ECG signal analysis using CWT, spectrogram and autoregressive technique

Abstract

The cardiovascular system is a combination of the heart, blood and blood vessels. Cardiovascular diseases (CVD) are a key factor behind casualties worldwide among both women and men. About 9.4 million deaths occur due to high Blood Pressure (BP) only, out of which 51% deaths are due to strokes and 45% deaths are due to coronary heart diseases. The Electrocardiogram (ECG) represents the heart health condition of the subject, (patient) since it is acquired through electrical conduction, which appears in terms of P-QRS-T waves. But analysis of these waves is very tedious due to the existence of different noises/artifacts. Computer Aided Diagnosis (CAD) system is required in practical medical scenario for better and automated ECG signal analysis and to compensate for human errors. In general, implementation of a CAD system for ECG signal analysis requires; preprocessing, feature extraction and classification. In the existing literature, some authors have used time domain techniques which yield good performance for cleaned ECG signals i.e., without noise/artifact. Some authors have used frequency domain techniques later, but they suffer from the problem of spectral leakage making them unsuitable for real time/pathological datasets. The existing techniques from both these domains are not able to effectively analyze nonlinear behavior of ECG signals. These limitations have motivated this work where Continuous Wavelet Transform (CWT), Spectrogram and Autoregressive (AR) technique are used collectively for interpreting nonlinear and non-stationary features of the ECG signals. In this paper, both Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (MB Ar DB) and Real-time database (RT DB) have been used. Performance of the proposed method is compared with that of the previous studies on the basis of sensitivity (SE) and detection rate (D.R). The proposed technique yields SE of 99.90%, D.R of 99.81% & SE of 99.77%, D.R of 99.87% for MB Ar DB and RT DB, respectively. Therefore, the proposed technique showcases the possibility of an encouraging diagnostic tool for further improving the present situation of health informatics in cardiology labs/hospitals.

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Abbreviations

CVD:

Cardiovascular Disease

STFT:

Short-time Fourier Transform

CWT:

Continuous Wavelet Transform

MB Ar DB:

Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database

RT DB:

Real-time Database

CAD:

Computer Aided Diagnosis

ECG:

Electrocardiogram

SGDF:

Savitzky–Golay Digital Filtering

KNN:

K-Nearest Neighbor

AR:

Autoregressive

TFA:

Time–Frequency Analysis

EDM:

Euclidean Distance Metric

PSD:

Power Spectral Density

SE:

Sensitivity

DR:

Detection Rate

TP:

True Positive

FP:

False Positive

FN:

False Negative

WHO:

World Health Organization

SNR:

Signal to Noise Ratio

RMSE:

Root Mean Square Error

PRD:

Percent Root Mean Square Difference

ACC:

Accuracy

SPE:

Specificity

References

  1. 1.

    Kumar, M., et al.: An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals. Expert Syst Appl (2016). https://doi.org/10.1016/j.eswa.2016.06.038

    Article  Google Scholar 

  2. 2.

    Martis, R.J., et al.: Current methods in electrocardiogram characterization. Comp Biol Med 48, 133–149 (2014)

    Article  Google Scholar 

  3. 3.

    Gupta, V., et al.: Performance evaluation of various pre-processing techniques for R-peak detection in ECG signal. IETE J Res (2020). https://doi.org/10.1080/03772063.2020.1756473

    Article  Google Scholar 

  4. 4.

    Sharma, L.D., Sunkaria, R.K.: Myocardial infarction detection and localization using optimal features based lead specific approach. IRBM 41, 58–70 (2020)

    Article  Google Scholar 

  5. 5.

    Gupta, V., et al.: R-peak detection using chaos analysis in standard and real time ECG databases. IRBM 40(6), 341–354 (2019)

    Article  Google Scholar 

  6. 6.

    Wong, N.D.: Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol 11, 276–289 (2014)

    Article  Google Scholar 

  7. 7.

