test heartbeats among 18 different classes of arrhythmiae specificity sensitivity and PPV of each individual classare summarized in Table 2 which shows that the proposedapproach demonstrates a reasonable individual-class per-formance showing greater specificity good sensitivity andPPV Moreover it has given better results on classes whose


presence was low like ldquoQrdquo ldquoerdquo and ldquoxrdquo e performanceevaluation measures are shown for all three folds in Table 2where the best accuracy achieved was 999 In addition987 average sensitivity 999 average specificity 998average PPV and 9975 overall accuracy were achievedeconfusion matrix is presented in Table 3 which shows thecorrectly classified and misclassified arrhythmia classes

A comparison of the classification accuracy of theproposed approach and state-of-the-art methods based onclass-oriented strategy is presented in Table 4 An im-provement in accuracy with increased number of classes andreduced feature dimension is observed In addition otherperformance parameters such as sensitivity specificity andPPV have also shown improvementese results depict thatthe proposed method is more generalized and computa-tionally efficient for arrhythmia classification An assessmentbased solely on ldquoclass-orientedrdquo scheme is not a faithfulmeasure for the performance analysis of a real-timeheartbeat classification system e performance of thesubject-oriented scheme is also analyzed for practicalevaluation

322 Subject-Oriented Evaluation In subject-orientedscheme results have reported according to the ANSIAAMI standard e specificity sensitivity PPV and

accuracy of each individual class in ANSIAAMI standard isshown in Table 5e hybrid feature approach demonstratedreasonable individual-class performances showing greaterspecificity good sensitivity positive PPV and accuracy forall classes e fusion beats showed lower PPV in Fold IIwhich can be attributed to smaller number of beats in thisclass e peak accuracy achieved was 999 with 997average sensitivity 999 average specificity 991 of av-erage PPV and 998 average accuracy Table 6 shows theconfusion matrix depicting an improved performance on allclasses with an average accuracy of 998 A comparativeanalysis is presented in Table 7 with state-of-the-art tech-niques for subject-oriented classification A significant im-provement is observed when taking this fact into accountthat 18 arrhythmia classes were classifiede comparison isreported for methods using the MIT-BIH data for addingcredence to the results

4 Conclusion

In this paper a new technique for automatic heartbeatclassification of all types of arrhythmia was presented Animproved hybrid feature representation of heartbeat seg-ments was used based on a mixture of a set of derivedmorphological and dynamic features e classification wasdone using twelve ICA projection coefficients computed

Table 1 Mapping from MIT-BIH arrhythmia database (MITDB)supraventricular arrhythmia database (SVDB) heartbeat classes toANSIAAMI heartbeat classes

AAMI classes MITDBSVDB classes TotalNonectopic beat (N) NOR LBBB RBBB AE NE 30929Supraventricular ectopic beat (S) APC AP APB NP SP 1538Ventricular ectopic beat (V) PVC VE VF 2035Fusion beat (F) F 14Unknown beat (Q) UN FPN PACE ∣ 1329

Table 2 A summary of performance analysis of the proposed method on each arrhythmia class in the ldquoclass-orientedrdquo scheme

Fold I Fold II Fold III AverageHeartbeat type Sp Se PPV Acc Sp Se PPV Acc Sp Se PPV Acc Sp Se PPV AccNormal beat (NOR) 100 100 998 994 100 100 999 997 998 100 997 997 999 100 998 996Atrial premature contraction 100 97 100 100 100 965 100 100 100 975 100 100 100 97 100 100Fusion of ventricular and normal beat 100 100 100 100 100 100 100 927 100 100 100 100 100 100 100 975Left bundle branch block (LBBB) 100 975 100 100 100 97 100 993 100 965 100 100 100 97 100 997Unclassifiable beat (UN) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100Right bundle branch block beat (RBBB) 998 994 994 997 999 992 994 994 999 993 994 994 998 993 994 995Premature ventricular contraction (PVC) 100 991 996 997 100 992 996 995 100 993 996 996 100 992 996 996Ventricular flutter wave (VF) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100Aberrated atrial premature beat (AP) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100Nodal (junctional) premature beat (NP) 100 926 100 100 100 928 100 100 100 927 100 100 100 928 100 100Atrial escape beat (AE) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100Fusion of paced and normal beat (FPN) 100 100 100 100 999 100 100 100 100 100 100 100 100 100 100 100Isolated QRS-like artifact (Iso) 999 100 972 100 999 100 981 100 999 991 991 991 999 981 981 995Ventricular escape beat (VE) 100 100 100 100 999 100 952 100 999 100 985 100 100 952 100 100Nodal (junctional) escape beat 999 100 95 100 999 100 975 100 100 100 100 100 100 100 100 100Paced beat (PACE) 999 100 992 100 100 100 100 100 999 100 996 100 100 992 100 100Nonconducted P-wave (blocked APB) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100Supraventricular premature beat (SP) 100 100 100 100 100 100 100 100 100 100 100 100 100 994 100 100Average 999 999 994 999 999 993 994 993 999 999 998 999 999 987 998 9975


