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PCA as an effective tool for the detection of R-peaks in an ECG signal processing

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Abstract

Visual inspection of R-peaks in Electrocardiogram (ECG) signal is avoided because of limited resolution and in the variation of parameters of the underlying subject (patient). Therefore, Principal Component Analysis has been used for R-peak detection without any pre-processing in noisy and different morphologies of the ECG signal with fractional Fourier transform which is using as a feature extraction technique. For validating this research work MIT-BIH Arrhythmia database is considered. The performance of developed algorithm is judged based on sensitivity, positive predictive value and accuracy. It has been revealed that the developed algorithm is capable to analyze ECG signals in both situations either normal or abnormal. It opens their huge applications in the time varying signals which can capture important clinical attributes in critical pathological situations.

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References

  • Abdollahzadeh B, Gharehchopogh FS (2021) A multi-objective optimization algorithm for feature selection problems. Eng Comput. https://doi.org/10.1007/s00366-021-01369-9

    Article  Google Scholar 

  • Acharya UR, Sankaranarayanan M, Nayak J, Xiang C, Tamura T (2008) Automatic identification of cardiac health using modeling techniques: a comparative study. Info Sci 178(23):4571–4582

    Article  Google Scholar 

  • Afonso VX, Tompkins WJ, Nguyen TQ, Luo S (1999) ECG beat detection using filter banks. IEEE Trans Biomed Eng 46(2):192–202

    Article  Google Scholar 

  • Alqudah AM, Qazan S, Al-Ebbini L et al (2021) ECG heartbeat arrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03247-0

    Article  Google Scholar 

  • Altan G, Kutlu Y, Yeniad M (2019) ECG based human identification using second order difference plots. Comp Meth Prog Biomed 170:81–93

    Article  Google Scholar 

  • Alwan A (2011) Global status report on noncommunicable diseases. World Health Organization, Geneva, Switzerland, pp 9–31

    Google Scholar 

  • Alyasseri ZAA, Khader AT, Betar MAA, Awadallah MA (2018) Hybridizing β-hill climbing with wavelet transform for denoising ECG signals. Info Scien 429:229–246

    Article  MathSciNet  Google Scholar 

  • Aqil M, Jbari A, Bourouhou A (2016) Adaptive ECG wavelet analysis for R-peaks detection. In: Proc. 2nd international conference on electrical and information technologies ICEIT, Tangiers, Morocco, pp 1–4

  • Arbateni K, Bennia A (2014) Sigmoidal radial basis function ANN for QRS complex detection. Neurocomputing 145(5):438–450

    Article  Google Scholar 

  • Ayar M, Isazadeh A, Gharehchopogh FS et al (2021) Chaotic-based divide-and-conquer feature selection method and its application in cardiac arrhythmia classification. J Supercomput. https://doi.org/10.1007/s11227-021-04108-5

    Article  Google Scholar 

  • Bahoura M, Hassani M, Hubin M (1997) DSP implementation of wavelet transform for real time ECG waveforms detection and heart rate analysis. J Comp Meth Prog Biom 52(1):35–44

    Article  Google Scholar 

  • Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR (2019) Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recogn Lett 122:23–30

    Article  Google Scholar 

  • Bouaziz F, Boutana D, Benidir M (2014) Multiresolution wavelet‐based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Process 8(7):774–782. https://doi.org/10.1049/iet-spr.2013.0391

    Article  Google Scholar 

  • Chawla MPS (2008) Segment classification of ECG data and construction of scatter plots using principal component analysis. J Mech Med Bio 8(3):421–458

    Article  Google Scholar 

  • Chen SW, Chen CH, Chan HL (2006a) A real-time QRS method based on moving-averaging incorporating with wavelet denoising. Comput Method Program Biomed 82(3):187–195

    Article  Google Scholar 

  • Chen SW, Chen CH, Chan HL (2006b) A real-time QRS method based on moving-averaging incorporating with wavelet denoising. Comp Met Prog Biom 82(3):187–195

    Article  Google Scholar 

  • Choi S, Adnane M, Lee GJ, Jang H, Jiang Z, Park HK (2010) Development of ECG beat segmentation method by combining lowpass filter and irregular R-R interval checkup strategy. Exp Syst Appl 37(7):5208–5218

    Article  Google Scholar 

  • Daamouche A, Hamami L, Alajlan N, Melgani F (2012) A wavelet optimization approach for ECG signal classification. Biom Signal Process Control 7(4):342–349

    Article  Google Scholar 

  • Das M, Ari S (2013) Analysis of ECG signal denoising method based on S-transform. IRBM 34(6):362–370