    Sahoo, S., et al.: “Machine learning approach to detect cardiac arrhythmias in ecgsignals: a survey. IRBM (2019). https://doi.org/10.1016/j.irbm.2019.12.001

    Article  Google Scholar 

  8. 8.

    Kora, P., Kalva, S.R.: Improved Bat algorithm for the detection of myocardial infarction. SpringerPlus 4, 666 (2015). https://doi.org/10.1186/s40064-015-1379-7

    Article  Google Scholar 

  9. 9.

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

    Google Scholar 

  10. 10.

    Gupta, V., Mittal, M.: Efficient R-peak detection in electrocardiogram signal based on features extracted using hilbert transform and burg method. J Inst Eng India Ser B (2020). https://doi.org/10.1007/s40031-020-00423-2

    Article  Google Scholar 

  11. 11.

    M.C. Helen, M. et al., “Changes in scale-invariance property of electrocardiogram as a predictor of hypertension,” International Journal of Medical Engineering and Informatics (IJMEI), Vol.12 No.3, pp.228 – 236, 2020.

  12. 12.

    Ripoll, V.J.R., et al.: Assessment of electrocardiograms with pretraining and shallow networks. J Comput Cardiol 4, 1061–1064 (2014)

    Google Scholar 

  13. 13.

    Chandra, S., et al.: A comparative analysis of performance of several wavelet based ECG data compression methodologies. IRBM (2020). https://doi.org/10.1016/j.irbm.2020.05.004

    Article  Google Scholar 

  14. 14.

    Mary, M.C., et al.: Assessment of scale invariance changes in heart rate signal during postural shift. IETE J Res 1, 1604172 (2019)

    Google Scholar 

  15. 15.

    Gupta, V., Mittal, M.: Dimension reduction and classification in ECG signal interpretation using FA & PCA: A Comparison. Jangjeon Mathemat Soc 21(4), 765–777 (2018)

    MathSciNet  MATH  Google Scholar 

  16. 16.

    Aouinet, A., Adnane, C.: Electrocardiogram denoised signal by discrete wavelet transform and continuous wavelet transform. Akramaouinet and cherifadnane. J Signal Proc Internat J (SPIJ) 8, 1–9 (2014)

    Google Scholar 

  17. 17.

    Gupta V, and Mittal M., “Respiratory Signal Analysis using PCA, FFT and ARTFA,” 2016 International Conference on Electrical Power and Energy Systems (ICEPES), Maulana Azad National Institute of Technology, Bhopal, India, pp. 221–225 (2016).

  18. 18.

    Gupta, V., Mittal, M.: ECG (Electrocardiogram) signals interpretation using chaostheory. J Adv Res Dyn Cont Sys (JARDCS) 10(2), 2392–2397 (2018)

    Google Scholar 

  19. 19.

    Rahhal, M.M.A., et al.: Deep learning approach for active classification of electrocardiogram signals. Internat J Inform Sci 345, 340–354 (2016)

    Article  Google Scholar 

  20. 20.

    Zhang, X.S., et al.: New approach to studies on ECG dynamics: extraction and analyses of QRS complex irregularity time series. J Med Biol Eng Comput 5, 467–473 (1997)

    Article  Google Scholar 

  21. 21.

    Subramanian, B., Ramasamy, A.: Investigation on the compression of electrocardiogram signals using dual tree complex wavelet transform. IETE J Res (2017). https://doi.org/10.1080/03772063.2016.1275988

    Article  Google Scholar 

  22. 22.

    Zhang, J., et al.: ECG signals denoising method based on improved wavelet threshold algorithm. IEEE Internat Conf 1, 1779–1784 (2016)

    Google Scholar 

  23. 23.

    Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. J Expert Syst Appl 34, 2841–2846 (2008)

    Article  Google Scholar 

  24. 24.

    Krummen, D.E.: Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study. J Cardiov Electrophys 21, 11 (2010)

    Article  Google Scholar 

  25. 25.

    Annavarapu, A., Kora, P.: ECG-based atrial fibrillation detection using different orderings of conjugate symmetric-complex hadamard transform. Internat J Cardiov Acad 12, 151–154 (2016)

    Article  Google Scholar 

  26. 26.