from the DWT features plus four RR interval features andTeager energy value Two types of evaluation schemes class-and subject-oriented were implemented for analyzing thesystem On the standard benchmark of MIT-BIH arrhyth-mia database and MIT-BIH supraventricular arrhythmiadatabase an average accuracy of 9975 with a peak ac-curacy in a single fold of 999 in the class-oriented eval-uation was achieved An accuracy of 998 in the subject-oriented evaluation was achieved In future an automaticpatient customization scheme will be considered allowing

Table 3 Confusion matrix for the proposed method using a neural network based classifier (class-oriented scheme)

Predicted labels N A F L Q R V a J E f mdash E J X SActual labelsN 8656 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A 2 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0F 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0L 8 0 0 260 0 0 0 0 0 0 0 0 0 0 0 0 0 0Q 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0R 2 0 0 0 0 314 0 0 0 0 0 0 0 0 0 0 0 0V 4 0 0 0 0 0 564 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0a 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0J 1 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0e 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0f 0 0 0 0 0 0 0 0 0 0 0 33 0 0 0 0 0 0mdash 0 0 0 0 0 2 0 0 0 0 0 0 105 0 0 0 0 0E 1 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0j 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 254 0 0x 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0S 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 376

Table 4 Comparison of the proposed scheme with state-of-the-art methods using class-oriented scheme

Features Dimension Classes Accuracy Sensitivity Specificity PPVProposed DWT + RR + TEO 17 18 9975 987 999 998Zidelmal et al [8] Frequency content + RR + QRS 13 2 972 99 mdash mdashYe et al [11] WT + ICA + RR 18 16 993 913 mdash mdashEbrahimzadeh et al [4] HOS + timing interval 24 5 9518 9561 988 906Pathoumvanh et al [24] DCT 5 5 9911 9701 9944 mdashRabee and Barhumi [26] Multi resolution WT 251 14 992 962 100 mdashAlajlan et al [27] HOS of 2nd-order-cumulant 604 2 9496 9219 9519 mdashde Oliveira et al [14] Waveform + RR mdash 2 95 95 9987 98Li et al [19] Timing interval + waveform amplitude mdash 2 982 931 mdash 814

Table 5 Performance of the proposed method on each arrhythmia class in the ldquosubject-orientedrdquo scheme

Fold I Fold II Fold III AverageHeartbeat type Sp Se PPV Acc Sp Se PPV Acc Sp Se PPV Acc Sp Se PPV AccNonectopic beats (N) 999 100 942 100 997 999 998 999 100 100 998 100 999 999 979 999Supraventricular ectopic beats (S) 100 998 100 100 999 100 998 100 100 999 100 100 999 996 999 100Ventricular ectopic beats (V) 100 996 100 100 100 995 100 999 100 997 100 100 100 996 100 999Fusion beats (F) 999 100 999 100 999 100 928 989 999 100 100 100 999 100 976 996Unclassifiable beats (Q) 100 996 100 998 100 994 100 100 100 995 100 997 100 995 100 998Average 999 998 988 999 999 997 996 977 999 998 999 999 999 997 991 998

Table 6 Confusion matrix for Fold II using NN (subject-orientedscheme)

Predicted labels N S V F QActual labelsN 9280 1 0 0 0S 1 458 0 1 0V 2 0 604 0 0F 0 0 0 14 0Q 2 0 0 0 398


the heartbeat classification method to be able to adjust toindividual physiological features using wearable sensors

Data Availability

e data used in this study are available for download at thephysionet MIT-BIH website

