Abstract
The principal purpose of this paper is to incorporate digital filters in the preprocessing of electroencephalogram (EEG) signal to remove deep brain stimulation (DBS) artifact. DBS is used in the treatment of Parkinson’s disease (PD). During the monitoring of EEG, various stimulation artifacts may overlap with EEG signal. Therefore, a filter is required, which can effectively eliminate these artifacts with the least distorting EEG signal. In the present work, performance comparison of Hampel and median filters is carried out to eliminate these artifacts. The effectiveness of these filters is tested on different types of signals: sinusoidal, synthetic EEG signals. Further, these filters are tested on real EEG signals corrupted with DBS noise. These signals are acquired from patients under the treatment for PD. The performance comparison of filters is evaluated on the basis of signal-to-noise ratio (SNR), SNR improvement (SNRI), mean square error (MSE), and signal distortion. The results reveal that Hampel filter removes the noise more efficiently as compared to median filter.
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Dagar, M., Mishra, N., Rani, A., Agarwal, S., Yadav, J. (2018). Performance Comparison of Hampel and Median Filters in Removing Deep Brain Stimulation Artifact. In: Panda, B., Sharma, S., Batra, U. (eds) Innovations in Computational Intelligence . Studies in Computational Intelligence, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-10-4555-4_2
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DOI: https://doi.org/10.1007/978-981-10-4555-4_2
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