    Aqil, M., et al.: ECG-waves: analysis and detection by continuous wavelet transform. J Telecommun J Electronic Computer Eng 9, 45–52 (2010)

    Google Scholar 

  27. 27.

    Lin, C.: Heart Rate Variability Analysis using Windows and Wavelet Transform. Internat J Cardiol 109(1), 101–107 (2006)

    Article  Google Scholar 

  28. 28.

    Chen, S., et al.: Heartbeat classification using projected and dynamic features of ECG Signal. Biomed Signal Process Control 31, 165–173 (2017)

    Article  Google Scholar 

  29. 29.

    Gupta, V., Mittal, M.: Principal component analysis and factor analysis as an enhanced tool of pattern recognition. Int J Elec Electr Eng Telecoms 1(2), 73–78 (2015)

    Google Scholar 

  30. 30.

    Kumar, M., et al.: Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocyber Bi Med Eng. (2018). https://doi.org/10.1016/j.bbe.2018.04.004

    Article  Google Scholar 

  31. 31.

    Alshebly, Y.S., Nafea, M.: Isolation of fetal ECG signals from abdominal ECG using wavelet analysis. IRBM (2019). https://doi.org/10.1016/j.irbm.2019.12.002

    Article  Google Scholar 

  32. 32.

    Chazal, P.D., et al.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51, 1196–1206 (2004)

    Article  Google Scholar 

  33. 33.

    Jonnagaddala, J., et al.: Coronary artery disease risk assessment from unstructured electronic health records using text mining. J Biomed Inform 58, 203–210 (2015)

    Article  Google Scholar 

  34. 34.

    Martínez, J.P., et al.: Wavelet based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 51, 570–581 (2004)

    Article  Google Scholar 

  35. 35.

    Robert, K., Colleen, E.C.: Basis and treatment of cardiac arrhythmias, 1st edn. Springer-Verlag, New York (2006)

    Google Scholar 

  36. 36.

    Mokeddem, F., et al.: Study of murmurs and their impact on the heart variability. Internat J Med Eng Inform (IJMEI) 12(3), 291–301 (2020)

    Google Scholar 

  37. 37.

    Webster, J.G.: Medical Instrumentation: application and design, 3rd edn. JohnWiley & Sons, London (2008)

    Google Scholar 

  38. 38.

    Mortezaee, M., et al.: An improved SSA-based technique for EMG removal from ECG. IRBM 40, 62–68 (2019)

    Article  Google Scholar 

  39. 39.

    Kumar, M.: Identifying heart-brain interactions during internally and externally operative attention using conditional entropy. Biomed Signal Process Control 57, 101826 (2020). https://doi.org/10.1016/j.bspc.2019.101826

    Article  Google Scholar 

  40. 40.

    Acharya, U.R., et al.: A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89, 389–396 (2017)

    Article  Google Scholar 

  41. 41.

    Gupta, V., et al.: R-peak detection based chaos analysis of ECG signal. Analog Integr Circ Sig Process 102, 479–490 (2020)

    Article  Google Scholar 

  42. 42.

    Xingyuan, W., Juan, M.: Wavelet-based hybrid ECG compression technique. Analog Integr Circ Sig Process 59(3), 301–308 (2009)

    Article  Google Scholar 

  43. 43.

    Gandhi, B., Raghava, N.S.: Fabrication techniques for carbon nanotubes based ECG electrodes: a review. IETE J Res (2020). https://doi.org/10.1080/03772063.2020.1768909

    Article  Google Scholar 

  44. 44.

    Daamouche, A., et al.: A wavelet optimization approach for ECG signal classification. Biomed Signal Process Control 7, 342–349 (2012)

    Article  Google Scholar 

  45. 45.

    Rahman, A.: A statistical designing approach to MATLAB based functions for the ECG signal preprocessing. Iran J Computer Sci (2019). https://doi.org/10.1007/s42044-019-00035-0

    Article  Google Scholar 

  46. 46.

