Classification of Cardiac Arrhythmias Using Fractal Dimensions
Abstract
Fractals are an intriguing mathematical tool that enables us to model the intricate structures found in nature and comprehend the complexity of such objects. They serve as a valuable resource for better understanding our world. Fractal objects are typically characterized by their fractal dimension, which plays a vital role in the analysis of fractal signals. In this study, we define the concept of fractal dimension and present various methods for its calculation. We demonstrate that the electrocardiogram (ECG) is a fractal signal, allowing us to classify heartbeats based on fractal theory. Our goal is to develop a digital technique for ECG signal analysis, with the aim of achieving accurate diagnosis of cardiovascular diseases.
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2. Kirk KJ, O’Shea J, Ruhf LK. ECG interpretation made incredibly easy!. Chris Burghardt.5 (2011).
3. Sedielmaci I, Reguig FB. Detection of some heart diseases using fractal dimension and chaos theory In 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA) 1 (2013): 89-94.
4. Oweis R, Hijazi L. A computer-aided ECG diagnostic tool. Computer methods and programs in biomedicine 3 (2006): 279-284.
5. http://www.physionet.org/physiobank
6. KAK, Subhash. Fractals with optimal information dimension. Circuits, Systems, and Signal Processing, 2021, p. 1-11
7. ISLAM, Nahina, HAMID, Nafiz Imtiaz Bin, MAHMUD, Adnan, et al. Detection of some major heart diseases using fractal analysis. International Journal of Biometrics and Bioinformatics (IJBB), 2010, vol. 4, no 2, p. 63
8. Maghsoudi F, Kiani K. A powerful novel method for ECG signal de-noising using different thresholding and Dual Tree Complex Wavelet Transform. the 2th Int. Knowledge-Based Engineering and Innovation (2015).
9. Kania M, Fereniec M, Maniewski R. Wavelet denoising for multi-lead high resolution ECG signals. Measurement science review 4 (2007): 30-33
10. Donoho DL, Johnstone IM. Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 1 (1995): 1200-1224.
11. Raj VN, Venkateswarlu T. ECG signal denoising using undecimated wavelet transform. In2011 3rd International Conference on Electronics Computer Technology 1 (2011): 94-98.
12. http://www.wahl.org/fe/HTML_version/link/FE4W/c4.htm
13. ABAJADDI, Nesrine, ELFAHM, Youssef, ZULFIQAR, Ali, et al. A robust voice activity detection based on single frequency filtering approach and the fractal dimension. 2022.
14. Kiani, Kourosh, and Farzane Maghsoudi. "Classification of 7 arrhythmias from ECG using fractal dimensions." Journal of Bioinformatics and Systems Biology 2.3 (2019): 53-65.
| Files | ||
| Issue | Vol 19 No S1 (2024): Supplementary 1 | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jthc.v19is1.18474 | |
| Keywords | ||
| Fractal dimension Fractal signal Electrocardiogram Signal Classification of heart diseases | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Sabrine BA. Classification of Cardiac Arrhythmias Using Fractal Dimensions. Res Heart Yield Transl Med. 2024;19(S1):12-16.
