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A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA

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Abstract

In general, Electrocardiogram (ECG) signal gets corrupted by variety of noise at the time of its acquisition. Unfortunately, these noise tend to mask the crucial information. Consequently, it may endanger life of the subject (patient) due to delayed diagnosis of heart health. In critical situations, proper analysis of ECG signals is very important for correct and timely detection of heart diseases. This situation motivated the present authors to develop an efficient arrhythmia detection algorithm. In this paper, a novel fractional wavelet transform (FrWT), Yule–Walker Autoregressive Analysis (YWARA), and Principal Component Analysis (PCA) are used for preprocessing, feature extraction, and detection, respectively. The type of arrhythmia detected has been interpreted based on variance estimation theory. For performance evaluation, various statistical parameters such as mean square error (MSE), detection accuracy (Acc), & output signal-to-noise ratio (SNR) are used. The proposed algorithm achieved a MSE of 0.1656%, Acc of 99.89%, & output SNR of 25.25 dB for MIT-BIH Arrhythmia database. For complete validation of this proposed work, other databases such as ventricular tachyarrhythmia, MIT-BIH long-term, and atrial fibrillation are also utilized.

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Available on physionet website (https://physionet.org).

Material Availability

Software application which is developed by present authors.

References

  1. Mehta, S.S., Lingayat, N.S. (2008). Detection of P and T-waves in electrocardiogram. World Congress on Engineering and Computer Science (WCECS-2008) pp. 1–6.

  2. Gupta, V., Mittal, M., & Mittal, V. (2019). R-peak detection based chaos analysis of ECG signal. Analog Integrated circuits and Signal Processing, 102, 479–490.

    Article  Google Scholar 

  3. Elgendi, M. (2013). Fast QRS detection with an optimized knowledge-based method: Evaluation on 11 standard ECG databases. PLoS ONE, 8(9), e73557. https://doi.org/10.1371/journal.pone.0073557,2013

    Article  Google Scholar 

  4. Elgendi, M., Jonkman, M., & Boer, F. D. (2009). Improved QRS detection algorithm using dynamic thresholds. International Journal of Hybrid Information Technology, 2(1), 65–80.

    Google Scholar 

  5. Elgendi, M., Eskofier, B., Dokos, S., & Abbott, D. (2014). Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE, 9(1), e84018. https://doi.org/10.1371/journal.pone.0084018

    Article  Google Scholar 

  6. Gupta, V., Mittal, M., & Mittal, V. (2021). An efficient low computational cost method of R-peak detection. Wireless Personal Communications. https://doi.org/10.1007/s11277-020-08017-3

    Article  Google Scholar 

  7. Park, J. S., Lee, S. W., & Park, U. (2017). R peak detection method using wavelet transform and modifed shannon energy envelope. Journal of Healthcare Engineering, 17, 1–14. https://doi.org/10.1155/2017/4901017

    Article  Google Scholar 

  8. Elgendi, M., Meo, M., & Abbott, D. (2016). A proof-of-concept study: Simple and effective detection of P and t waves in arrhythmic ECG signals. Bioengineering, 3, 26. https://doi.org/10.3390/bioengineering3040026

    Article  Google Scholar 

  9. Sahoo, S., Biswal, P., Das, T., & Sabut, S. (2016). De-noising of ECG signal and QRS detection using Hilbert transform and adaptive thresholding. Procedia Technology, 25, 68–75.

    Article  Google Scholar 

  10. Martis, R. J., Acharya, U. R., Mandana, K. M., Ray, A. K., & Chakraborty, C. (2012). Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Journal of Expert Systems with Applications, 39, 11792–11800.

    Article  Google Scholar 

  11. Gupta, V., & Mittal, M. (2020). Arrhythmia detection in ECG signal using fractional wavelet transform with principal component analysis. Journal of The Institution of Engineers India Series B. https://doi.org/10.1007/s40031-020-00488-z

    Article  Google Scholar 

  12. Xingyuan, W., & Juan, M. (2009). Wavelet-based hybrid ECG compression technique. Analog Integrated Circuits and Signal Processing, 59(3), 301–308.

    Article  Google Scholar 

  13. Rajankar, S. O., & Talbar, S. N. (2019). An electrocardiogram signal compression techniques: A comprehensive review. Analog Integrated Circuits and Signal Processing, 98(1), 59–74.

    Article  Google Scholar 

  14. Singh, D., Saini, B. S., & Kumar, V. (2008). Heart rate variability—A bibliographical survey. IETE Journal of Research, 54(3), 209–216.

