脑电图
人工智能
计算机科学
模式识别(心理学)
分类器(UML)
连贯性(哲学赌博策略)
机器学习
随机森林
精神分裂症(面向对象编程)
语音识别
神经科学
数学
心理学
统计
程序设计语言
作者
Manuel A. Vázquez,Arash Maghsoudi,Inés P. Mariño
标识
DOI:10.3389/fnsys.2021.652662
摘要
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.
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