Conflicts of Interest

e authors declare that there are no conflicts of interestregarding the publication of this paper

Authorsrsquo Contributions

Syed Muhammad Anwar and Maheen Gul contributedequally to the study

References

[1] H V Huikuri A Castellanos and R J Myerburg ldquoSuddendeath due to cardiac arrhythmiasrdquo New England Journal ofMedicine vol 345 no 20 pp 1473ndash1482 2001

[2] P de Chazal and R B Reilly ldquoA patient-adapting heartbeatclassifier using ECG morphology and heartbeat intervalfeaturesrdquo IEEE Transactions on Biomedical Engineeringvol 53 no 12 pp 2535ndash2543 2006

[3] A Mustaqeem S M Anwar M Majid and A R KhanldquoWrapper method for feature selection to classify cardiacarrhythmiardquo in Proceedings of 39th Annual InternationalConference of the IEEE Engineering in Medicine and BiologySociety (EMBC) pp 3656ndash3659 IEEE Jeju Island SouthKorea July 2017

[4] A Ebrahimzadeh B Shakiba and A Khazaee ldquoDetection ofelectrocardiogram signals using an efficient methodrdquo AppliedSoft Computing vol 22 pp 108ndash117 2014

[5] G de Lannoy D Franccedilois J Delbeke and M VerleysenldquoWeighted conditional random fields for supervised inter-patient heartbeat classificationrdquo IEEE Transactions on Bio-medical Engineering vol 59 no 1 pp 241ndash247 2012

[6] S Poungponsri and X-H Yu ldquoAn adaptive filtering approachfor electrocardiogram (ECG) signal noise reduction usingneural networksrdquo Neurocomputing vol 117 pp 206ndash2132013

[7] E J d S Luz D Menotti andW R Schwartz ldquoEvaluating theuse of ECG signal in low frequencies as a biometryrdquo ExpertSystems with Applications vol 41 no 5 pp 2309ndash2315 2014

[8] Z Zidelmal A Amirou D Ould-Abdeslam and J MerckleldquoECG beat classification using a cost sensitive classifierrdquoComputer Methods and Programs in Biomedicine vol 111no 3 pp 570ndash577 2013

[9] R J Martis U R Acharya and L C Min ldquoECG beat clas-sification using PCA LDA ICA and discrete wavelet

transformrdquo Biomedical Signal Processing and Control vol 8no 5 pp 437ndash448 2013

[10] A Mustaqeem S M Anwar and M Majid ldquoMulticlassclassification of cardiac arrhythmia using improved featureselection and SVM invariantsrdquo Computational and Mathe-matical Methods in Medicine vol 2018 Article ID 731049610 pages 2018

[11] C Ye B V Kumar and M T Coimbra ldquoHeartbeat classi-fication using morphological and dynamic features of ECGsignalsrdquo IEEE Transactions on Biomedical Engineering vol 59no 10 pp 2930ndash2941 2012

[12] M Abo-Zahhad S M Ahmed and S N Abbas ldquoBiometricauthentication based on PCG and ECG signals present statusand future directionsrdquo Signal Image and Video Processingvol 8 no 4 pp 739ndash751 2014

[13] E Ullah A D Bakhshi M Majid and S Bashir ldquoEmpiricalmode decomposition for improved least square t-wavealternans estimationrdquo in Proceedings of 15th InternationalBhurban Conference on Applied Sciences and Technology(IBCAST) pp 334ndash338 IEEE Islamabad Pakistan January2018

[14] L S de Oliveira R V Andreatildeo and M Sarcinelli-FilholdquoPremature ventricular beat classification using a dynamicbayesian networkrdquo in Proceedings of Annual InternationalConference of the IEEE Engineering in Medicine and BiologySociety EMBC pp 4984ndash4987 IEEE Boston MA USAAugust-September 2011

[15] T Mar S Zaunseder J P Martınez M Llamedo and R PollldquoOptimization of ECG classification by means of featureselectionrdquo IEEE Transactions on Biomedical Engineeringvol 58 no 8 pp 2168ndash2177 2011

[16] M M Tantawi K Revett A-B Salem and M F Tolba ldquoAwavelet feature extraction method for electrocardiogram(ECG)-based biometric recognitionrdquo Signal Image and VideoProcessing vol 9 no 6 pp 1271ndash1280 2015