    Article  Google Scholar 

  15. Kaur, H., & Rajni, R. (2017). On the detection of cardiac arrhythmia with principal component analysis. Journal of Wireless Personal Communications, 97(4), 5495–5509.

    Article  Google Scholar 

  16. Gupta, V., Kanungo, A., Kumar, P., Sharma, A. K., & Gupta, A. (2018). Auto-regressive time frequency analysis (ARTFA) of electrocardiogram (ECG) signal. International Journal of Applied Engineering Research, 13(6), 133–138.

    Google Scholar 

  17. Addison, P. S. (2005). Wavelet transforms and the ECG: A review. Physiological Measurement, 26, 155–199.

    Article  Google Scholar 

  18. Rawal, K., Saini, B. S., & Saini, I. (2017). Effect of age and postural related changes on cardiac autonomic function in the pre-menopausal and post-menopausal women. International Journal of Medical Engineering and Informatics, 9(4), 299–315.

    Article  Google Scholar 

  19. Gupta, V. et al. (2021). ECG signal analysis using CWT, Spectrogram and autoregressive technique. Iran Journal of Computer Science, in press

  20. Gupta, V., Mittal, M., and Mittal, V. (2022). An efficient AR modeling based electrocardiogram signal analysis for health informatics. International Journal of Medical Engineering and Informatics (IJMEI), in press.

  21. Mortezaee, M., Mortezaie, Z., & Abolghasemi, V. (2019). An improved SSA-based technique for EMG removal from ECG. IRBM, 40, 62–68.

    Article  Google Scholar 

  22. Chandra, S., Sharma, A., & Singh, G. K. (2019). Computationally efficient cosine modulated filter bank design for ECG signal compression. IRBM. https://doi.org/10.1016/j.irbm.2019.06.002

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Gupta, V., & Mittal, M. (2020). Efficient R-peak detection in electrocardiogram signal based on features extracted using hilbert transform and burg method. Journal of the Institution of Engineers (India): Series B. https://doi.org/10.1007/s40031-020-00423-2

    Article  Google Scholar 

  25. Jangra, M., et al. (2020). ECG arrhythmia classification using modified visual geometry group network (mVGGNet). Journal of Intelligent & Fuzzy Systems, 38, 3151–3165.

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  28. Gandhi, B., & Raghava, N. S. (2020). Fabrication techniques for carbon nanotubes based ECG electrodes: A review. IETE Journal of Research. https://doi.org/10.1080/03772063.2020.1768909

    Article  Google Scholar 

  29. Dohare, A. K., et al. (2014). An efficient new method for the detection of QRS in electrocardiogram. Computers & Electrical Engineering, 40(5), 1717–1730.

    Article  Google Scholar 

  30. Kaur, I., Rajni, R., & Marwaha, A. (2016). ECG signal analysis and arrhythmia detection using wavelet transform. Journal of the Institution of Engineers (India): Series B, 97(4), 499–507.

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Sejdic, E., Djurovic, I., Jiang, J., & Stankovic, L. J. (2009). Time-frequency based feature extraction and classification: considering energy concentration as a feature using stockwell transform and related approaches (Vol. 1). VDM Verlag Publishing.

  33. Gupta, V., and Mittal, M. (2018). ECG signal analysis: Past, present and future. In: Proc. 8th IEEE Power india international conference (PIICON), 10–12 Dec, 1–6, NIT Kurukshetra, Haryana, India (2018).

  34. Marwaha, P., & Sunkaria, R. K. (2015). Cardiac variability time-series analysis by sample entropy and multiscale entropy. International Journal of Medical Engineering and Informatics, 7(1), 1–14.

    Article  Google Scholar 

  35. Amar, D., & Abboud, S. (2016). P-wave morphology in focal atrial tachycardia using a 3D numerical model of the heart. International Journal of Medical Engineering and Informatics, 8(3), 263–274.

    Article  Google Scholar 

  36. Salman, M. N., Rao, P. T., & Rahman, M. Z. U. (2017). Cardiac signal enhancement using normalised variable step algorithm for remote healthcare monitoring systems. International Journal of Medical Engineering and Informatics, 9(2), 145–161.

    Article  Google Scholar 

  37. Murthy, H. S. N., & Meenakshi, M. (2017). Novel and efficient algorithms for early detection of myocardial ischemia. International Journal of Medical Engineering and Informatics, 9(4), 351–372.

