多导睡眠脑电分析图特征的变化与帕金森病患者认知功能障碍的关系分析

目的 探讨多导睡眠脑电分析图特征的变化与帕金森病患者认知功能障碍的关系分析。方法 选取2021年1月至2022年1月期间在本院神经内科就诊并判定为帕金森病的患者共207例。所有患者均接受帕金森睡眠量表(PDSS)量表以评估其睡眠状况,根据其最终得分将纳入的患者进行分组,分别为无睡眠障碍组(n=126)及睡眠障碍组(n=81)。收集所有患者临床资料,并应用多导睡眠脑电监测方法进行监测。对比两组患者基线资料数据差异、脑电波相关数据、多导睡眠参数情况、睡眠相关评分,观察两组患者认知功能、焦虑、抑郁相关评分。结果 睡眠障碍组患者年龄、病程均明显高于无睡眠障碍组(均P<0.05);两组患者脑电波参数中波幅、频率、脑波指数及泛化占比对比均P<0.05;无睡眠障碍组患者睡眠效率明显高于睡眠障碍组,无睡眠障碍组患者NREM2期睡眠比例明显低于睡眠障碍组,无睡眠障碍组患者NREM3期睡眠比例明显高于睡眠障碍组(均P<0.05);无障碍睡眠组患者快速眼球运动睡眠比例明显高于睡眠障碍组(P<0.05);两组患者H-Y分期、PDSS各条目评分对比均P<0.05;睡眠障碍组认知功能评分包括MMSE、MoCA量表评分均明显低于无睡眠障碍组,睡眠障碍组焦虑、抑郁评分结果均明显高于无睡眠障碍组(均P<0.05);采用相关性分析显示年龄(r=-0.482,P=0.001)、H-Y分期(r=-0.414,P=0.005)、HAMA评分(r=-0.591,P<0.001)、HAMD评分(r=-0.574,P<0.001)、MoCA量表评分(r=-0.583,P<0.001)与患者出现睡眠障碍均呈负相关。结论 帕金森病患者认知功能障碍与睡眠结构紊乱存在一定相关性,睡眠结构紊乱会导致认知功能损害加重。

An insulinoma with clinical and electroencephalographic features resembling complex partial seizures

We described a female patient with insulinoma who experienced recurrent episodes of automatism, confusion and convulsion. Furthermore, her electroencephalography （EEG） findings resembled the pattern in complex partial seizures with secondary generalization. The interictal EEG showed spikes and sharp waves, as well as focal slowing over the left temporal lobe, and the ictal EEG revealed generalized spikes and sharp waves associated with diffused slowing. She was initially misdiagnosed as pharmacoresistant epilepsy. After the insulinoma was found and surgically removed, her EEG turned normal and she was seizure-free during the 4-year follow-up. This report highlights the need for careful reassessment of all seizures refractory to medication, even for the oatients associated with eoileotiform discharges on EEG.

An Ocular Artifacts Removal Method Based on Canonical Correlation Analysis and Two-Channel EEG Recordings

In order to more effectively apply an artifact removal melhod in an online brain-computer interface （BCI） system, a new method based on canonical correlation analysis （CCA） and two-channel eleetroeneephalography （EEG） recordings to quickly remove ocular artifacts （OA） is proposed in this paper. Considering both the formation of EEG signals contaminated by OA and the spread of OA, vertical electrooculo~＇aphy （VEOG） was appropriately introduced in CCA, and the blind source separation （BSS~ method based on CCA was used in a new way during the OA removal process. Both experimental and comparison with ICA and SOBI results show that the new method with simple calculation and fast processing speed can effectively separate and remove OA using only two-channel EEG recordings, with retaining useful EEG signals. Hence, this method used in an online BCI system will be more effective.

Uncertainty through Polynomial Chaos： A Sensor Sensitivity and Correlation Analysis in EEG Problems

The author studies the effect of uncertain conductivity on the electroencephalography （EEG） forward problem. A three-layer spherical head model with different and random layer conductivities is considered. Polynomial Chaos （PC） is used to model the randomness. The author performs a sensitivity and correlation analysis of EEG sensors influenced by uncertain conductivity. The author addressed the sensitivity analysis at three stages： dipole location and moment averaged out, only the dipole moment averaged out, and both fixed. On average, the author observes the least influenced electrodes along the great longitudinal fissure. Also, sensors located closer to a dipole source, are of greater influence to a change in conductivity. The highly influenced sensors were on average located temporal. This was also the case in the correlation analysis. Sensors in the temporal parts of the brain are highly correlated. Whereas the sensors in the occipital and lower frontal region, though they are close together, are not so highly correlated as in the temporal regions. This study clearly shows that intrinsic sensor correlation exists, and therefore cannot be discarded, especially in the inverse problem. In the latter it makes it possible not to specify the conductivities. It also offers an easy but rigorous modeling of the stochastic propagation of uncertain conductivity to sensorial potentials （e.g., making it suited for research on optimal placing of these sensors）.

Phase Spectral Analysis of EEG Signals

A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition,the original EEGs are mapped to an orthogonal wavelet space,such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain,but do not the epileptic EEGs. For epileptic EEGs,only at those transient points,the phase variations are obvious. In order to verify these results with the observational data,the phase variations of EEGs in principal component space are observed and found that,the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.

Detection and Separation of Event-related Potentials from Multi-Artifacts Contaminated EEG by Means of Independent Component Analysis

Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.

Brain-Computer Interface Design Using Signal Powers Extracted During Motor Imagery Tasks

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the 6al level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

Wavelet Variance Analysis of EEG Based on Window Function

A new wavelet variance analysis method based on window function is proposed to investigate the dynamical features of electroencephalogram(EEG).The exprienmental results show that the wavelet energy of epileptic EEGs are more discrete than normal EEGs, and the variation of wavelet variance is different between epileptic and normal EEGs with the increase of time-window width. Furthermore, it is found that the wavelet subband entropy (WSE) of the epileptic EEGs are lower than the normal EEGs.