    Article  Google Scholar 

  • Dinh AN, Kumar DK, Pah ND, Burton P (2002) Wavelet for QRS detection. In: Proc. IEEE conference engineering in medicine and biology society, pp 7803–7811, pp1883–1887

  • Dohare AK, Kumar V, Kumar R (2013) An efficient new method for the detection of QRSin electrocardiogram. Comp Elect Eng 40(5):1–9

    Google Scholar 

  • Dokur Z, Ölmez T (2001) ECG beat classification by a novel hybrid neural network. Comput Method Program Biomed 66(3):167–181

    Article  Google Scholar 

  • Gharehchopogh FS, Maleki I, Dizaji ZA (2021) Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evol Intel. https://doi.org/10.1007/s12065-021-00590-1

    Article  Google Scholar 

  • Gupta V, Mittal M (2019) R-peak detection in ECG signal using Yule-Walker and principal component analysis. IETE J Res 65:1–15

    Google Scholar 

  • Gupta V, Mittal M (2019a) QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG databases. J Inst Eng India Ser B 100:489–497. https://doi.org/10.1007/s40031-019-00398-9

    Article  Google Scholar 

  • Gupta V, Mittal M (2019b) A comparison of ECG signal pre-processing using FrFT, FrWT and IPCA for improved analysis. IRBM 40(3):145–156. https://doi.org/10.1016/j.irbm.2019.04.003

    Article  Google Scholar 

  • Gupta V, Mittal M (2020a) A novel method of cardiac arrhythmia detection in electrocardiogram signal. Int J Med Eng Inform 12(5):489. https://doi.org/10.1504/IJMEI.2020.109943

    Article  Google Scholar 

  • Gupta V, Mittal M (2020b) Efficient R-peak detection in electrocardiogram signal based on features extracted using Hilbert transform and Burg method. J Inst Eng India Ser B 101:23–34

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2019) R-peak detection using chaos analysis in standard and real time ECG databases. IRBM 40(6):341–354. https://doi.org/10.1016/j.irbm.2019.10.001

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2020a) R-peak detection based chaos analysis of ECG signal. Analog Integr Circuits Signal Process 102(3):479–490. https://doi.org/10.1007/s10470-019-01556-1

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V (2020b) Chaos theory: an emerging tool for arrhythmia detection. Sens Imaging 21:10. https://doi.org/10.1007/s11220-020-0272-9

    Article  Google Scholar 

  • Gupta V, Mittal M, Mittal V et al (2021) ECG signal analysis using CWT, spectrogram and autoregressive technique. Iran J Comput Sci. https://doi.org/10.1007/s42044-021-00080-8

    Article  Google Scholar 

  • Helen Mary MC, Singh D, Deepak KK (2019) Detecting changes in cardiovascular interaction during postural stress using directed coherence. Signal Image Video Process 13(8):1521–1528. https://doi.org/10.1007/s11760-019-01495-4

    Article  Google Scholar 

  • Huang JS, Chen BQ, Zeng NY et al (2020) Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02110-y

    Article  Google Scholar 

  • Kaur A, Agarwal A, Agarwal R, Kumar S (2018) A novel approach to ECG R-peak detection. Arab J Sci Eng 44(8):6679–6691. https://doi.org/10.1007/s13369-018-3557-8

    Article  Google Scholar 

  • Kaya Y, Pehlivan H (2015a) Classification of premature ventricular contraction in ECG. Int J Adv Com Sci Appl 6:34–40

    Google Scholar 

  • Kaya Y, Pehlivan H, Tenekeci ME (2017) Effective ECG beat classification using higher order statistic features and genetic feature selection. J Biomed Res 28:7594–7603

    Google Scholar 

  • Kaya Y, Pehlivan H (2015b) Feature selection using genetic algorithms for premature ventricular contraction classification. In: Proc. 9th international conf. on IEEE electrical and electronics engineering, Turkey, pp 1229–1232

  • Kim J, Shin HS, Shin K, Lee M (2009) Robust algorithm for arrhythmia classification in ECG using extreme learning machine. BioMed Eng OnLine 8(31):1–12

    Google Scholar 

  • Kublanov V, Dolganov A (2019) Development of a decision support system for neuro-electrostimulation: diagnosing disorders of the cardiovascular system and evaluation of the treatment efficiency. J App Soft Comp 77:329–343

    Article  Google Scholar 

  • Leelavathy J, Selva Brunda S (2020) A cloud-based secured framework for smart medical diagnosis: a survey. Int J Med Eng Inform 12(6):581. https://doi.org/10.1504/IJMEI.2020.111031