    Chakraborty, M.: Quantitative assessment of arrhythmia using non-linear approach: a non-invasive prognostic tool. J Inst Eng India Ser B (2017). https://doi.org/10.1007/s40031-017-0307-3

    Article  Google Scholar 

  47. 47.

    Gupta, V., Mittal, M., Mittal, V.: Chaos theory: an emerging tool for arrhythmia detection. Sens Imaging. 21(10), 1–22 (2020). https://doi.org/10.1007/s11220-020-0272-9

    Article  Google Scholar 

  48. 48.

    Christov, I.I.: Real time electrocardiogram QRS detection using combine adaptive threshold. Biomed Eng Online 3, 28 (2004). https://doi.org/10.1186/1475-925X-3-28

    Article  Google Scholar 

  49. 49.

    Hamilton, P.S., Tompkin, W.J.: Quantitative investigation of QRS detection rules using MIT/BIH Arrhythmia database. IEEE Trans BME 33, 1157–1165 (1986)

    Article  Google Scholar 

  50. 50.

    Rao, K.D.: DWT based detection of r-peaks and data compression of ECG Signals. IETE J Res 43(5), 345–349 (1997)

    Article  Google Scholar 

  51. 51.

    Sahoo, S., et al.: Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement 108, 55–66 (2017)

    Article  Google Scholar 

  52. 52.

    Mittal, M.: A computationally efficient piecewise constant solution for system transfer function inversion using orthogonal functions. J Adv Res Dynam Control Syst 9, 2398–2404 (2018)

    Google Scholar 

  53. 53.

    Mittal, V., Mittal, M.: Haar wavelet based numerical approach for computing system response to arbitrary excitations. J Adv Res Dynamical Control Syst 2, 2433–2439 (2018)

    Google Scholar 

  54. 54.

    Valli, T., Mittal, M.: Analysis of Fractional Systems using Haar Wavelet. Int J Innov Technol Exploring Eng (IJITEE) 8(9), 455–459 (2019)

    Google Scholar 

  55. 55.

    Rao, H., Rekha, S.: A 0.8-V, 5.51-dB DR, 100 Hz low-pass filter with low-power PTAT for bio-medical applications”. IETE J Res (2019). https://doi.org/10.1080/03772063.2019.1682074

    Article  Google Scholar 

  56. 56.

    Kora, P.: ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed (2017). https://doi.org/10.1016/j.cmpb.2017.09.015

    Article  Google Scholar 

  57. 57.

    He, R., et al.: A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization. EURASIP J Adv Sig Proc 82, 4 (2017). https://doi.org/10.1186/s13634-017-0519-3

    Article  Google Scholar 

  58. 58.

    Bilas, R., et al.: An improved online paradigm for screening of diabetic patients using RR-interval signals. J Mech Med Biol 16(1), 1640003 (2016)

    Article  Google Scholar 

  59. 59.

    Jain, S., et al.: QRS detection using adaptive filters: a comparative study. ISA Trans 66, 362–375 (2017)

    Article  Google Scholar 

  60. 60.

    Gupta V. and Mittal M., “A novel method of cardiac arrhythmia detection in electrocardiogram signal,” IJMEI, 2019 (in press).

  61. 61.

    Jothi, S.H., Prabha, K.H.: Fetal electrocardiogram extraction using adaptive neuro-fuzzy inference systems and undecimated wavelet transform. IETE J Res 58(6), 469–475 (2012)

    Article  Google Scholar 

  62. 62.

    Acharya, U.R., et al.: Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comp Biol Med 43, 1523–1529 (2013)

    Article  Google Scholar 

  63. 63.

    Das, M.K., Ari, S.: Analysis of ECG signal denoising method based on S-transform. IRBM (2013). https://doi.org/10.1016/j.irbm.2013.07.012

    Article  Google Scholar 

  64. 64.

