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

  • Navdeep PrasharEmail author
  • Meenakshi Sood
  • Shruti Jain
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


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.


Cardiovascular disease Mallat Tree Decomposition Heart rate Time interval measurement Morphology analysis 


  1. 1.
    Alwan, A.: Global Status Report on No Communicable Diseases 2010, pp. 9–31. World Health Organization, Geneva (2011)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Dilaveris, P.E., et al.: Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am. Heart J. 135, 733–738 (1998)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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. 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. 13.
    Köhler, B.-U., Hennig, C., Orglmeister, R.: QRS detection using zero crossing counts. Prog. Biomed. Res. 8, 138–145 (2003)Google Scholar
  14. 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. 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. 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. 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. 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. 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. 20.
    Jain, S.: Classification of protein kinase B using discrete wavelet transform. Int. J. Inf. Technol. 10(2), 211–216 (2018)Google Scholar
  21. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  27. 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. 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. 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).

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringBahra UniversitySolanIndia
  2. 2.Jaypee University of Information TechnologySolanIndia

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