MODIFIED QRS DETECTION ALGORITHM FOR ECG SIGNALS

(1)

J. Mech. Cont.& Math. Sci., Special Issue, No.-5, January (2020) pp 84-94

Copyright reserved © J. Mech. Cont.& Math. Sci.

Anchula Sathish et al

84

MODIFIED QRS DETECTION ALGORITHM FOR ECG SIGNALS

Anchula Sathish

¹

, V Phalguna Kumar

²

1

Professor and Head of Department, Department of Electronics &

Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Andhra Pradesh, India

2

Assistant Professor, Department of Electronics & Communication Engineering, Rajeev Gandhi Memorial College of Engineering and

Technology, Andhra Pradesh, India

1

[email protected],

²

[email protected] Corresponding Author: V Phalguna Kumar

https://doi.org/10.26782/jmcms.spl.5/2020.01.00007

Abstract

This paper proposes an algorithmic approach to find QRS complex in an ECG signal. These QRS complexes help to identify the functioning of heart and to detect the symptoms of cardiac arrest. Tele-health applications are increasing its range day by day. Normal algorithms cannot analyses the Telehealth ECG signal. So proposed algorithm used to analyses Tele ECG signals. Normal algorithms can detect QRS complex which are recorded in pure clinical ECG where the noise level will be low. The proposed algorithm is able to detect QRS complex recorded in Tele Health Environment were the noise level will be high.

Keywords :

ECG, QRS complex, Tele-Health, Detection, Bio Medical Signal Processing

I.

Introduction

One of the most common causes of deaths globally is Cardio Vascular Diseases (CVDs). In 2018, about 17 Million people died because of CVDs, which is 31% of all global deaths [I].An Electrocardiogram (ECG) signal gives evidence about the myocardium electrical activity of heart recorded on the body`s surface.

ECG signal gives the information about cardiac health. The truthful and real time heart beat finding of the ECG signal is very important in monitoring CVDs. In an ECG signal QRS complex is important waveform. QRS complex helps in finding the heart rates, classifying cardiac cycle and also useful in finding abnormally.

Tele health uses the present available technology to monitor patient’s health condition remotely. Now a days people are adopting tele-health technology into their lives. They are performing ECG test at home by themselves. So that it will be easy to monitor their health anywhere without having to go to hospital which in turn can reduce the expenses.

The Paper Presented at National Conference on Recent Trends & Challenges in Engineering Organized by Rajive Gandhi Memorial College, AP, India

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES ISSN (Online) : 2454 -7190 www.journalimcms.org ISSN (Print) 0973-8975

(2)

85

But one of the main problem in tele-health ECG recording is reduction in signal quality, because tele-health ECG checking devices use dry electrodes for recording ECG. Therefore, the original signal amplitude turn into smaller and ECG signal look far noisier. In addition to this problem, artifacts are more frequent in this type of ECG recordings.

In this paper, we projected a modified QRS detection algorithm through algorithmic analysis. This algorithm is applied to many databases [XI] which given more better results compared to our previous work [XXIV]

II State Of Art

In literature there are many numeral approaches are available for detecting QRS complex. Especially using signal processing and classification techniques. Over analysis it came to notice that any technique which is used for knowing ECG beat mainly consists of three phases. 1. Pre-processing 2. Extracting features and 3.Classification.These three stages combined in different fashions to form an individual technique.

The main aim of the pre-processing stage in any technique is to remove or at least to reduce background noise, this is generally achieved by using the denoising techniques. So there are so many denoising techniques used in literature like Chazal et, al. used medium filter and 12 tap low pass filter [IV]-[V] and Mar et.al and Lin.et.al used to two medium filters [VI] – [VII]. Li [VIII] used high pass filter with 1 Hz of cut off frequency, Das [IX] used a bandpass filter of [0.1Hz– 100Hz] cut off frequencies, Kim [X] used a Morphological filter, Alickovie [XI] used the combination of Discrete Wavelet Transform and Principal Component Analysis to form a multiscale principal component analysis (MSPCA) filter. Ye [XII] used approach of wavelets,Banerjee [XIII] used the approach of Discrete Wavelet Transform. Mitra [XIV] used a band pass filter with [5Hz – 12Hz] such that baseline wanders can be removed. Therefore, looking across any pre-processing stage it mainly consists of filters like MF, LPF, HPF, BPF, and Notch Filter [VIII].