    Lin, C.C., et al.: A novel wavelet-based algorithm for detection of QRS complex. Appl Sci 12, 4 (2019)

    Google Scholar 

  65. 65.

    Addison, P.S.: Wavelet transforms and the ECG: a review. PhysiolMeas 26, 155–199 (2005)

    Google Scholar 

  66. 66.

    Ghaffari, A., et al.: A new mathematical based QRS detector using continuous wavelet transform. Comput Electr Eng 34, 81–91 (2008)

    MATH  Article  Google Scholar 

  67. 67.

    Free hospital cartoons. Available:Error! Hyperlink reference not valid. on 17 Dec. 2019.

  68. 68.

    Computer analysis free technology items. Available: www.all-free-download.com. (Accessed on 28 Oct 2019).

  69. 69.

    MP35 Biopac system-www.biopac.com.

  70. 70.

    Rajesh, K., Dhuli, R.: Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. J Comput Biol Med 87, 271–284 (2017)

    Article  Google Scholar 

  71. 71.

    Rangayyan, R.M.: Biomedical signal analysis: a case-study approach. Wiley-Interscience, New York (2001)

    Google Scholar 

  72. 72.

    Gupta, V., Mittal, M.: R-Peak Detection in ECG Signal Using Yule-Walker and Principal Component Analysis. IETE J. Res. (2019). https://doi.org/10.1080/03772063.2019.1575292

    Article  Google Scholar 

  73. 73.

    Gupta, V., Mittal, M.: A novel method of cardiac arrhythmia detection in electrocardiogram signal. Internat J Med Eng Informs (IJMEI) 12, 18 (2020)

    Google Scholar 

  74. 74.

    Luz, E.J.S., et al.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comp Met Prog Biom 127, 144–164 (2016)

    Article  Google Scholar 

  75. 75.

    Physionet database/MIT-BIH Arrhythmia database/ (Accessed on Nov 22, 2017).

  76. 76.

    Giorgio, A., et al.: Improving ECG signal denoising using wavelet transform for the prediction of malignant arrhythmias. Internat J Med Eng Inform 12(2), 135–150 (2020)

    Google Scholar 

  77. 77.

    Mostafi, M., et al.: Discrimination of signals phonocardiograms by using SNR report. Internat J Med Eng Inform (IJMEI) 11(4), 386–403 (2019)

    Google Scholar 

  78. 78.

    Haque, Z.U., et al.: Analysis of ECG Signal Processing and Filtering Algorithms. Int J Adv Comp Sci Appl 10, 3 (2019). https://doi.org/10.14569/IJACSA.2019.0100370

    Article  Google Scholar 

  79. 79.

    Li, H., et al.: Novel ECG signal classification based on KICA nonlinear feature extraction. J Circuits Syst Signal Process (2004). https://doi.org/10.1007/s00034-015-0108-3

    Article  Google Scholar 

  80. 80.

    Gupta, V., et al.: Principal component and independent component calculation of ECG signal in different posture. AIP Conf Proc 1414, 102–108 (2011)

    Article  Google Scholar 

  81. 81.

    Nikan S. et al., “Pattern Recognition Application in ECG Arrhythmia Classification,” in Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), pp. 48–56, 2017.

  82. 82.

    Priyadarshini, S.H., et al.: Processing of wrist pulse signals using linear and nonlinear techniques – a review. Internat J Eng Sci Computing 4, 7477–7482 (2016)

    Google Scholar 

  83. 83.

    Bromba, M.U.A., Ziegler, H.: Application hint for Savitsky-golay digital smoothing filters. Anal Chem 53, 1583–1586 (1981)

    Article  Google Scholar 

  84. 84.

    Jha, C., Kolekar, M.H.: Empirical mode decomposition and wavelet transform based ECG data compression. Scheme (2020). https://doi.org/10.1016/j.irbm.2020.05.008

    Article  Google Scholar 

  85. 85.

    Guiñón JL. “Moving Average and Savitzki-Golay Smoothing Filters Using Mathcad,” International Conference on Engineering Education – ICEE, Coimbra, Portugal, pp.1–4, 2007.

