Key result
The proposed statistical mathematical model for EEG signal de-noising achieved an accuracy of 99.01%, outperforming the existing discrete wavelet transform technique which had 97.66% accuracy.
Why the study?
Raw EEG signals contain different types of noise that hinder early diagnosis of brain-related diseases, requiring efficient algorithms to process the signal and combat noise.
Absolute Event Rate: 99.01% vs 97.66%
The proposed algorithm using likelihood ratio test and PCA classifier achieves 99.01% efficiency in denoising EEG signals.
May support refined EEG preprocessing in research; leaves open prospective clinical validation before practice impact.
An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test converts EEG data signal into segmented data with nearly constant noise characteristics. This will aid in detecting the noise present in a tiny segment which ensures proper signal denoising. The processed signal is compared with the database of noiseless EEG of the same person using principal component analysis (PCA) classifier. The proposed algorithm is 99.01% efficient to identify and combat noise in the EEG signal.
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Dalal et al. (2022) studied Epilepsy (EEG signal noise). Proposed statistical mathematical model (likelihood ratio test and PCA classifier) vs. Discrete Wavelet Transform (DWT) based technique was evaluated on Accuracy of signal de-noising and classification. The proposed statistical mathematical model for EEG signal de-noising achieved an accuracy of 99.01%, outperforming the existing discrete wavelet transform technique which had 97.66% accuracy.
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