Morphological filter [X] MSPCA filter [XI]

The next stage is the feature extraction i.e., QRS complexes, for this in literature different signal procession techniques were proposed resulting in different types of features like Chazal [IV] – [V] , lim [VII] extracted morphological features and R – R peak features, Mar [VI] obtained Temporal features, Morphological features and Statistical features, Li [VIII] obtained temporal features, Spectral features and Statistical features. Das [IX] obtained RR peak distance, S-transform

&BocternaForaging optimization Algorithm features [(BFO) features] as ECG feature extraction. Kim [X] obtained CWT, Morphological features, R to R peak features, PCA.Alicko vie [XI] obtained statistical features, Ye [XII] obtained wavelet, ICA, RR features, Banerjee [XIII] obtained cross wavelet , WCS(Wavelet Cross Spectrum), WCOH (Wavelet Coherence) , Mitra [XIV] obtained morphological features, Linh [XV] obtained Hermite polynomial. Hang [XVI]

obtained combination Hermite Polynomial & R – R peak features.

Ghorbanian[XVIII] extracted features using PCA, Continuous Wavelet Transform, and Neural Networks. Sure Z [XIX] obtained polynomial features & GA features.

(3)

86

Melgam [XX] obtained Morphology &Temporal and R-R peak features, Lnce [XXI]

obtained Wavelet features & R – R peak features, Graja [XXII] obtained Morphological & Temporal features, Kuranyas [XXIII] obtained temporal & spectral features.

In ECG feature extraction, Morphological features, temporal features, Statistical features, Spectral features (Stockwell) S-transform features, CWT features, PCA features, are generally extracted using different signal processing techniques.

After feature extraction, beat classification is the third stage in most of the signal processing algorithms. Chazal [IV] – [V] Lin [VII] obtained beat classification using Linear Discriminant Analysis [LDA].Mar[VI] obtained beat classification using combination of LDA & MLP [Multi-Layer Perception].Li [VIII], Das [IX], Ye [XVII], obtained beat clarification using support vector Machine. Melgani [XX]

obtained beat classification using combination of SVM & PSO (Partical Swarm Optimization). And many other studies proved tomany other classifiers like Kim [X]

used Extreme Learning Machine [ELM] Banerjee [XIII] used Threshold based clarification, Mitra [XIV] used rule based on Rought set, Linh [XV] used Neuro–

Fuzzy Network concepts, Jiang [XVI] used Block-based Neural Network concepts [BbNN], Surez [XIX] used Geometric Matching [GM] concepts.

By observing all the three stages work proposed in literature and observing the end accuracy there is a great variation especially when working with larger data bases and Noisy ECG signals with Artefacts& external noises which are added to that larger data bases.

III Methodology

A schematic representation of processing steps of the anticipated algorithm is shown in figure 1.Artifact Masking & ECG filtering are the pre-processing steps, finding differentiation & envelops & Multiplying them followed by squaring are used for feature extraction. Threshold setting &peak detection are beat classification steps.Finally Backtracking is used for rechecking the resulted QRS locations to find exact QRS locations.

Comparing to this block diagram in Fig.1 with authors previous work[XXIV], the pre-processing steps are given more importance, and Beat classification is done by using threshold setting. Backtracking is included at the end for finding maximum possible QRS locations.

(4)

87 RAW ECG SIGNAL

QRS COMPLEX

Fig. 1: Block Diagram of proposed algorithm

The advantages of adding these steps for authors previous work [XIV]includes complete removal of arte facts in pre-processing stage and this algorithm primarily focused on pure clinical ECG data so the proposed algorithm improves the QRS complex detection.

A. Raw ECG Signal

Raw ECG Signal is the information which is undergoing different processing steps and this information i.e., Raw ECG signal is collected from different databases available in internet [II] - [III]. These different databases are used here for identifying whether proposed automated Analysis is equal to the exact QRS detection Accuracy.

The data bases which used here are 1) MIT-BIH Arrhythmia (ARR Data Base) 2) Noise stress test (NST Data base)3) Tele Data base (Tele health Data Base). Other than the above mentioned Databases, additional ECG signals which are manually generated are also used as RAW ECG signal.

