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Machine Learning-Based Automated Method for Effective De-noising of Magnetocardiography Signals Using Independent Component Analysis

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Abstract

This study aims to develop an automated method for de-noising cardiac signals using independent component analysis (ICA) on a 37-channel magnetocardiography (MCG) system. The traditional approach of applying ICA involves manual visual inspection to determine the retention or removal of independent component (IC) related to signal or noise, which is time-consuming and lacks assurance in preserving essential attributes of signal components during the de-noising process. To address these challenges, we propose a novel approach. A feature set comprising spectral, statistical, and nonlinear time series properties is computed from the ICs of thirty subjects. These features are then evaluated by a few machine learning (ML) models to optimally select ICs for de-noising cardiac time series. It is found that ICs evaluated by a gradient boosting decision tree (GBDT) classifier could accomplish the task of efficiently selecting components to de-noise MCG with an accuracy of 93%. The performance of the proposed method is qualitatively and quantitatively compared against conventional methods for noise elimination and preserving signal features. The proposed method has extensive application in de-noising multichannel MCG signals where the characteristics of the noise are not clearly known and for routine diagnostic assessments of subjects with cardiac anomalies in hospital settings.

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Data Availability

The data that support the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Dr. K Gireesan, Dr. R. Nagendran, and Dr. N. V. Chandrasekar for their encouragement and support. Author C Kesavaraja expresses his gratitude to DAE for providing a research fellowship to carry out this study.

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Authors and Affiliations

  1. Indira Gandhi Centre for Atomic Research, A CI of Homi Bhabha National Institute, Kalpakkam, Tamilnadu, 603102, India

    C. Kesavaraja & Awadhesh Mani

  2. SQUIDs Applications Section, SQUID & Detector Technology Division, Materials Science Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, Tamilnadu, 603102, India

    S. Sengottuvel & Rajesh Patel

  3. Condensed Matter Physics Division, Materials Science Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, Tamilnadu, 603102, India

    Awadhesh Mani

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  1. C. Kesavaraja
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Correspondence to C. Kesavaraja or Awadhesh Mani.

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This study was approved by the institutional ethics committee of Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry.

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Appendix A

Appendix A

Signal-to-error ratio and SNR were used to assess the performance of the proposed method in de-noising MCG signals.

1.1 Signal-to-Error Ratio (SER)

$$ {\text{SER }} = \frac{{\mathop \sum \nolimits_{t = 0}^{N - 1} x^{2} \left( t \right)}}{{\mathop \sum \nolimits_{t = 0}^{N - 1} \left[ {x\left( t \right) - x{\text{de-noised}}\left( t \right)} \right]^{2} }} $$
(10)

where x(t) is the raw measured signals, xde-noised(t) is the de-noised MCG signals after the ICA algorithm, and N is the total number of samples in the signal. The SER is expressed in units of dB [5, 35].

1.2 Signal-to-Noise Ratio (SNR)

$$ {\text{SNR}} = \frac{{\mathop \sum \nolimits_{t = 0}^{N - 1} x^{2} \left( t \right)}}{{\mathop \sum \nolimits_{t = 0}^{N - 1} n^{2} \left( t \right)}} $$
(11)

Signal-to-noise ratio is calculated by considering the amplitudes of the R wave as x(t) and the peak-to-peak noise amplitude present in the time segment before the onset of the P wave as n(t), and it is expressed in dB [5].

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Kesavaraja, C., Sengottuvel, S., Patel, R. et al. Machine Learning-Based Automated Method for Effective De-noising of Magnetocardiography Signals Using Independent Component Analysis. Circuits Syst Signal Process (2024). https://doi.org/10.1007/s00034-024-02655-9

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