  86. 86.

    http://www.robots.ox.ac.uk/~gari/teaching/cdt/A3/8_A3_BSP_Time_Freq.pdf.

  87. 87.

    Gupta, V., et al.: Auto-regressive time frequency analysis (ARTFA) of electrocardiogram (ECG) signal. Internat J Appl Eng Res 13(6), 133–138 (2019)

    Google Scholar 

  88. 88.

    https://content.nexosis.com/blog/what-are-autoregressive-models.

  89. 89.

    Evaristo, R.M., et al.: Mathematical model with autoregressive process for electrocardiogram signals. J Commun Nonlinear SciNumerSimulat 57, 415–421 (2018)

    MathSciNet  MATH  Article  Google Scholar 

  90. 90.

    https://dsp.stackexchange.com/questions/9518/what-are-autoregressive-coefficients.

  91. 91.

    Emresoy, M.K., Jaroudi, A.E.: Signal Proc 64, 157–165 (1998)

    Article  Google Scholar 

  92. 92.

    https://en.wikipedia.org/wiki/Short-time_Fourier_transform.

  93. 93.

    Rohini, R., et al.: A new paradigm for plotting spectrogram. J Inform Syst Commun 3, 158–161 (2012)

    Google Scholar 

  94. 94.

    Vlad S, et al., “Efficient ECG Signal Parameters Extraction using Multiresolution Analysis,” International Conference on Advancements of Medicine and Health Care through Technology, Romania, 2009.

  95. 95.

    Gupta, V., Mittal, M.: KNN and PCA classifier with autoregressive modelling during different ECG signal interpretation. Procedia Computer Sci Elsevier 125, 18–24 (2018)

    Article  Google Scholar 

  96. 96.

    Acharya, U.R., et al.: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. J Inform Sci 377, 17–29 (2017)

    Article  Google Scholar 

  97. 97.

    Saini, I., et al.: QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res 4(4), 331–344 (2013)

    Article  Google Scholar 

  98. 98.

    Confusion matrix.https://towardsdatascience.com/taking-the-confusion-out-of-confusion-matrices-c1ce054b3d3e.

  99. 99.

    Acharya, U.R., et al.: Automatic identification of cardiac health using modeling techniques: a comparative study. J Inform Sci 178, 4571–4582 (2008)

    Article  Google Scholar 

  100. 100.

    Bogunovic N, Jovic A. “Processing and Analyisis of Biomedical Nonlinear Signals by Data Mining Methods,” IWSSIP 2010 - 17th International Conference on Systems, Signals and Image Processing, pp. 276–279, 2010.

  101. 101.

    Elhaj, F.A., et al.: Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. J Computer Methods Programs Biomed 127, 52–63 (2016)

    Article  Google Scholar 

  102. 102.

    Marinho, L.B., et al.: A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Fut Gen Comp Syst 97, 564–577 (2019)

    Article  Google Scholar 

  103. 103.

    Mukherjee, S., et al.: Malignant melanoma detection using multi layer preceptron with visually imperceptible features and PCA components from Med-Node dataset. Internat J Med Eng Inform (IJMEI) 12(2), 151–168 (2020)

    Google Scholar 

  104. 104.

    Mehta, S.S., Lingayat, N.S.: Development of SVM based ECG pattern recognition technique. IETE J Res 54(1), 5–11 (2008)

    Article  Google Scholar 

  105. 105.

    Mehta, S.S., Lingayat, N.S.: SVM-based algorithm for recognition of QRS complexes in electrocardiogram. IRBM 29, 310–317 (2008)

    Article  Google Scholar 

  106. 106.

    Nayak, C., et al.: “An efficient QRS complex detection using optimally designed digital differentiator. Circuits Syst Signal Proc (2019). https://doi.org/10.1007/s00034-018-0880-y

    Article  Google Scholar 

  107. 107.

