In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.

中文翻译：

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使用脑电信号检测精神分裂症的一种可解释的机器学习方法

在这项工作中，我们提出了一种机器学习（ML）方法，以脑电图（EEG）作为输入数据来帮助诊断精神分裂症。该计算算法不仅提出了诊断建议，而且更为重要的是，它还提供了可以接受临床解释的其他信息。它基于称为随机森林的ML模型，该模型对从EEG信号中提取的连通性指标进行操作。具体来说，我们使用广义局部有向相干性（GPDC）和直接有向传递函数（dDTF）的量度来构建ML模型的输入特征。后者可以识别与性能最相关的特征，从而提供有关与精神分裂症相关的EEG信号和频带的一些见解。我们对真实数据的初步结果表明，与枕骨区域相关的信号似乎在疾病诊断中起着重要作用。而且，尽管每个频带都可能会产生有用的诊断信息，但是β和theta（频带）频带提供的功能最终与我们已经实现的ML分类器更相关。