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International Conference on Advanced Informatics for Computing Research

ICAICR 2018: Advanced Informatics for Computing Research pp 171–181Cite as

Morphology Analysis and Time Interval Measurements Using Mallat Tree Decomposition for CVD Detection

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 955))

Abstract

Electrocardiogram signal is used to identify the heart related abnormalities as cardiovascular disease. Automatic detection and analysis of abnormalities of long duration of ECG signals is tedious and quite subjective as it is difficult to decipher the minute morphological variations. In this paper, Morphology Analysis and Time Interval Measurements using Mallat Tree Decomposition (MTD) are done to obtain the signal in the desired form for calculation of heart rate. ECG signals are analyzed with various mother wavelets using MTD, and analyzed on the basis of performance matrices. It was found for this research work bior 3.9 wavelet is well suited for the processing of ECG signal. Heart rate using Peak Detection Algorithm (PDA) is calculated after pre-processing technique and bior 3.9 wavelet. The experiments were carried out on MATLAB R2016a environment.

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References

  1. Alwan, A.: Global Status Report on No Communicable Diseases 2010, pp. 9–31. World Health Organization, Geneva (2011)

    Google Scholar 

  2. Palanivel, S., Sukanesh, R.: Experimental studies on intelligent, wearable and automated wireless mobile tele-alert system for continuous cardiac surveillance. J. Appl. Res. Technol. 11, 133–143 (2013)

    Article  Google Scholar 

  3. Dilaveris, P.E., et al.: Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am. Heart J. 135, 733–738 (1998)

    Article  Google Scholar 

  4. Elgendi, M., Eskofier, B., Dokos, S., Abbott, D.: Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE 9, e84018 (2014)

    Article  Google Scholar 

  5. Hasan, M.A., Mamun, M.D.: Hardware approach of R-peak detection for the measurement of fetal and maternal heart rates. J. Appl. Res. Technol. 10, 835–844 (2012)

    Google Scholar 

  6. Bashour, C., et al.: Characterization of premature atrial contraction activity prior to the onset of postoperative atrial fibrillation in cardiac surgery patients. Chest 126, 831S–832S (2004)

    Article  Google Scholar 

  7. Tran, T., McNames, J., Aboy, M., Goldstein, B.: Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans. Biomed. Eng. 51, 561–569 (2004)

    Article  Google Scholar 

  8. Tsipouras, M.G., Fotiadis, D.I., Sideris, D.: Arrhythmia classification using the R-R- interval duration signal. IEEE Comput. Cardiol. 2002, 485–488 (2002)

    Article  Google Scholar 

  9. Surawicz, B., Childers, R., Deal, B.J., Gettes, L.S.: AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram. J. Am. Coll. Cardiol. 53, 976–981 (2009)

    Article  Google Scholar 

  10. Benitez, D., Gaydecki, P.A., Zaidi, A., Fitzpatrick, A.P.: The use of the Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31, 399–406 (2001)

    Article  Google Scholar 

  11. Prashar, N., Dogra, J., Sood, M., Jain, S.: Removal of electromyography noise from ECG for high performance biomedical systems. Netw. Biol. 8(1), 12–24 (2018)

    Google Scholar 

  12. Prashar, N., Jain, S., Sood, M., Dogra, J.: Review of biomedical system for high performance applications. In: 4th IEEE International Conference on signal processing and control (ISPCC 2017). 21–23 September, Jaypee University of Information technology, Waknaghat, Solan, H.P, India, pp. 300–304 (2017)

    Google Scholar 

  13. Köhler, B.-U., Hennig, C., Orglmeister, R.: QRS detection using zero crossing counts. Prog. Biomed. Res. 8, 138–145 (2003)

    Google Scholar 

  14. Dogra, J., Sood, M., Jain, S., Prashar, N.: Segmentation of magnetic resonance images of brain using thresholding techniques. In: 4th IEEE International Conference on signal processing and control (ISPCC 2017), Jaypee University of Information technology, Waknaghat, Solan, H.P, India, pp. 311–315 (2017)

    Google Scholar 

  15. Sharma, S., Jain, S., Bhusri, S.: Two class classification of breast lesions using statistical and transform domain features. J. Glob. Pharma Technol. (JGPT) 9(7), 18–24 (2017)