    Dasgupta, H.: Human age recognition by electrocardiogram signal based on artificial neural network. Sens Imaging 17(4), 1–15 (2016)

    Google Scholar 

  108. 108.

    Jangra, M., et al.: ECG arrhythmia classification using modified visual geometry group network (mVGGNet). J Intel Fuzzy Syst 38, 3151–3165 (2020)

    Article  Google Scholar 

  109. 109.

    Gupta, V. and Mittal, M., “R-peak based Arrhythmia Detection using Hilbert Transform and Principal Component Analysis,” 2018 3rd International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). pp.116–119, doi:https://doi.org/10.1109/cipech.

  110. 110.

    Halder, B.: Classification of complete myocardial infarction using rule-based rough set method and rough set explorer system. IETE J Res (2019). https://doi.org/10.1080/03772063.2019.1588175

    Article  Google Scholar 

  111. 111.

    Sheetal, A., et al.: QRS detection of ECG signal using hybrid derivative and MaMeMi filter by effectively eliminating the baseline wander. Analog Integr Circ Sig Process 98(1), 1–9 (2019)

    Article  Google Scholar 

  112. 112.

    Narina, A., et al.: Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. J Computers Biol Med 45, 72–79 (2014)

    Article  Google Scholar 

  113. 113.

    Rai, H.M., et al.: R-peak detection using daubechies wavelet and ecg signal classification using radial basis function neural network. J Inst Eng India Ser B 95(1), 63–71 (2014)

    Article  Google Scholar 

  114. 114.

    Phy, J.: Algorithm for detection the QRS complexes based on support vector machine. J Phy IOP Conf Series 929, 1–5 (2017)

    Google Scholar 

  115. 115.

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

    Google Scholar 

  116. 116.

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

    Google Scholar 

  117. 117.

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

    Article  Google Scholar 

  118. 118.

    Liu, X., et al.: A novel R-peak detection method combining energy and wavelet transform in electrocardiogram signal. J Biom Eng 26, 1–9 (2014)

    Google Scholar 

  119. 119.

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

    Article  Google Scholar 

  120. 120.

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

    Article  Google Scholar 

  121. 121.

    Dohare, A.K., et al.: An efficient new method for the detection of QRS in electrocardiogram. Comput Electr Eng 40(5), 1717–1730 (2014)

    Article  Google Scholar 

  122. 122.

    Manikandan, M.S., Soman, K.P.: A novel method for detecting R-peaks in the electrocardiogram (ECG) signal. Biom Sig Proc Cont 7(2), 118–128 (2012)

    Article  Google Scholar 

  123. 123.

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

    Article  Google Scholar 

  124. 124.

    Pandit, D., et al.: A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput Methods Prog Biomed 144, 61–75 (2017)

    Article  Google Scholar 

  125. 125.

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

    MathSciNet  Article  Google Scholar 

  126. 126.

    Zidelmal, Z., et al.: QRS detection based on wavelet coefficients. Comp meth Prog Biomed 107(3), 490–496 (2012)

    Article  Google Scholar 

  127. 127.

    Christov II (2004) Real time electrocardiogram QRS detection using combined adaptive threshold. Bio Med Eng. OnLine 28(3P):4. http://www.biomedical-engineering-online.com/content/3/1/28.

  128. 128.

    Bouaziz, F.: Multiresolution wavelet-based QRS complex detection algorithm suited to several abnormal morphologies. IET Signal Proc 8(7), 774–782 (2014)

    Article  Google Scholar 

  129. 129.

    Choi, S., et al.: Development of ECG beat segmentation method by combining lowpass filter and irregular R-R interval checkup strategy. Exp Syst Appl 37(7), 5208–5218 (2010)

    Article  Google Scholar 

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Correspondence to Varun Gupta.

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

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Keywords

  • Cardiovascular
  • Electrocardiogram
  • Computer-aided diagnosis
  • Preprocessing
  • Feature extraction
  • Nonlinear behavior
  • Continuous wavelet transform
  • Spectrogram
  • Autoregressive technique