    Article  Google Scholar 

  38. Kamath, M.V., Bentley, T., Spaziani, R., Tougas, G., Fallen, E.L., McCartney, N., Runions, J., & Upton, A.R.M. (1996). Time–frequency analysis of heart rate variability signals in patients with autonomic dysfunction. In: International symposium on time–frequency and time-scale analysis (IEEE SP 1996), pp. 373–376

  39. Singh, G., Gupta, V., Sekharmantri, A. K., Gupta, A., & Kumar, P. (2010). Real-time online monitoring of electrocardiogram(ECG) using very low cost for developing countries. AIP Conference Proceedings, 1324(1), 251–254.

    Article  Google Scholar 

  40. Qin, S., Ji, Z. (2004). Multi-resolution time-frequency analysis for detection of rhythms of EEG signals. In: 2004 IEEE 11th digital signal processing workshop & IEEE signal processing education workshop (IEEE DSP 2004), pp 338–341

  41. Meireles, A.J.M.D. (2011). ECG denoising based on adaptive signal processing technique. Thesis, Master of Technology in Electronics and Computer Science, Instituto Superior de Engenharia do Porto Portugal

  42. Nayak, C., Saha, S. K., Kar, R., & Mandal, D. (2018). Optimal SSA based wideband digital differentiator design for cardiac QRS complex detection application. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 32(2), 1–25.

    Google Scholar 

  43. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 32, 230–236.

    Article  Google Scholar 

  44. Sharma, A., Patidar, S., Upadhyaya, A., & Acharya, U. R. (2019). Accurate tunable-Q wavelet transform based method for QRS complex detection. Computers & Electrical Engineering, 75, 101–111.

    Article  Google Scholar 

  45. Nallathambi, G., & Príncipe, J. C. (2014). Integrate and fire pulse train automaton for QRS detection. IEEE Transactions on Biomedical Engineering, 61(2), 317–326.

    Article  Google Scholar 

  46. Pandit, D., Zhang, L., Liu, C., Chattopadhyay, S., Aslam, N., & Lim, C. P. (2017). A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Computer Methods and Programs in Biomedicine, 144, 61–75.

    Article  Google Scholar 

  47. Yakut, O., & Bolat, E. D. (2018). An improved QRS complex detection method having low computational load. Biomedical Signal Processing and Control, 42, 230–241.

    Article  Google Scholar 

  48. Yazdani, S., & Vesin, J. M. (2016). Extraction of QRS fiducial points from the ECG using adaptive mathematical morphology. Digital Signal Processing, 56, 100–109.

    Article  MathSciNet  Google Scholar 

  49. Yazdani, A., Fallet, S., & Vasin, J. M. (2018). A novel short-term event extraction algorithm for biomedical signals. IEEE Transactions on Biomedical Engineering, 65(4), 754–762.

    Article  Google Scholar 

  50. Biswal, B. (2017). ECG signal analysis using modified S-transform. Healthcare Technology Letters, 4(2), 68–72.

    Article  Google Scholar 

  51. Nguomkam Negou, A., & Kengne, J. (2019). A minimal three-term chaotic flow with coexisting routes to chaos, multiple solutions, and its analog circuit realization. Analog Integrated Circuits and Signal Processing. https://doi.org/10.1007/s10470-019-01436-8

    Article  Google Scholar 

  52. V. Gupta, et al., “Attractor plot as an emerging tool in ECG signal processing for improved health informatics,” International Conference on Future Technologies 2020 (ICOFT 2020) in Manufacturing, Automation, Design and Energy (MADE@NITPY), National Institute of Technology Puducherry Karaikal, India, December 28–30, 2020.

  53. Gupta, V. et al. (2020). Spectrogram as an emerging tool in ECG signal processing. In: International conference on future technologies 2020 (ICOFT 2020) in manufacturing, automation, design and energy (MADE@NITPY), National Institute of Technology Puducherry Karaikal, India, 28–30 Dec 2020.

  54. Gupta, V. et al. (2021). ECG signal analysis using emerging tools in current scenario of health informatics. In: 11th International conference on cloud computing, data science & engineering (Confluence 2021), Amity University Noida, India, 28–29 Jan 2021

  55. Elgendi, M., Jonkman, M, & Boer, F. D. (2009). R wave detection using coiflets wavelets. In: 2009 IEEE 35th annual northeast bioengineering conference. Boston, MA, USA. https://doi.org/10.1109/NEBC.2009.4967756

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

    Article  Google Scholar 

  57. Luz, E. J. S., Schwartz, W. R., Chávez, G. C., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmiadetection: A survey. Computer Methods and Programs in Biomedicine, 127, 144–164.