ARTEFACT MASKING

ECG FILTERING

DIFFERENTIATOR FINDING ENVELOPS

MULTIPLIER SQUARING THRESHOLD SETTING

PEAK DETECTION

BACK TRACKING

(5)

88

1)

ARR Data Base:

Most frequently used Data base in literature is ARR Data base, this data base consists of 48 duo clinical ECG records that are recorded from 47 dissimilar subjects, All recordings are taken out in a clean accomplished clinical surroundings. These records were completed by digitizing at proportion of 360 samples / Sec for every channel per 11 bit resolution finished in a range of 10 MV.

2)

NST Data Base:

NST Data base [XIX] always finds as an alternative to the ARR data base as far as its usage is concerned in literature. It consists of 12 ECG recordings. From ARR data base, two clean recordings were taken [118, 119]

recordings and three different types of noises i.e. base-line wandering, muscle (EMG) artefact, and electrode motion artefact were added in different fashions to obtain 12 different ECG recordings. Thus for two 2 clean recordings (118, 119) of ARR data base, a total of 12 different NST recordings are obtained by adding 6 different levels of noises.

3)

TELE Data Base:

This data base be made up ofofa number of ECG signals that were recorded in a tele-health atmosphere [XXI] these signals were recorded using silver / silver chloride plate electrodes single lead – I systems are used to record this data. Out of 300 tele-health recordings, after rejecting 50 recordings remaining 250 recordings are available in open data base as the Tele Data base A large amount of noise and interface is added the Tele-health ECG signals.

B. ArtEefact Masking

This is the first pre-processing stage to which the RAW ECG signal is given. In ECG signal there will be many types of artefacts present like loose lead artefact, wandering baseline artefact, Muscle tremor artifact, Electromagnetic interface (EMI). To overcome these types of artefacts, several masking techniques are used [XXV]

Five artefacts that are possible to add to the raw ECGsignal have been explored in [XXIII]

 Motion artefact:This is mainly due to body motion while recording the ECG signal. This results in addition of 5Hzsine wave signal to the original ECG signal.

 Base drift artefact: This is mainly due to breathing while recording the ECG signal. This results in introduction of 0.5 Hz sine wave to the real ECG signal

 EMI artefact: This is mainly due to electrical interference which results in addition of 50 Hz sine wave to the original signal.

 Muscle Tremor artefact: This is a type of Random Noise mainly added to the original ECG signal due to electromyography.

 Due to improper placement of sensors, the ECG signal will get attenuated which results in low signal to Noise Ratio.

The above mentioned artefacts are removed in Artefact masking stage. Here several types of masks are used like HFM[High Frequency Mask], RCM [Rail Contact Mask],LPM[Low Power Mask], BSM [Base like shift Mask] and some combination of these masks[XXVI].

(6)

89 C. ECG Filtering:

The output of Art efact Masking is given to the filter stage. Here a Band Pass filler is used in which an infinite Impulse Response method is applied. This is prepared to decrease the baseline drift of the ECG signal.

D. Differentiator:

The output of the ECG filtering process is given to the FIR derivative filter.

Generally anold-fashioned derivative filer is not used as they are tough enough to noise. Let the derivative of the filtered ECG signal 𝑥(𝑛)is 𝑑(𝑛).

E. Finding Envelope:

Here two envelops upper envelop and lower envelop of𝑥(𝑛)i.e., 𝑢(𝑛)and 𝑙(𝑛)are calculated and the upper envelop is subtracted from lower envelop to result in a Amplitude Envelop 𝑒(𝑛)

𝑒(𝑛) = 𝑢(𝑛) − 𝑙(𝑛) (1)

F. Multiplier:

Here the outputs of two stages i.e., Differentiator 𝑑(𝑛)& Amplitude envelope 𝑒(𝑛) are multiplied. The resulted signal is 𝑚(𝑛)

𝑚(𝑛) = 𝑑(𝑛) ∗ 𝑒(𝑛) (2)

This is done for correct finding of QRS feature. The correct QRS feature should be capable to identify sudden growth in derivative &higher local amplitude.

G. Squaring:

The output of the multiplier is subjected to squaring operation point by point this is done to make all data points positive, such that only QRS complex are visible at the output i.e., 𝑠(𝑛).

H.

Threshold setting

:

The Threshold level is generally fixed based on topmost (R to R) amplitude dissimilarities in the last featured signal𝑠(𝑛). The mean value of the 𝑠(𝑛)is calculated and the peaks at least above 20% of the mean value are considered to be original QRS complex

T= 20% of {meanof (s[n])}. (3)

T is Threshold level I. Peak Detection:

In an ECG signals, the instant rise and instant fall are considered as peak regions.

