Noise Removal from Epileptic EEG signals using Adaptive Filters

  • Rekh Ram JanghelEmail author
  • Satya Prakash Sahu
  • Gautam Tatiparti
  • Mangesh Kose
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


Electroencephalography (EEG) is a well-established clinical procedure which provides information pertinent to the diagnosis of various brain disorders. EEG waves are highly vulnerable to diverse forms of noise which pose notable challenges in the analysis of EEG data. In this paper, adaptive filtering techniques, namely, Recursive Least Squares (RLS), Least Mean Squares (LMS), and Shift Moving Average (SMA) filters, were applied to the collected EEG signals to filter noise from the EEG signal. Various fidelity parameters, namely, Mean Square Error (MSE), Maximum Error (ME), and Signal-to-Noise Ratio (SNR), were observed. Our method has shown better performance compared to previous filtering techniques. Overall, in comparison to the previous methods, this proposed strategy is more appropriate for EEG filtering with greater accuracy.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rekh Ram Janghel
    • 1
    Email author
  • Satya Prakash Sahu
    • 1
  • Gautam Tatiparti
    • 1
  • Mangesh Kose
    • 1
  1. 1.National Institute of Technology, RaipurRaipurIndia

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