Armitage R, Hoffmann R, Fitch T, Morel C, Bonato R
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas 75235-9070, USA.
Psychiatry Res. 1995 Apr 28;56(3):245-56. doi: 10.1016/0165-1781(95)02615-4.
Three experiments were carried out to evaluate the relationship between two techniques for quantifying electroencephalographic (EEG) data during sleep: period amplitude analysis (PAA) and power spectral analysis (PSA). In Experiment 1, canonical correlations and regression analyses were computed on PSA and PAA data from 40 undergraduate volunteers. The results yielded an average canonical correlation of 0.98. Further, multiple regression analyses demonstrated that the PSA variables accounted for approximately 66% of the variance in the PAA data, whereas PAA variables captured 88% of the variance in the PSA data. Epoch-to-epoch correlations were higher for PAA measures than for PSA data, perhaps indicating greater stability of PSA measures across epochs of sleep. In Experiment 2, PSA and PAA data were compared in 17 unmedicated outpatients with unipolar depression. Canonical correlations and regression analyses indicated that the overlap in variance between PSA and PAA did not exceed 50%, regardless of whether PSA or PAA variables were used as predictors. Epoch-to-epoch correlations between PAA measures were significantly higher than correlations among PSA variables, again suggesting greater stability of PAA data across epochs of sleep. The range of correlations for either data set was, however, substantially lower in the depressed than in the normal group. Experiment 3 evaluated the possibility that filter settings and artifact-rejection procedures had contributed to reduced overlap in PSA-PAA variance and reduced stability in depressed patients. An additional group of eight healthy volunteers served as subjects. Findings in Experiment 3 indicated that methodological differences between Experiments 1 and 2 did not account for the reduced correlations in the depressed group. It was concluded that PSA and PAA data should be comparable in normal subjects but are relatively independent in depressed patients. Epoch-to-epoch correlations were higher for PAA data than those found between PSA measures in both normal subjects and depressed patients, suggesting that PAA may be more stable across sleep epochs. Reduced stability may be a reflection of nonstationarity in the EEG of depressed patients.
进行了三项实验,以评估睡眠期间量化脑电图(EEG)数据的两种技术之间的关系:周期幅度分析(PAA)和功率谱分析(PSA)。在实验1中,对40名本科志愿者的PSA和PAA数据进行了典型相关分析和回归分析。结果得出平均典型相关系数为0.98。此外,多元回归分析表明,PSA变量约占PAA数据方差的66%,而PAA变量则捕获了PSA数据方差的88%。PAA测量的逐段相关性高于PSA数据,这可能表明PSA测量在睡眠各段中的稳定性更高。在实验2中,对17名未服用药物的单相抑郁症门诊患者的PSA和PAA数据进行了比较。典型相关分析和回归分析表明,无论将PSA还是PAA变量用作预测因子,PSA和PAA之间的方差重叠均不超过50%。PAA测量之间的逐段相关性显著高于PSA变量之间的相关性,这再次表明PAA数据在睡眠各段中的稳定性更高。然而,抑郁症组中任一数据集的相关范围都明显低于正常组。实验3评估了滤波器设置和伪迹排除程序导致抑郁症患者PSA - PAA方差重叠减少和稳定性降低的可能性。另外一组8名健康志愿者作为受试者。实验3的结果表明,实验1和实验2之间的方法学差异并不能解释抑郁症组相关性降低的原因。得出的结论是,PSA和PAA数据在正常受试者中应具有可比性,但在抑郁症患者中相对独立。正常受试者和抑郁症患者中PAA数据的逐段相关性均高于PSA测量之间的相关性,这表明PAA在睡眠各段中可能更稳定。稳定性降低可能反映了抑郁症患者脑电图的非平稳性。