This is chosen by finding local maxima’s and local minima’s at the signal. If the signal maximum value is higher than average value then it is local maxima. If it the single maximum value is low than average values then it is local minima. Therefore, the peak signals are those which have amplitude values among local maxima’s.

J.

Back Tracking:

(7)

90

After completing all the steps, there are some chances for not correctly detecting the QRS complexes (or) there are chances for detecting errored QRS complex. In such cases, it is conceivable to isolate the clear-cut QRS locations. One of the possibility is to compare the threshold level; with the ending peak signals (feature signals) this process is called back tracking.

IV. Simulation Results and Analysis

Here two parameters are used to analyses the operations of suggested algorithm compared with previous algorithms. The two parameters are sensitivity and positive predictivity. Sensitivity tells about how much correctly the algorithm is able to detect the beats. Positive Predictivity tells about how much effectively the algorithm is able to differentiate among true and false beats.

+𝑝 = (4)

𝑆𝑒(%) = (5)

Where 𝑇 is Totalbeats number that are correctly noticed and they are really correct. 𝐹 isTotal beats number which are not correctly noticed as they are really correct. 𝐹 is total beats numbe rthat are noticed but they are really false beats.

Result analysis of QRS report of an ECG from 100_120 record TELEdatabank is as shown in below figure 2 to figure 9

Fig 2: Raw ECG signal

Fig 3: Artefact Masked signal

(8)

91

Fig 4: Filtered ECG signal

Fig 5: Differentiated ECG signal

Fig 6: Enveloped ECG signal

Fig 7: Output of Multiplier

Fig 8: Output of Squaring

(9)

92

Fig 9: Threshold Determination

The RAW ECG signal is downloaded from TELE databank database as shown in figure 2. Figure 3 shows the output of the Artefact masking stage. Figure 4 shows the output of the ECG filtering stage. Figure 5 shows differentiation of Filtered ECG signal. Figure 6 shows the Envelope of the filtered ECG signal. Figure 7 shows the output of the multiplier stage. Figure 8 shows the output of the squaring stage. Figure 9 shows the output of the threshold Determination.

Traditionally threshold level is calculated to 20% of the peak value. Here threshold levelis calculated as

T=0.2 * mean (s[n]) T=20 % (0.6) T=0.12

The QRS locations which are above 0.12 amplitude are considered as detected QRS locations.The results, of telehealth data base recording which undergo each stage of proposed algorithm are shown in below table.

Table 1: Telehealth Data Base Sensitivity and Positive Predictivity

TELEDATA BASE

(Parameter)

Previous Work [XXIV]

Proposed (Modified) Algorithm

Sensitivity 96.54 % 97.21 %

Positive Predictivity 74.27 % 87.46 %

Sensitivity and positive predictivity is calculated for all the records in tele data base and it shows that proposed algorithm shows better results.

V. Conclusion

Here an algorithm is proposed which is capable of detecting QRS complex from Tele-health ECG signals which are subjected to noise and interfaces. This is done by modifying our previous work. By using this algorithm the exact positions of QRS complexes are recognized. By enhancing the present work, the truthfulness of QRS Detection of a TELE ECG signals can be improved. This algorithm shows better results compared to various algorithms.

(10)

93

References

I. A. Ruha, S. Sallinen, S. Nissila, “A real-time microprocessor QRS detector system with a 1ms timing accuracy for measurement of ambulatory HRV”, IEEE Trans.Biomed.Eng., pp,159-167,1997.

II. C. C. Lin, C. M. Yang, “Heartbeat classification using normalized intervals and morphological features”, Mathematical Problems in Engineering, 2014.

III. C. Ye, B. Kumar, M. Coimbra, “An automatic subject-adaptable heartbeat classifier based on multi-view learning,” IEEE J. Biomed. Health Informatics, Vol. 20, Issue. 6, 2016.

IV. C. Ye, B.V.K. Vijaya Kumar, M.T. Coimbra, “Heartbeat classifi-cation using morphological and dynamic features of ECG signals,” IEEE Trans.

Biomed. Eng., Vol. 59, Issue. 10, pp.2930-2941, 2012.

V. E. Alickovic, A. Subasi, “Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier”, J. Medical Systems, Vol. 40, Issue. 4, pp. 1-12, 2016.

VI. F. Melgani, Y. Bazi, “Classification of electrocardiogram signals with support vector machines and particle swarm optimization,” IEEE Trans.

