QUANTITATIVE EEG (QEEG) PREDICTS RELAPSE IN PATIENTS WITH CHRONIC-ALCOHOLISM AND POINTS TO A FRONTALLY PRONOUNCED CEREBRAL DISTURBANCE

The capability of predicting relapse in chronic alcoholism using quant itative EEG was investigated. For this purpose, 78 in-patients with al coholism underwent EEG recordings (eyes closed) 7 days after the begin ning 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 classifi cation of the two diagnostic subgroups (relapsers vs. abstainers), mul tivariate discriminant analysis as well as artificial neural network t echnology has been applied. Correct classification of patients' EEGs w as achieved in 83-85% and thus outperformed classification with clinic al variables considerably. Furthermore, artificial neural networks (AN N) improved classification results when compared with discriminant ana lysis. It was found that, in comparison to abstainers, relapsers had E EGs that were more desynchronized over frontal areas, which was interp reted as a functional disturbance of the prefrontal cortex. (C) 1998 E lsevier Science Ireland Ltd.