[17] F Jabloun A E Cetin and E Erzin ldquoTeager energy basedfeature parameters for speech recognition in car noiserdquo IEEESignal Processing Letters vol 6 no 10 pp 259ndash261 1999

[18] S Khan S M Anwar W Abbas and R Qureshi ldquoA noveladaptive algorithm for removal of power line interferencefrom ECG signalrdquo Science International vol 28 no 1pp 139ndash143 2016

[19] P Li C Liu X Wang D Zheng Y Li and C Liu ldquoA low-complexity data-adaptive approach for premature ventricularcontraction recognition signalrdquo Image and Video Processingvol 8 no 1 pp 111ndash120 2014

[20] S Kadambe R Murray and G F Boudreaux-BartelsldquoWavelet transform-based QRS complex detectorrdquo IEEETransactions on Biomedical Engineering vol 46 no 7pp 838ndash848 1999

[21] P S Addison ldquoWavelet transforms and the ECG a reviewrdquoPhysiological Measurement vol 26 no 5 p R155 2005

[22] G B Moody and R G Mark ldquoe MIT-BIH arrhythmiadatabase on cd-rom and software for use with itrdquo in

Table 7 Comparison of the proposed scheme with state-of-the-art methods using subject-oriented scheme

Features Dimensions Classes Accuracy Sensitivity SpecificityProposed DWT + RR interval + TEO 17 5 (18) 998 997 999Ye et al [11] WT + ICA + RR 18 5 (16) 864 913 mdashMartis et al [9] DWT + ICA 12 5 (15) 9928 9797 9983Mar et al [15] RR interval series and WT mdash 3 93 80 82de Lannoy et al [5] Waveform + HOS + RR 249 5 (16) 94 mdash mdash


Proceedings of Computers in Cardiology 1990 pp 185ndash188IEEE Chicago IL USA 1990

[23] A L Goldberger L A Amaral L Glass et al ldquoPhysiobankphysiotoolkit and physionetrdquo Circulation vol 101 no 23pp e215ndashe220 2000

[24] S Pathoumvanh K Hamamoto and P Indahak ldquoArrhyth-mias detection and classification base on single beat ECGanalysisrdquo in Proceedings of 4th Joint International Conferenceon Information and Communication Technology Electronicand Electrical Engineering (JICTEE) pp 1ndash4 IEEE ChiangRai ailand March 2014

[25] Association for the Advancement of Medical Instrumentationand others Testing and reporting performance results ofcardiac rhythm and st segment measurement algorithmsANSIAAMI EC38 vol 1998 1998

[26] A Rabee and I Barhumi ldquoECG signal classification usingsupport vector machine based on wavelet multiresolutionanalysisrdquo in Proceedings of 11th International Conference onInformation Science Signal Processing and their Applications(ISSPA) pp 1319ndash1323 IEEE Montreal Canada July 2012

[27] N Alajlan Y Bazi F Melgani S Malek and M A BencherifldquoDetection of premature ventricular contraction arrhythmiasin electrocardiogram signals with kernel methodsrdquo SignalImage and Video Processing vol 8 no 5 pp 931ndash942 2014


Stem Cells International

Hindawiwwwhindawicom Volume 2018


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EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



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applied on each ECG signal to make it noise free e high-order statistics of ECG signals and three timing intervalswere considered as features which were then fed to a hybridbees algorithm for training the radial basis function andresulted in an average accuracy of 9518

Some techniques rely on fiducial features which aretemporal and dynamic features that directly depend uponthe ECG characteristics eg wave onset point peaks(maximaminima) and offset [5] Nonfiducial features thatare directly derived from the fiducial features or obtained bysegmenting the ECG signal into several parts have also beenused [6] ese features are not found good enough whenused independently for accurate classification of ECG ar-rhythmia A combination of both fiducial and nonfiducialfeatures was used for defining a biological marker for personidentification [7 12]

e extracted features have usually been analyzed eitherin time domain or frequency domain Some of the mostimportant time domain features including RR interval STsegment and T height require identification of key timepoints within the signal [13] An approach for heartbeatclassification with dynamic rejection thresholds was pro-posed using QRS morphology frequency information ACpower of QRS detail coefficients and RR intervals as featuresto represent ECG beats [8] A support vector machine(SVM) was used to classify with an improved accuracy of972 and minimum rejection cost A combination of timeand frequency information has also been used in[9 11 14ndash16] giving better extraction of information froma quasiperiodic ECG signal using wavelet transform whichprovides a good time and frequency resolution Five im-portant types of arrhythmia namely nonectopic ventricularectopic supraventricular ectopic unclassifiable beats andfusion betas were analyzed and detected in [9] Featureswere extracted using DWT and only significant coefficientswere selected by applying independent component analysiswhich in combination with neural network yielded an ac-curacy of 9928

