Abstract
The statistics indicate a dramatic increase in mortality due to cardiovascular failures worldwide. Since an electrocardiogram (ECG) is one of the essential tools for diagnosing cardiovascular diseases, it is a test that records the electrical activity of the heart muscle and how it contracts. This study has been presented to analyze this highly inexpensive and available signal. Noise reduction in ECG signals is one of the main problems that appear during the analysis of the heart’s electrical activity. The most troublesome noise sources contain frequency components within the ECG spectrum. Such noises are difficult to remove using typical filtering procedures. In this work, we show that the wavelet transforms very useful for denoising such types of signals. ECG signal is a self-similar object. So, fractal analysis can be implemented for the proper utilization of the gathered information. The fractal dimension is the best representative of the ECG signal, which can account for its hidden complexity. This paper introduces a new technique for the honest classification of arrhythmias from ECG signals using the fractal dimension.
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Sabrine, B.A., Taoufik, A. (2022). Classification of Arrhythmias from ECG Using Fractal Dimensions and Wavelet Theory. In: Maleh, Y., Alazab, M., Gherabi, N., Tawalbeh, L., Abd El-Latif, A.A. (eds) Advances in Information, Communication and Cybersecurity. ICI2C 2021. Lecture Notes in Networks and Systems, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-91738-8_16
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DOI: https://doi.org/10.1007/978-3-030-91738-8_16
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