    Article  Google Scholar 

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

    Article  Google Scholar 

  59. Gupta, V., & Mittal, M. (2019). R-Peak detection in ECG signal using Yule–Walker and principal component analysis. IETE Journal of Research. https://doi.org/10.1080/03772063.2019.1575292

    Article  Google Scholar 

  60. Gupta, V., & Mittal, M. (2018). Electrocardiogram signals interpretation using Chaos theory. Journal of Advanced Research in Dynamical and Control Systems, 10(2), 2392–2397.

    Google Scholar 

  61. Gupta, V., & Mittal, M. (2019). A novel method of cardiac arrhythmia detection in electrocardiogram signal. International Journal of Medical Engineering and Informatics, 12(5), 489–499.

    Article  Google Scholar 

  62. Gupta, V., Mittal, M., Mittal, V. et al. (2021). Detection of R-peaks using fractional Fourier transform and principal component analysis. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03484-3

  63. Rai, H. M., Trivedi, A., Chatterjee, K., & Shukla, S. (2014). R-Peak detection using Daubechies wavelet and ECG signal classification using radial basis function neural network. Journal of The Institution of Engineers (India): Series B, 95(1), 63–71.

    Article  Google Scholar 

  64. Bhatnagar, G., Wua, Q. M. J., & Raman, B. (2013). Discrete fractional wavelet transform and its application to multiple encryption. Information Sciences, 223, 297–316.

    Article  MathSciNet  MATH  Google Scholar 

  65. Ouelli, A., Elhadadi, B., Aissaoui, H., & Bouikhalene, B. (2012). AR modeling for cardiac arrhythmia classification using MLP neural networks. International Journal of Computer Applications, 47(24), 44–51.

    Article  Google Scholar 

  66. Arnold, M., Miltner, W. H. R., Witte, H., Bauer, R., & Braun, C. (1998). Adaptive AR modeling of nonstationary time series by means of Kalman filtering. IEEE Transactions on Biomedical Engineering, 45(5), 553–562.

    Article  Google Scholar 

  67. Chawla, M. P. S. (2008). Segment classification of ECG data and construction of scatter plots using principal component analysis. Journal of Mechanics in Medicine and Biology, 8(3), 421–458.

    Article  Google Scholar 

  68. Physionet database/MITBIH Arrhythmia database. Accessed 22 Nov 2017

  69. Slimane, Z. E. H., & Ali, A. N. (2010). QRS complex detection using empirical mode decomposition. Digital Signal Processing, 20(4), 1221–1228.

    Article  Google Scholar 

  70. Narayanana, V. A., & Prabhu, K. M. M. (2003). The fractional Fourier transform: Theory, implementation and error analysis. Jornal of Microprocessors and Microsys, 27(10), 511–521.

    Article  Google Scholar 

  71. Sejdic, E., Djurovic, I., Jiang, J., Stankovic, L.J. (2009). Time–frequency based feature extraction and classification: considering energy concentration as a feature using stockwell transform and related approaches. VDM Verlag Publishing Saarbrucken, Germany (2009). http://www.vdm-verlag.de.

  72. Alfaouri, M., & Daqrouq, K. (2008). ECG signal denoising by wavelet transform thresholding. American Journal of Applied Sciences, 5(3), 276–281.

    Article  Google Scholar 

  73. Gupta, V., & Mittal, M. (2019). A Comparison of ECG signal pre-processing using FrFT, FrWT and IPCA for improved analysis. Innovation and Research in BioMedical Engineering (IRBM). https://doi.org/10.1016/j.irbm.2019.04.003

    Article  Google Scholar 

  74. Dliou, A., Latif, R., Laaboubi, M., & Maoulainine, F. M. R. (2014). Abnormal ECG signal analysis using non parametric time-frequency techniques. Arabian Journal Science Engineering, 39(2), 913–921.

    Article  Google Scholar 

  75. Martis, R. J., Acharya, U. R., Lim, C. M., & Suri, J. S. (2013). Characterization of ECG beats from cardiac arrhythmia using discrete cosine. Knowledge-Based Systems, 45, 76–82.

    Article  Google Scholar 

  76. Homaeinezhad, M. R., Atyabi, S. A., Tavakolli, E., Toosi, H. N., Ghaffari, A., & Ebrahimpour, R. (2012). ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Systems with Applications, 39(2), 2047–2058.