All methods discussed above have the followingshortcomings

(1) Performances of most of the methods have beentested only on smaller data sets and there is a need toverify their performance on larger databases

(2) Selected classes of arrhythmia have been evaluatedand there is a need to test all arrhythmia classes

(3) e classification accuracy on sparsely occurringarrhythmia classes is not good

In this paper a novel technique for ECG beat classifi-cation of arrhythmia is proposed that considers a hybrid ofenhanced morphological and dynamic features to overcomethese shortcomings Morphological features are obtainedusing DWTon each heartbeat e resulting features consistof DWT approximation coefficient and detail coefficients atlevel 4 Independent component analysis is applied on bothapproximation and detail coefficients independently to ex-tract only important coefficients In addition four types ofRR interval features are calculated to represent the dynamic

features of ECG heartbeats Moreover to enhance the dy-namic features of ECG heartbeats corrupted with Gaussiannoise Teager energy operator (TEO) [17] is used All thesefeatures are combined and fed to a neural network (NN) forautomatic classification of heartbeats into different ar-rhythmia types according to both class-oriented scheme (18classes) and subject-oriented scheme (5 classes)

e rest of the paper is organized as follows Firstly theproposed methodology is explained in detail in Section 2Class- and subject-oriented evaluation of the proposedsystem is presented in Section 3 followed by conclusion inSection 4

2 Methodology

e proposed methodology is presented in Figure 1 whichconsists of four major phases namely preprocessingheartbeat segmentation feature extraction and featureclassificatione details of these phases are presented in thefollowing subsections

21 ECG Signal Preprocessing Mostly ECG signals are af-fected by baseline wander or power line interface (PLI)Different methods were introduced to remove these types ofnoises from the ECG signal [18] Experiments showed thatbaseline wander significantly affects the detection of ar-rhythmia and makes the ECG signal analysis difficult for anexpert [19] PLI is a type of noise that occurs due to brokenelectrodes offset voltages in the electrodes movement ofpatient respiration errors or electrode resistance whilerecording the ECG signal It is a low frequency noise andtypically exists in the frequency range of 0ndash03Hz Forbaseline wander removal the expected value of the signalwas subtracted from the raw ECG signal to obtain the noise-free ECG signal using

x[n] xr[n]minus μ (1)

where x[n] is the denoised signal xr[n] represents the rawECG signal and μ is the expected value of the raw EEG signal

22 Heartbeat Segmentation ree basic constituents ofa heart cycle are QRS complex T wave and P wave termedas fiducial pointse correct splitting of the ECG signal intoheartbeat segments involves recognition of borders and peaklocations of these fiducial points e information about theR-peak locations given in the dataset was used to obtainthese heartbeat segments A single heartbeat consisted of 200samples including the R-peak and the samples around thepeak is segment size contained maximum information ofa single heartbeat and is shown in Figure 2

23 Feature Extraction In feature extraction improvedfeatures based on DWT RR interval and Teager energyoperator were selected which were able to represent themorphological and dynamic changes in the ECG signal withmore significance







RRlocal 110sum5

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RRave 1NRR

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Feature extraction


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ECGsignal


0ndash06

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PPAR Research



Volume 2018


Journal of

ObesityJournal of
























RRlocal 110sum5

iminus5RR(i)

RRave 1NRR

sumNRR

i1RR(i)

(2)



Preprocessingx(n)


Feature extraction


ICA

RR interval



ECGsignal


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02

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08

1

100 200 300 400 500 600 700

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Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of





















ANE 1N

1113944NE x[n] (4)

















4 Conclusion


















Data Availability






References






































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Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of























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Data Availability






References






































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Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of






























Data Availability






References






































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of






























of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of



















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