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Intelligent ECG Signal Filtering Method Based on SVM Algorithm

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Abstract

This work presents a novel and robust automatic electrocardiogram (ECG) filtering system. The proposed filtering system allows an automatic selection of the adequate filter by applying the support vector machine (SVM). The SVM classification is based on an electrocardiogram feature vector that integrates four main input characteristics (maximum and minimum amplitudes, maximum frequency and power spectral density). This paper provides ECG denoising efficiency based on the automatic selection of three different competing filtering methods: the discrete wavelet transform (DWT), empirical mode decomposition (EMD) and the extended Kalman filter (EKF). The extensive simulations are performed by using the MIT-BIH database. The effectiveness of the proposed system is evaluated in term of signal-to-noise ratio (SNR) improvement in decibel (dB). The results showed that the proposed automatic ECG filtering system provided a better signal-to-noise ratio compared to the DWT, EMD or EKF algorithm alone. Therefore, an implementation of the proposed filtering system on graphical processing units (GPU) was realized to optimize execution time.

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Data availability

The dataset used in this article is available in ECG database at https://archive.physionet.org/physiobank/database/#ecg

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Authors and Affiliations

  1. Technology and Medical Imaging Laboratory, Faculty of Medicine of Monastir, University of Monastir, 5019, Monastir, Tunisia

    Ines Assali, Ibtihel Nouira, Afef Abidi & Mohamed Hédi Bedoui

  2. Faculty of Sciences of Monastir, University of Monastir, 1002, Monastir, Tunisia

    Ines Assali

  3. National Engineering School of Sousse, University of Sousse, BP 264, 4023, Erriadh, Sousse, Tunisia

    Afef Abidi

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  1. Ines Assali
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Assali, I., Nouira, I., Abidi, A. et al. Intelligent ECG Signal Filtering Method Based on SVM Algorithm. Circuits Syst Signal Process 42, 1773–1791 (2023). https://doi.org/10.1007/s00034-022-02196-z

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