Supervisor：Ministry of Education of the People's Republic of ChinaSponsor：Sichuan University Editor-in-chief：ZHOU DONG Publishing period：
Bimonthly
ISSN：2096-0247CN：51-1762/R
Objective To explore the change of EEG waveform recorded by clinical EEG under different filtering parameters.Methods 22 abnormal EEG samples of epilepsy patients with abundant abnormal waveforms recorded in Peking University first hospital were selected as the case group (abnormal group), and 30 normal EEG samples of healthy people with matched sex and age were selected as the control group (normal group). Visual examination and power spectrum analysis were then performed to compare the difference of wave forms and spectrum power under different settings of filter parameter between the two groups. Results The results of visual examination show that, lower high-frequency filtering has an effect on the fast wave composition of EEG and may distort and reduce the spike wave. Higher low-frequency filtering has an effect on the overall background and slow wave activity of EEG and may change the amplitude morphology of some slow waves. The results of power spectrum analysis show that, Compare the difference between the EEG normal group and the abnormal group, the main difference under the settings of 0.5~70Hz was on the θ and α3 frequency band, different brain regions were slightly different. In the central region, the difference in the high frequency band (α3, γ1, γ2) decreases or disappears with the decrease of the high frequency filtering. In the rest of the brain, the difference in the δ band appears gradually with the increase of the low frequency filtering. Compare the difference between frontal area and occipital area under different filter set, for the normal group, under the settings of 0.5 ~ 70 Hz, the difference between two regions is mainly on the θ, γ1 and γ2 band. When high frequency filter reduces, the difference between two regions on high frequency band (γ1, γ2) are gradually reduced or disappeared. And when low frequency filter increases, the difference on δ band appears. For the abnormal group, the difference between frontal and occipital region under the settings of 0.5 ~ 70 Hz is mainly on γ1 and γ2 bands. When the high-frequency filter decreases, the difference between two regions on high-frequency bands are gradually decreased or disappeared. All the results can be corrected by FDR. Conclusion The results show that the filter setting has a significant influence on EEG results. In clinical application, we should strictly set 0.5 ~ 70 Hz bandpass filtering as the standard.
Citation：LEI Xiangyu, DENG Xin, WANG Weiwei, WU Xun. EEG waveform and spectrum-power analysis under different settings of filter parameter. Journal of Epilepsy, 2022, 8(2): 99-107. doi: 10.7507/2096-0247.202111013Copy
Copyright ? the editorial department of Journal of Epilepsy of West China Medical Publisher. All rights reserved
1.
Rosenow F, Klein KM, Hamer HM. Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev Neurother, 2015, 15(4): 425-444.
2.
Gawel M, Zalewska E, Szmidt-salkowska E, et al. The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia. J Neurol Sci, 2009, 283(1-2): 127-133.
3.
Miyake W, Oda Y, Ikeda Y, et al. Electroencephalographic response following midazolam-induced general anesthesia: relationship to plasma and effect-site midazolam concentrations. J Anesth, 2010, 24(3): 386-393.
Sinha S R, Sullivan L, Sabau D, et al. American clinical neurophysiology society guideline 1: minimum technical requirements for performing clinical electroencephalography. J Clin Neurophysiol, 2016, 33(4): 303-307.
9.
Somers B, Francart T, Bertrand A. A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of neural engineering, 2018, 15(3): 036007.
10.
Liao SC, Wu CT, Huang HC, et al. Major depression detection from eeg signals using kernel eigen-filter-bank common spatial patterns. Sensors (Basel, Switzerland), 2017, 17(6): 1385.
11.
Seeck M, Koessler L, Bast T, et al. The standardized EEG electrode array of the IFCN. Clin Neurophysiol, 2017, 128(10): 2070-2077.
Benedek K, Berenyi A, Gombkoto P, et al. Neocortical gamma oscillations in idiopathic generalized epilepsy. Epilepsia, 2016, 57(5): 796-804.
1. Rosenow F, Klein KM, Hamer HM. Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev Neurother, 2015, 15(4): 425-444.
2. Gawel M, Zalewska E, Szmidt-salkowska E, et al. The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia. J Neurol Sci, 2009, 283(1-2): 127-133.
3. Miyake W, Oda Y, Ikeda Y, et al. Electroencephalographic response following midazolam-induced general anesthesia: relationship to plasma and effect-site midazolam concentrations. J Anesth, 2010, 24(3): 386-393.
8. Sinha S R, Sullivan L, Sabau D, et al. American clinical neurophysiology society guideline 1: minimum technical requirements for performing clinical electroencephalography. J Clin Neurophysiol, 2016, 33(4): 303-307.
9. Somers B, Francart T, Bertrand A. A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. Journal of neural engineering, 2018, 15(3): 036007.
10. Liao SC, Wu CT, Huang HC, et al. Major depression detection from eeg signals using kernel eigen-filter-bank common spatial patterns. Sensors (Basel, Switzerland), 2017, 17(6): 1385.
11. Seeck M, Koessler L, Bast T, et al. The standardized EEG electrode array of the IFCN. Clin Neurophysiol, 2017, 128(10): 2070-2077.
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Citation：LEI Xiangyu, DENG Xin, WANG Weiwei, WU Xun. EEG waveform and spectrum-power analysis under different settings of filter parameter. Journal of Epilepsy, 2022, 8(2): 99-107. doi: 10.7507/2096-0247.202111013
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