    Article  Google Scholar 

  • Leong CI, Mak PI, Lam CP, Dong C, Vai MI, Mak PU, Pun HS, Wan F, Martins RP (2012) A 0.83μW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35 m CMOS. IEEE Trans Biomed Circuits Syst 6(6):586–595

    Article  Google Scholar 

  • Li H, Wang X, Chen L, Li E (2014) Denoising and R-peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuit Syst Signal Process 33(4):1261–1276

    Article  Google Scholar 

  • Lin PY (1999) The fractional Fourier transform and its applications. National Taiwan University, Taiwan

    Google Scholar 

  • Lin Q, Ran T, Siyong Z, Yue W (2004) Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform. Sci China Ser F Info Sci 47(2):184–198

    Article  MathSciNet  MATH  Google Scholar 

  • Lin C, Yeh H-M, Lo M-T, Yeh C-H, Wang C-Y, Shi W, Bess Ma F, Serafico C-HW, Juan C-H, Young H-WV, Lin Y-J (2019) Robust fetal heart beat detection via R-peak intervals distribution. IEEE Trans Biomed Eng 66(12):3310–3319. https://doi.org/10.1109/TBME.2019.2904014

    Article  Google Scholar 

  • Liu X, Yang J, Zhu X, Zhou S, Wang H, Zhang H (2014) A novel R-peak detection method combining energy and wavelet transform in electrocardiogram signal. J Biom Eng 26:1–9

    Google Scholar 

  • Lu W, Jiang J, Ma L et al (2021) An arrhythmia classification algorithm using C-LSTM in physiological parameters monitoring system under internet of health things environment. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03456-7

    Article  Google Scholar 

  • Luz EJS, Schwartz WR, Cháveza GC, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comp Meth Prog Biomed 127:144–164

    Article  Google Scholar 

  • Manikandan MS, Soman KP (2012) A novel method for detecting R-peaks in the electrocardiogram (ECG) signal. Biom Signal Process Control 7(2):118–128

    Article  Google Scholar 

  • Manish Sharma R, San Tan U, Acharya R (2018) A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 102:341–356. https://doi.org/10.1016/j.compbiomed.2018.07.005 (In press)

    Article  Google Scholar 

  • Mehta SS, Lingayat NS (2008) SVM-based algorithm for recognition of QRS complexes in electrocardiogram. IRBM 29(5):310–317. https://doi.org/10.1016/j.rbmret.2008.03.006

    Article  Google Scholar 

  • Mehta SS, Shete DA, Lingayat NS, Chouhan VS (2010) K-means algorithm for the detection and delineation of QRS-complexes in electrocardiogram. IRBM 31:48–54

    Article  Google Scholar 

  • Meireles AJMD (2011) ECG denoising based on adaptive signal processing technique, M.Tech dissertation, dept. electronics and computer science, Porto, Portugal

  • Mendlovic D, Ozaktas HM (1993) Fractional fourier transforms and their optical implementation. J Opt Soc Am A 10(9):1875–1881

    Article  Google Scholar 

  • Mohammadzadeh H, Gharehchopogh FS (2021a) A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: case study email spam detection. Comput Intell 37:176–209. https://doi.org/10.1111/coin.12397

    Article  MathSciNet  Google Scholar 

  • Mohmmadzadeh H, Gharehchopogh FS (2021b) An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems. J Supercomput 77:9102–9144. https://doi.org/10.1007/s11227-021-03626-6

    Article  Google Scholar 

  • Nallathambi G, Príncipe JC (2014) Integrate and fire pulse train automaton for QRS detection. IEEE Trans Biomed Eng 61(2):317–326

    Article  Google Scholar 

  • Nayak C, Saha SK, Kar R, Mandal D (2018) An efficient QRS complex detection using optimally designed digital differentiator. Circuit Sys Signal Process 38(5):716–749

    Google Scholar 

  • Ning X, Selesnick IW (2013) ECG enhancement and QRS detection based on sparse derivatives. Biom Signal Process Control 8(6):713–723

    Article  Google Scholar 

  • Nygåards ME, Sörnmo L (1983) Delineation of the QRS complex using the envelope of the ECG. J Med Biol Eng Com 21(5):538–547

    Article  Google Scholar 

  • Ozaktas HM, Zalevsky Z, Kutay MA (2001) The fractional Fourier transform with applications in optics and signal processing. Wiley, New York