    Google Scholar 

  16. Rana, S., Jain, S., Virmani, J.: Classification of focal kidney lesions using wavelet-based texture descriptors. Int. J. Pharma Bio Sci. 7(3), 646–652 (2016)

    Google Scholar 

  17. Bhusri, S., Jain, S., Virmani, J.: Classification of breast lesions using the difference of statistical features. Res. J. Pharm. Biol. Chem. Sci. (RJPBCS) 7(4), 1365–1372 (2016)

    Google Scholar 

  18. Rana, S., Jain, S., Virmani, J.: SVM-based characterization of focal kidney lesions from B-mode ultrasound images. Res. J. Pharm. Biol. Chem. Sci. (RJPBCS) 7(4), 837–846 (2016)

    Google Scholar 

  19. Bhusri, S., Jain, S., Virmani, J.: Breast lesions classification using the amalagation of morphological and texture features. Int. J. Pharm. Bio Sci. (IJPBS) 7(2), 617–624 (2016)

    Google Scholar 

  20. Jain, S.: Classification of protein kinase B using discrete wavelet transform. Int. J. Inf. Technol. 10(2), 211–216 (2018)

    Google Scholar 

  21. Burte, R., Ghongade, R.: Advances in QRS detection: modified Wavelet energy gradient method. Int. J. Emerg. Trends Sign. Proc. 1, 23–29 (2012)

    Google Scholar 

  22. Zhao, Q., Zhan, L.: ECG feature extraction and classification using wavelet transform and support vector machines. In: International Conference on Neural Networks and Brain, ICNN&B 2005, vol. 2, pp. 1089–1092 (2005)

    Google Scholar 

  23. Tadejko, P., Rakowski, W.: Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification. In: 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2007, pp. 322–327 (2007)

    Google Scholar 

  24. Sufi, F., Mahmoud, S., Khalil, I.: A new ECG obfuscation method: a joint feature extraction and corruption approach. In: 2008 International Conference on Information Technology and Applications in Biomedicine, ITAB 2008, pp. 334–337 (2008)

    Google Scholar 

  25. Tamil, E.M., Kamarudin, N.H., RosliSalleh, M., Idris, Y.I., Noor, N.M., Tamil, A.M.: Heartbeat Electrocardiogram (ECG) Signal Feature Extraction Using Discrete Wavelet Transforms (DWT)

    Google Scholar 

  26. SaxenaS, C., Kumar, V., Hamde, S.T.: Feature extraction from ECG signals using wavelet transforms for disease diagnostics. Int. J. Syst. Sci. 33(13), 1073–1085 (2002)

    Article  Google Scholar 

  27. Jen, K.K., Hwang, Y.R.: ECG feature extraction and classification using cepstrum and neural networks. J. Med. Biological Eng. 28(1), 31 (2008)

    Google Scholar 

  28. Martis, R.J., Chakraborty, C., Ray, A.K.: An integrated ECG feature extraction scheme using PCA and wavelet transform. In: Annual IEEE India Conference (2009)

    Google Scholar 

  29. Srisawat, W.: Implementation of real time feature extraction of ECG using discrete wavelet transform. In: 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (2013). https://doi.org/10.1109/ECTICon.2013.6559628

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bahra University, Solan, India

    Navdeep Prashar

  2. Jaypee University of Information Technology, Solan, India

    Meenakshi Sood & Shruti Jain

Authors
  1. Navdeep Prashar
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  2. Meenakshi Sood
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  3. Shruti Jain
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Corresponding author

Correspondence to Navdeep Prashar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, Haryana, India

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  3. National Chung Cheng University, Minxiong Township, Chiayi County, Taiwan

    Pao-Ann Hsiung

  4. Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  5. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

  6. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

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Prashar, N., Sood, M., Jain, S. (2019). Morphology Analysis and Time Interval Measurements Using Mallat Tree Decomposition for CVD Detection. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_16

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  • DOI: https://doi.org/10.1007/978-981-13-3140-4_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3139-8

  • Online ISBN: 978-981-13-3140-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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