Electrical and Computer Eng. Dept., McMaster University, Hamilton, ON, Canada.
Clin Neurophysiol. 2010 Dec;121(12):1998-2006. doi: 10.1016/j.clinph.2010.05.009. Epub 2010 Jun 17.
To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia.
Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results. First, a feature selection scheme is employed to select a reduced subset of features extracted from the subjects' EEG that is most statistically relevant to our treatment-response prediction. These features are then entered into a classifier, which is realized in the form of a kernel partial least squares regression method that performs response prediction. Various scales, including the positive and negative syndrome scale (PANSS) are used as treatment-response indicators.
We determined that a set of discriminating EEG features do exist. A low-dimensional representation of the feature space showed significant clustering into clozapine responder and non-responder groups. The minimum level of performance of the proposed prediction methodology, tested over a range of conditions using the leave-one-out cross-validation method using the original 23 subjects, with further testing in an independent sample of 14 subjects, was 85%.
These findings indicate that analysis of pre-treatment EEG data can predict the clinical response to clozapine in treatment resistant schizophrenia.
If replicated in a larger population, this novel approach to EEG analysis may assist the clinician in determining treatment-efficacy.
研究是否可以将先进的机器学习（ML）方法应用于预处理脑电图（EEG）数据，以预测慢性精神分裂症成年患者对氯氮平治疗的反应。
在 23 名+14 名精神分裂症患者中收集预处理 EEG 数据。经过至少一年的随访，使用经过训练的临床医生对 EEG 结果不知情的临床评分来确定治疗结果。首先，采用特征选择方案从患者 EEG 中选择与我们的治疗反应预测最相关的统计学上有意义的特征的缩小子集。然后将这些特征输入到分类器中，该分类器以核偏最小二乘回归方法的形式实现，用于执行响应预测。各种量表，包括阳性和阴性症状量表（PANSS），用作治疗反应指标。
我们确定了一组具有区分能力的 EEG 特征确实存在。特征空间的低维表示显示出与氯氮平反应者和非反应者群体的显著聚类。在使用原始 23 名受试者的留一交叉验证方法测试的一系列条件下，所提出的预测方法的最低性能水平为 85%，在 14 名独立受试者的样本中进行了进一步测试。
这些发现表明，预处理 EEG 数据的分析可以预测氯氮平治疗抵抗性精神分裂症的临床反应。
如果在更大的人群中得到复制，这种 EEG 分析的新方法可能有助于临床医生确定治疗效果。
