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Noise Removal from Epileptic EEG signals using Adaptive Filters

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Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

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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Author information

Authors and Affiliations

  1. National Institute of Technology, Raipur, Raipur, Chhattisgarh, India

    Rekh Ram Janghel, Satya Prakash Sahu, Gautam Tatiparti & Mangesh Kose

Authors
  1. Rekh Ram Janghel
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  2. Satya Prakash Sahu
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  3. Gautam Tatiparti
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  4. Mangesh Kose
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Corresponding author

Correspondence to Rekh Ram Janghel .

Editor information

Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Janghel, R.R., Sahu, S.P., Tatiparti, G., Kose, M. (2019). Noise Removal from Epileptic EEG signals using Adaptive Filters. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_4

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