    Google Scholar 

  • Padmavathi K, Kalva SR (2015) Improved bat algorithm for the detection of myocardial infarction. Springerplus 4(1):666

    Article  Google Scholar 

  • Padmavathi K, Ramakrishna KS (2015a) Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence. Int J Sys Control Commun 6(4):292. https://doi.org/10.1504/IJSCC.2015.072519

    Article  Google Scholar 

  • Padmavathi K, Ramakrishna KS (2015b) Detection of atrial fibrillation using autoregressive modelling. Int J Electr Comput Eng 5(1):64–70

    Google Scholar 

  • Padmavathi K, Ramakrishna KS (2016) ECG based heart arrhythmia detection using wavelet coherence and bat algorithm. Sens Imaging 17(12):1–16

    Google Scholar 

  • Padmavathi K, Abraham A, Meenakshi K (2019) Heart disease detection using hybrid of bacterial foraging and particle swarm optimization. Evol Syst. https://doi.org/10.1007/s12530-019-09312-6

    Article  Google Scholar 

  • Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32:230–236

    Article  Google Scholar 

  • Pandit D, Zhang L, Liu C, Chattopadhyay S, Aslam N, Lim CP (2017) A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput Methods Prog Biomed 144:61–75

    Article  Google Scholar 

  • Pham TH, Sree V, Mapes J et al (2021) A novel machine learning framework for automated detection of arrhythmias in ECG segments. J Ambient Intell Hum Comput 12:10145–10162. https://doi.org/10.1007/s12652-020-02779-1

    Article  Google Scholar 

  • Phukpattaranont P (2015) QRS detection algorithm based on the quadratic filter. Exp Sys Appl 42(11):4867–4877

    Article  Google Scholar 

  • Pipberger HV, McManus CD, Pipberger HA (1990) Methods of ECG interpretation in the AVA program. Methods Inf Med 29(4):337–340

    Article  Google Scholar 

  • Plesnik E, Malgina O, Tasic JF, Zajc M (2012) Detection of the electrocardiogram fiducial points in the phase space using the Euclidian distance measure. Med Eng Phys 34(4):524–529

    Article  Google Scholar 

  • Rai HM, Trivedi A, Chatterjee K, Shukla S (2014) R-peak detection using Daubechies wavelet and ECG signal classification using radial basis function neural network. J Inst Eng India Ser B 95:63–71

    Article  Google Scholar 

  • Rajendiran N, Gurunathan T, Palanivel M (2020) Wavelet packet transform-based medical image multiple watermarking with independent component analysis extraction. Int J Med Eng Inform 12(4):322–335. https://doi.org/10.1504/IJMEI.2020.108236

    Article  Google Scholar 

  • Rakshit M, Das S (2017) An efficient wavelet-based automated R-Peaks detection method using Hilbert transform. Biocybern Biomed Eng 37(3):566–577

    Article  Google Scholar 

  • Ramachandran SK, Manikandan P (2021) An efficient ALO-based ensemble classification algorithm for medical big data processing. Int J Med Eng Inform 13(1):54–63. https://doi.org/10.1504/IJMEI.2021.111864

    Article  Google Scholar 

  • Rekik S, Ellouze N (2017) Enhanced and Optimal algorithm for QRS detection. IRBM 38(1):56–61

    Article  Google Scholar 

  • Rodríguez R, Mexicano A, Bila J, Cervantes S, Ponc R (2015) Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis. J Appl Res Tech 13(2):261–269

    Article  Google Scholar 

  • Sahoo S, Biswal P, Das T, Sabut S (2016) De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding. Proc Tech 25:68–75

    Article  Google Scholar 

  • Sahoo S, Kanungo B, Behera S, Sabut S (2017) Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 108:55–66

    Article  Google Scholar 

  • Saini I, Singh D, Khosla A (2013) QRS detection using k-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res 4(4):331–344

    Article  Google Scholar 

  • Saravanan S, Karthigaivel R, Magudeeswaran V (2021) A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model. J Ambient Intell Hum Comput 12:4727–4735. https://doi.org/10.1007/s12652-020-01875-6

    Article  Google Scholar 

  • Sejdic E, Djurovic I, Jiang J, Stankovic LJ (2009) Time–frequency based feature extraction and classification: considering energy concentration as a feature using Stockwell transform and related approaches. VDM Verlag Publishing, Germany

    Google Scholar 

  • Sharma T, Sharma KK (2016) QRS complex detection in ECG signals using the synchrosqueezed wavelet transform. IETE J Res 62(6):885–892