    Article  Google Scholar 

  77. Gupta, V., Mittal, M. (2016). Respiratory signal analysis using PCA, FFT and ARTFA. In: 2016 IEEE Proc. of ICEPES-16. Maulana Azad National Institute of Technology, Bhopal, India, 2016, pp. 221–225.

  78. Lin, C. H. (2008). Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Computers & Mathematics with Applications, 55(4), 680–690.

    Article  MathSciNet  MATH  Google Scholar 

  79. Übeyli, E. D. (2009). Statistics over features of ECG signals. Expert Systems with Applications, 36(5), 8758–8767.

    Article  Google Scholar 

  80. Güler, I., & Übeyli, E. D. (2005). ECG beat classifier designed by combined neural network model. Pattern Recognition, 38(2), 199–208.

    Article  Google Scholar 

  81. Kay, S. M. (1988). Modern spectral estimation (1st ed., pp. 328–457). Prentice Hall.

  82. Kallas, M., Honeine, P., Richard, C., Francis, C., Amoud, H. (2012). Prediction of time series using Yule–Walker equations with Kernels. In: 2012 IEEE int conf. on acoustics, speech and signal processing (ICASSP 2012), pp. 2185–2188

  83. Tomar, A. (2016). Various classifiers based on their accuracy for age estimation through facial features. International Research Journal of Engineering and Technology, 3(7), 1679–1682.

    Google Scholar 

  84. Chawla, M. P. S. (2009). A comparative analysis of principal component and independent component techniques for electrocardiograms. Journal of Neural Computing and Applications, 18(6), 539–556.

    Article  Google Scholar 

  85. Nikan, S., Sridhar, F.G., Bauer, M. Pattern recognition application in ECG arrhythmia classification. In: 10th Int joint conference on biomedical engineering systems and technologies (BIOSTEC 2017), pp.48–56

  86. Yeh, Y. C., Wang, W. J., & Chiou, C. W. (2009). Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement, 42(5), 778–789.

    Article  Google Scholar 

  87. 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.

    Article  Google Scholar 

  88. Mukhopadhyay, S., & Sircar, P. (1996). Parametric modelling of ECG signal. Journal Medical & Biological Engineering & Computing, 34(2), 171–174.

    Article  Google Scholar 

  89. Gupta, V., Mittal, M., & Mittal, V. (2021). Chaos theory and ARTFA: emerging tools for interpreting ECG signals to diagnose cardiac arrhythmias. Wireless Personal Communications, 118, 3615–3646.

    Article  Google Scholar 

  90. Gnana Subha, G., & Suja Priyadharsini, S. (2019). An efficient algorithm based on combined encoding techniques for compression of ECG data from multiple leads. Wireless Personal Communications, 108, 2137–2147. https://doi.org/10.1007/s11277-019-06513-9

    Article  Google Scholar 

  91. Al-Dujaili, M. J., & Mezeel, M. T. (2021). Novel approach for reinforcement the extraction of ECG signal for twin fetuses based on modified BSS. Wireless Personal Communications, 119, 2431–2450.

    Article  Google Scholar 

  92. Kalaivani, S., Tharini, C., Saranya, K., et al. (2020). Design and implementation of hybrid compression algorithm for personal health care big data applications. Wireless Personal Communications, 113, 599–615.

    Article  Google Scholar 

  93. Gupta, V., Mittal, M., Mittal, V., et al. (2021). A critical review of feature extraction techniques for ECG signal analysis. Journal of The Institution of Engineers (India): Series B. https://doi.org/10.1007/s40031-021-00606-5

    Article  Google Scholar 

  94. Huang, J. S., Chen, B. Q., Zeng, N. Y., et al. (2020). Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02110-y

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, U.P, 201206, India

    Varun Gupta

  2. Department of Electrical Engineering, NIT, Kurukshetra, Haryana, 136119, India

    Monika Mittal

  3. Department of Electronics and Communication Engineering, NIT, Kurukshetra, 136119, India

    Vikas Mittal

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  3. Vikas Mittal
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All simulations and methodology are performed by VG. Literature survey and consideration of available datasets is done by MM. Motivation of the research on the ECG signal is suggested by VM.

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Gupta, V., Mittal, M. & Mittal, V. A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA. Wireless Pers Commun 124, 1229–1246 (2022). https://doi.org/10.1007/s11277-021-09403-1

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