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Classification of ECG signals based on local fractal feature

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Abstract

Accurate and automatic analysis of electrocardiogram (ECG) signals plays a key role in the diagnosis of cardiovascular disease. This paper aims to investigate the performance of the multifractal detrending fluctuation analysis (MF-DFA) method based on a sliding window in ECG signal classification. We apply ECG signals to the detrended fluctuation analysis (DFA) method to obtain a group of local Hurst exponents and compose the DFA series. Afterwards, we use the MF-DFA method to get 6 generalized Hurst exponents of atrial premature beats (APB) and ECG signals with normal sinus rhythm (NSR), respectively. The 6 generalized Hurst exponents compose a feature vector and it is used in support vector machine (SVM) to examine the accuracy of ECG signal classification. The calculated results show that the MF-DFA method combined with a sliding window (SWD-MF-DFA) method performs well. Compared with directly using the MF-DFA method, with the same parameters, the accuracy of SWD-MF-DFA is higher, so as is the sensitivity and the specificity. As well, the proposed model can better analyze the local detailed features of time series and allows the application of MF-DFA in different parts of the time series, enabling the detection and analysis of time series variability.

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Data Availibility Statement

The datasets of ECG signals analysed during the current study are available in the MIT-BIH Arrhythmia Database and the data can be acquired from the corresponding author upon reasonable request.

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Acknowledgements

The corresponding author Jian Wang was supported by the Open Project of Center for Applied Mathematics of Jiangsu Province (Nanjing University of Information Science and Technology). In addition, the authors are grateful to Haixiao Wang for his contributions in language editing and polishing. Meanwhile, the authors express deep gratitude to the reviewers for their valuable suggestions and comments, which significantly improved the quality of this article.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Wenjing Jiang & Jian Wang

  2. Center for Applied Mathematics of Jiangsu Province, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Wang

  3. Jiangsu International Joint Laboratory on System Modeling and Data Analysis, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Wang

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  1. Wenjing Jiang
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Correspondence to Jian Wang.

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Jiang, W., Wang, J. Classification of ECG signals based on local fractal feature. Multimed Tools Appl 83, 54773–54789 (2024). https://doi.org/10.1007/s11042-023-17787-4

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  • DOI: https://doi.org/10.1007/s11042-023-17787-4

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