    Article  Google Scholar 

  • Sharma T, Sharma KK (2017) QRS complex detection in ECG signals using locally adaptive weighted total variation denoising. Comp Biol Med 87:187–199

    Article  Google Scholar 

  • Sharma LD, Sunkaria RK (2016) A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 87:194–204

    Article  Google Scholar 

  • Sharma A, Patidar S, Upadhyaya A, Acharya UR (2019) Accurate tunable-Q wavelet transform based method for QRS complex detection. Comp Electr Eng 75:101–111

    Article  Google Scholar 

  • Sheetal A, Singh H, Kaur A (2019) QRS detection of ECG signal using hybrid derivative and MaMeMi filter by effectively eliminating the baseline wander. Analog Integr Circuit Signal Process 98(1):1–9

    Article  Google Scholar 

  • Singh U, Singh SN (2017) Application of fractional fourier transform for classification of power quality disturbances. IET Sci Meas Tech 11(1):67–76

    Article  Google Scholar 

  • Sridhar C, Lih OS, Jahmunah V et al (2021) Accurate detection of myocardial infarction using non linear features with ECG signals. J Ambient Intell Hum Comput 12:3227–3244. https://doi.org/10.1007/s12652-020-02536-4

    Article  Google Scholar 

  • Sunkaria RK, Saxena SC, Kumar V, Singhal AM (2010) Wavelet based R-peak detection for heart rate variability studies. J Med Eng Tech 34(2):108–115

    Article  Google Scholar 

  • Thakor NV, Zhu Y-S (1991) Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng 38(8):785–794. https://doi.org/10.1109/10.83591

    Article  Google Scholar 

  • Trahanias P (1993) An approach to QRS complex detection using mathematical morphology. IEEE Trans Biomed Eng 40(2):201–205

    Article  Google Scholar 

  • Verma AK, Saini I, Saini BS (2018) Alexander fractional differential window filter for ECG denoising. Austra Phys Eng Sci Med 41(2):519–539

    Article  Google Scholar 

  • Vimala K, Kalaivani V (2014) Stress causing arrhythmia detection from ECG signal using HMM. Inter J Innov Res Comp Commun Eng 2(5):1–7

    Google Scholar 

  • Wang YC (2018) Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network. J Ambient Intell Hum CoMput. https://doi.org/10.1007/s12652-018-0997-7

    Article  Google Scholar 

  • Yakut O, Bolat ED (2018) An improved QRS complex detection method having low computational load. Biom Signal Process Control 42:230–241

    Article  Google Scholar 

  • Yazdani S, Vesin JM (2016) Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology. Dig Sig Proc 56:100–109

    Article  MathSciNet  Google Scholar 

  • Yochum M, Renaud C, Jacquir S (2016) Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT. Biomed Signal Process Control 25:46–52

    Article  Google Scholar 

  • Yu Q, Liu A, Liu T, Mao Y, Chen W, Liu H (2019) ECG R-wave peaks marking with simultaneously recorded continuous blood pressure. PLoS ONE 14(3):e0214443. https://doi.org/10.1371/journal.pone.0214443

    Article  Google Scholar 

  • Zayed AI (1996) On the relationship between the Fourier and fractional Fourier transforms. IEEE Signal Process Lett 3(12):310–311

    Article  Google Scholar 

  • Zhang C, Li X, Zhang M (2010) A novel ECG signal denoising method based on Hilbert–Huang transform. In: Proc. IEEE conference on computer and communication technologies in agriculture engineering (CCTAE), China, pp 284–287

  • Zidelmal Z, Amirou A, Adnane M, Belouchrani A (2012) QRS detection based on wavelet coefficients. Comp Meth Prog Biomed 107(3):490–496

    Article  Google Scholar 

  • Zidelmal Z, Amiroua A, Ould-Abdeslamb D, Moukademb A, Dieterlen A (2014) QRS detection using S-transform and Shannon energy. Comput Meth Prog Biomed 116(1):1–9

    Article  Google Scholar 

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  1. Department of Electrical and Electronics Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, 201206, India

    Varun Gupta, Nitin Kumar Saxena & Sourav Diwania

  2. Department of Electronics and Communication Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, 201206, India

    Abhas Kanungo & Parvin Kumar

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  1. Varun Gupta
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All simulations and methodology are performed by VG. Literature survey is done by NKS, AK, PK and SD.

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Gupta, V., Saxena, N.K., Kanungo, A. et al. PCA as an effective tool for the detection of R-peaks in an ECG signal processing. Int J Syst Assur Eng Manag 13, 2391–2403 (2022). https://doi.org/10.1007/s13198-022-01650-0

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