Inform. Technol. Biomedicine, Vol. 12, Issue. 5, pp. 667-677, 2008.

VII. G. B. Moody, “A noise stress test for arrhythmia detectors”, Comput.Cardiol., Vol. 11, pp. 381–384, 1984.

VIII. G. B. Moody, R. G. Mark, “The impact of the MIT-BIH arrhyth-mia database, IEEE Eng. Med. Biol. Mag., Vol.: 20, Issue: 3, pp. 45–50, 2001 IX. J. Kim, S. D. Min, M. Lee, “An arrhythmia classification algorithm using a

dedicated wavelet adapted to different subjects,” Biomed. Eng. Online, Vol.

10, Issue. 1, 2011.

X. K. V. Surez, J. C. Silva, Y. Berthoumieu, P. Gomis, M. Najim, “ECG beat detection using a geometrical matching approach,” IEEE Trans. Biomed.

Eng., Vol. 54, Issue. 4, pp. 641-650, 2007.

XI. M. Elgendi, “Fast QRS detection with an optimized knowledgebased method: evaluation on 11 standard ECG databases,” PLoS One, Vol.: 8, article e73557, 2013.

XII. M. K. Das, S. Ari, “Patient-specific ECG beat classification tech-nique,”

Healthcare Technol. Lett., Vol.: 1, Issue: 3, 2014.

XIII. M. Milanesi, N. Martini, N. Vanello, L. Landini, “Independent component analysis applied to the removal of motionartefacts from electrocardiographic signals,” Med. Biol. Eng. Comput., Vol. 46, pp. 251–261, 2008.

XIV. P. d. Chazal, M. O. Dwyer, R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., Vol.: 51, Issue: 7, pp. 1196-1206, 2004.

(11)

94

XV. P. d. Chazal, R. B. Reilly, “A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval feature,” IEEE Trans. Biomed.

Eng., Vol.: 53, pp. 2535-2543, 2006.

XVI. P. Ghorbanian, A. Ghaffari, A. Jalali, C. Nataraj, “Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier,” Computing Cardiol, pp. 669-672, 2010.

XVII. Q. Li, C. Rajagopalan, G. D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Trans.

Biomed. Eng., Vol.: 61, Issue: 6, pp. 1607-1613, 2014.

XVIII. S. Banerjee, M. Mitra, “Application of cross wavelet transform for ecg pattern analysis and classification,” IEEE Trans. Instrum. Meas., Vol. 63, Issue. 2, pp. 326-333, 2014.

XIX. S. Kiranyaz, T. Ince, M. Gabbouj, “Real-Time patient-specific ECG classification by 1-D convolutional neural networks,” IEEE Trans. Biomed.

Eng., Vol. 63, Issue. 3, pp. 664-675, 2016.

XX. S. Mitra, M. Mitra, B. B. Chaudhuri, “A rough-set-based inference engine for ECG S. Graja, J. M. Boucher, “Hidden Markov tree model applied to ECG delineation,” IEEE Trans. Instrum. Meas., Vol. 54, Issue. 6, pp. 2163- 2168, 2005.

XXI. T. H. Linh, S. Osowski, M. Stodolski, “On-line heart beat recogni-tion using Hermite polynomials and neuro-fuzzy network,” IEEE Trans. Instrum.

Meas., Vol. 52, Issue. 4, pp. 1224-1231, 2003.

XXII. T. Ince, S. Kiranyaz, M. Gabbouj, “A generic and robust system for automated patient-specific classification of ECG signals,” IEEE Trans.

Biomed. Eng., Vol. 56, pp. 1415-1426, 2009.

XXIII. T. Mar, S. Zaunseder, J. P. Martnez, M. Llamedo, R. Poll, “Optimiza-tion of ECG classification by means of feature selection,” IEEE Trans. Biomed.

Eng., Vol.: 58, Issue: 8, pp. 2168-2177, 2011

XXIV. V. Phalguna Kumar, G. Amjad Khan “ QRS Detection Algorithm for Electrocardiogram Signal ” IJAERD, Vol.: 4, Issue 8, pp. 2348-6406, 2017 XXV. W. Jiang, S. G. Kong, “Block-based neural networks for personal-ized ECG

signal classification,” IEEE Trans. Neural Net., Vol. 18, Issue. 6, pp. 1750- 1761, Nov. 2007.