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Denoising Techniques for ECG Arrhythmia Classification Systems: An Experimental Approach

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Proceedings on International Conference on Data Analytics and Computing (ICDAC 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 175))

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Abstract

This paper presents a review of denoising techniques being implemented in ECG arrhythmia classification systems. In this work, we have investigated the frequently used denoising techniques: cascaded median filter and wavelet-based denoising methods. An experimental study was conducted using MIT-BIH arrhythmia dataset and MIT-BIH Noise Stress Test Database. The techniques are compared on the basis of SNR improvement and RMSE as performance measures. The experimental results demonstrate that the wavelet transform-based denoising method outperforms the cascaded median filter method.

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Authors and Affiliations

  1. Department of ECE, Guru Jambheshwar Univeristy of Science and Technology, Hisar, Haryana, India

    Manisha Jangra & Sanjeev Kumar Dhull

  2. Department of CSE, ASET, Amity University Uttar Pradesh, Noida, India

    Krishna Kant Singh

  3. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Akansha Singh

Authors
  1. Manisha Jangra
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  2. Sanjeev Kumar Dhull
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  3. Krishna Kant Singh
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  4. Akansha Singh
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Corresponding author

Correspondence to Akansha Singh .

Editor information

Editors and Affiliations

  1. Department of Mathematics, DR BR Ambedkar NIT Jalandhar, Jalandhar, India

    Anupam Yadav

  2. School of Mathematical Sciences, College of Science and Technology, Wenzhou Kean University, Wenzhou, China

    Gaurav Gupta

  3. School of Mathematical Sciences, College of Science and Technology, Wenzhou Kean University, Wenzhou, China

    Puneet Rana

  4. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

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Jangra, M., Dhull, S.K., Singh, K.K., Singh, A. (2023). Denoising Techniques for ECG Arrhythmia Classification Systems: An Experimental Approach. In: Yadav, A., Gupta, G., Rana, P., Kim, J.H. (eds) Proceedings on International Conference on Data Analytics and Computing. ICDAC 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 175. Springer, Singapore. https://doi.org/10.1007/978-981-99-3432-4_1

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