Winterer G, Klöppel B, Heinz A, Ziller M, Dufeu P, Schmidt L G, Herrmann W M
Department of Psychiatry, Free University of Berlin, Germany.
Psychiatry Res. 1998 Mar 20;78(1-2):101-13. doi: 10.1016/s0165-1781(97)00148-0.
The capability of predicting relapse in chronic alcoholism using quantitative EEG was investigated. For this purpose, 78 in-patients with alcoholism underwent EEG recordings (eyes closed) 7 days after the beginning of detoxification. Additionally, other clinical evaluations were carried out. After discharge from hospital, patients were regularly re-evaluated for the duration of 3 months in order to determine whether they relapsed or abstained from alcohol during this time. For classification of the two diagnostic subgroups (relapsers vs. abstainers), multivariate discriminant analysis as well as artificial neural network technology has been applied. Correct classification of patients' EEGs was achieved in 83-85% and thus outperformed classification with clinical variables considerably. Furthermore, artificial neural networks (ANN) improved classification results when compared with discriminant analysis. It was found that, in comparison to abstainers, relapsers had EEGs that were more desynchronized over frontal areas, which was interpreted as a functional disturbance of the prefrontal cortex.
研究了使用定量脑电图预测慢性酒精中毒复发的能力。为此，78名酒精中毒住院患者在戒毒开始7天后进行了脑电图记录（闭眼）。此外，还进行了其他临床评估。出院后，对患者进行了为期3个月的定期重新评估，以确定在此期间他们是否复发或戒酒。为了对两个诊断亚组（复发者与戒酒者）进行分类，应用了多变量判别分析以及人工神经网络技术。患者脑电图的正确分类率达到了83%-85%，因此明显优于使用临床变量进行的分类。此外，与判别分析相比，人工神经网络（ANN）改善了分类结果。研究发现，与戒酒者相比，复发者额叶区域的脑电图去同步化程度更高，这被解释为前额叶皮质的功能障碍。
