PubMed · 14828868
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D MUNNELL. 1950. Electroencephalography.. https://pubmed.ncbi.nlm.nih.gov/14828868/
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To evaluate sleep-related obstructive breathing events in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), we developed a technique for digital recording and analysis of esophageal pressure (Pes) and elucidated the Pes parameters. Pes was recorded overnight with a microtip-type pressure transducer in 74 patients with OSAHS. Simultaneously, in all patients digital polysomnography was recorded. The mean nadir end-apneic Pes swing (Pes Nadir) ranged from -20.2 to -147.4 cmH(2)O, with a mean of -53.6+/-2.9 cmH(2)O. Correlation of the mean Pes Nadir indicated a linear relationship with the mean ratio of maximal Pes swing to apnea duration (r(2)=0.70) and the mean area of the Pes (Pes Area) (r(2)=0.82). Significant correlations were noted between the mean Pes Nadir and apnea-hypopnea index (AHI, ranging from 7.9 to 109.5 per hour; r(2)=0.66), minimum SpO(2) (r(2)=0.60), oxygen desaturation index (ODI) of more than 3 (r(2)=0.65), arousal index (r(2)=0.54), and between the mean Pes Area and AHI (r(2)=0.63), minimum percutaneous arterial oxygen saturation (SpO(2); r(2)=0.57), ODI (r(2)=0.69), and arousal index (r(2)=0.41). Pes parameters were found to be significant in the evaluation of the severity of the respiratory effort during the sleep-related obstructive breathing events for patients with OSAHS.
OBJECTIVE: Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets. METHODS: The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax rotations were tested on 100 simulation datasets. Also, whether the effects of these parameters are mediated by component size was examined. RESULTS: Parameters were evaluated according to time course reconstruction, source localization results, and misallocation of ANOVA effects. Correlation matrices resulted in dramatic misallocation of variance. The Promax rotation yielded much more accurate results than Varimax rotation. Covariance loadings were inferior to Kaiser Normalization and unweighted loadings. CONCLUSIONS: Based on the current simulation of two components, the evidence supports the use of a covariance matrix, Kaiser normalization, and Promax rotation. When these parameters are used, unrestricted solutions did not materially improve the results. We argue against their use. Results also suggest that optimized PCA procedures can measurably improve source localization results. SIGNIFICANCE: Continued development of PCA procedures can improve the results when PCA is applied to ERP datasets.