北京大学学报(医学版)
北京大學學報(醫學版)
북경대학학보(의학판)
JOURNAL OF PEAKING UNIVERSITY(HEALTH SCIENCES)
2003年
3期
231-235
,共5页
亓颖伟%罗非%张蔚婷%王颖%张景渝%Donald J.WOODWARD%陈昭燃%韩济生
亓穎偉%囉非%張蔚婷%王穎%張景渝%Donald J.WOODWARD%陳昭燃%韓濟生
기영위%라비%장위정%왕영%장경투%Donald J.WOODWARD%진소연%한제생
滑行窗口技术%诱发电位%单次检验阈值
滑行窗口技術%誘髮電位%單次檢驗閾值
활행창구기술%유발전위%단차검험역치
Sliding-window Technique%Evoked potentials%Individual test threshold
目的:评价滑行窗口技术分析脑电诱发电位的能力.方法:将具有一定宽度的时间窗口延时间轴滑行,计算该窗口内的脑电电位平均值,再与对照窗口进行统计比较,以检验诱发电位是否具有统计显著性.利用该方法分析随机产生的模拟数据,计算在指定单次检验阈值下,多次统计比较导致显著性差异点连续出现的几率,以确定可使整体α值小于0.05的cluster大小.为检验该方法的有效性,在14名健康右利手志愿者右手中指给予痛或非痛电刺激,记录EEG信号并采用上述技术加以分析.结果:在整体α值确定的前提下,作为显著性判据的cluster大小随单次检验阈值与窗宽的增加而增大.依据上述方法分析真实EEG数据,确定了体感与痛觉诱发电位波形中具有统计学意义的成分,以及两种波形之间的显著性差异.结论:滑行窗口技术可有效地用于分析脑电诱发电位.
目的:評價滑行窗口技術分析腦電誘髮電位的能力.方法:將具有一定寬度的時間窗口延時間軸滑行,計算該窗口內的腦電電位平均值,再與對照窗口進行統計比較,以檢驗誘髮電位是否具有統計顯著性.利用該方法分析隨機產生的模擬數據,計算在指定單次檢驗閾值下,多次統計比較導緻顯著性差異點連續齣現的幾率,以確定可使整體α值小于0.05的cluster大小.為檢驗該方法的有效性,在14名健康右利手誌願者右手中指給予痛或非痛電刺激,記錄EEG信號併採用上述技術加以分析.結果:在整體α值確定的前提下,作為顯著性判據的cluster大小隨單次檢驗閾值與窗寬的增加而增大.依據上述方法分析真實EEG數據,確定瞭體感與痛覺誘髮電位波形中具有統計學意義的成分,以及兩種波形之間的顯著性差異.結論:滑行窗口技術可有效地用于分析腦電誘髮電位.
목적:평개활행창구기술분석뇌전유발전위적능력.방법:장구유일정관도적시간창구연시간축활행,계산해창구내적뇌전전위평균치,재여대조창구진행통계비교,이검험유발전위시부구유통계현저성.이용해방법분석수궤산생적모의수거,계산재지정단차검험역치하,다차통계비교도치현저성차이점련속출현적궤솔,이학정가사정체α치소우0.05적cluster대소.위검험해방법적유효성,재14명건강우리수지원자우수중지급여통혹비통전자격,기록EEG신호병채용상술기술가이분석.결과:재정체α치학정적전제하,작위현저성판거적cluster대소수단차검험역치여창관적증가이증대.의거상술방법분석진실EEG수거,학정료체감여통각유발전위파형중구유통계학의의적성분,이급량충파형지간적현저성차이.결론:활행창구기술가유효지용우분석뇌전유발전위.
SUMMARY Objective: To evaluate the efficiency of sliding-window technique in extracting and analyzing somatosensory evoked potentials (SEP) from multichannel electroencephalogram (EEG) data. Methods: A time window of certain window size was moved along the time dimension of data sets. Values within the window were averaged for each trial, and then compared with a preset control window. The probability of randomly appeared significance resulting from repeated statistical comparison was calculated utilizing simulated EEG data sets. Cluster size (number of successive significant data points with given individual significance threshold) was determined to keep the general alpha value under 0.05. To test this procedure, multichannel EEG signals were recorded and analyzed from fourteen healthy right-handed volunteers, with painful and non-painful electrical stimuli delivered to the right middle fingers. Results: Cluster size increased in parallel with window size and individual statistical threshold. The major SEP components of real EEG data, as well as the difference between pain and non-pain SEPs, were demonstrated to be significant with the sliding-window method. Conclusion: Sliding-window method is an effective tool for the analysis of SEP data.