Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 468-475

Original Article

Slantlet Transform-Based ECG Classification System with Efficient Design and Implementation

Majid A. Alwan 1 Abdulhamed M. Jasim 2 Anas Khalid Abdullah 3 Jassim M. Abdul-Jabbar 1
1 Control and Computer Department - Almaaqal University , College of Engineering, Basrah, Iraq
2 Department of Electronic Engineering, - Ninevah University, College of Electronics Engineering, Mosul, Iraq
3 Department of Communication Engineering - Ninevah University, College of Electronics Engineering, Mosul, Iraq
History
  • Received: December 24, 2024
  • Revised: February 23, 2025
  • Accepted: March 7, 2025
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.41

Abstract

Since cardiac conditions are among the most fatal illnesses in the medical community, ECG classification systems are crucial for understanding and diagnosing patients’ health conditions. Numerous techniques for ECG feature extraction and classification algorithms are developed by researchers. This paper presents a method for accurately classifying ECG illnesses based on the 3-scale Slantlet transform (SLT) and artificial neural network (ANN). The ability of the SLT filters to decompose the ECG signal at various resolutions led to excellent classification. As a new realization, all coefficients of the modified designed SLT filters are expressed by the sum-of-power-of-two (SOPOT) approach to reduce the complexity. It is noteworthy that the average and maximum deviation error values between the responses of original and modified filters are very small. Hardwarely, the new realization leads to a less complex implementation for the designed SLT filters on FPGA kit using the Xilinx System Generator for DSP with very small errors between output resposes of the original and modified filters. FPGA results show that the system is designed using a best-selected wordlength method. The proposed classification system is capable of distinguishing the ECG normal case and other four different diseases with a high overall accuracy of 98.50 %.

Keywords

References

  1. W. Zhang, X. Wang, L. Ge, and Z. Zhang, “Detection of ecg signal based on multi-resolution sub-band filter,” pp. 2714–2717, 2006.
  2. S. Sumathi, “An approach for ecg feature extraction and classification of cardiac abnormalities,” Cardiovascular Pharmacology: Open Access, vol. 7, p. 234, 2018.
  3. R. Thilagavathy, R. Srivatsan, S. Sreekarun, D. Sudeshna, P. L. Priya, and B. Venkataramani, “Real-time ecg signal feature extraction and classification using support vector machine,” pp. 44–48, 2020.
  4. I. W. Selesnick, “The slantlet transform,” IEEE transactions on signal processing, vol. 47, no. 5, pp. 1304–1313, 2002.
  5. N. B. Patil, V. Viswanatha, and S. Pande, “Slant transformation as a tool for pre-processing in image processing,” International Journal of Scientific & Engineering Research, vol. 2, no. 4, pp. 1–7, 2011.
  6. R. Sinhal and I. A. Ansari, “Comparative analysis of watermark reconstruction using discrete wavelet transform and slantlet transform for user identification in social media,” pp. 1–5, 2020.
  7. H. Singh, A. Kumar, L. Balyan, and G. K. Singh, “Slantlet filter-bank-based satellite image enhancement using gamma-corrected knee transformation,” International Journal of Electronics, vol. 105, no. 10, pp. 1695–1715, 2018.
  8. D. Sinaga, E. H. Rachmawanto, C. A. Sari, N. A. Setiyanto, et al., “An enhancement of data hiding imperceptibility using slantlet transform (slt),” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 87–98, 2019.
  9. R. Baudin and G. Lesthievent, “Design of fir filters with sum of power-of-two representation using simulated annealing,” pp. 339–345, 2014.
  10. A. Madanayake, R. J. Cintra, V. Dimitrov, F. Bayer, K. A. Wahid, S. Kulasekera, A. Edirisuriya, U. Potluri, S. Madishetty, and N. Rajapaksha, “Low-power vlsi architectures for dct/dwt: precision vs approximation for hd video, biomedical, and smart antenna applications,” IEEE Circuits and Systems Magazine, vol. 15, no. 1, pp. 25–47, 2015.
  11. A. M. Jasim, H. M. Abd, and J. M. Abdul-Jabbar, “Complexity reduction of slantlet transform structure based on the multiplierless realization,” Journal of Engineering Science and Technology, vol. 15, no. 3, pp. 1705–1718, 2020.
  12. H. Zairi, M. Kedir Talha, K. Meddah, and S. Ould Slimane, “Fpga-based system for artificial neural network arrhythmia classification,” Neural Computing and Applications, vol. 32, pp. 4105–4120, 2020.
  13. M. G. Egila, M. A. El-Moursy, A. E. El-Hennawy, H. A. El-Simary, and A. Zaki, “Fpga-based electrocardiography (ecg) signal analysis system using least-square linear phase finite impulse response (fir) filter,” Journal of Electrical Systems and Information Technology, vol. 3, no. 3, pp. 513–526, 2016.
  14. I. A. Basheer and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” Journal of microbiological methods, vol. 43, no. 1, pp. 3–31, 2000.
  15. L. V. Fausett, “Fundamentals of neural networks: Architectures, algorithms and applications,” Prentice Hall, first edition, p. 3, 1994.
  16. https://archive.physionet.org/cgi bin/ATM., “Ecg mitbih database,”
  17. R. J. Martis, “Application of higher order cumulant features for cardiac health diagnosis using ecg signals,” International Journal of Neural Systems, August 2013.
  18. L. C. M. Roshan Joy Martis, U. Rajendra Acharya, “Ecg beat classification using pca, lda, ica and discrete wavelet transform,” Journal of Biomedical Signal Processing and Control, 2013.
  19. S. Sahoo, B. Kanungo, S. Behera, and S. Sabut, “Multiresolution wavelet transform based feature extraction and ecg classification to detect cardiac abnormalities,” Measurement, vol. 108, pp. 55–66, 2017.
  20. O. N. Saadi, Z. N. Abdulkader, and J. M. Abdul-Jabbar, “Implementation of ecg classification xilinx system generator,” pp. 1–6, 2019.
  21. S. J. Abou-Loukh, T. Zeyad, and R. Thabit, “Ecg classification using slantlet transform and artificial neural network,” Journal of Engineering, vol. 16, no. 01, pp. 4510– 4528, 2010.