Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal.
Center for Microelectromechanical Systems (CMEMS), School of Engineering, University of Minho, Guimarães, Portugal.
Artif Intell Med. 2021 Apr;114:102039. doi: 10.1016/j.artmed.2021.102039. Epub 2021 Feb 19.
The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.
精神分裂症症状的复杂性和异质性对客观诊断构成挑战，而客观诊断通常基于行为和临床表现。此外，精神分裂症的边界与其他分类类别（如双相情感障碍）没有明确区分。精神分裂症的早期发现可以导致更有效的治疗，从而提高患者的生活质量。在过去几十年中，已有数百项研究旨在通过使用脑电图（EEG）等技术来确定支持精神分裂症临床表现的神经生物学机制。EEG 事件相关电位的变化与感觉和认知缺陷有关，并被提出作为精神分裂症的生物标志物。除了有助于更有效的诊断外，生物标志物对于预测精神分裂症的发病和预后也至关重要。但是，任何提出的生物标志物都需要大量的临床研究来证明其有效性和成本效益。受计算神经科学发展的推动，已经尝试使用脑成像模式识别方法来捕捉功能脑活动差异，对不同阶段（前驱期、首发期、慢性期）的精神分裂症进行自动分类，包括大脑成像。为此目的研究了高级学习技术，并取得了有希望的结果。这篇综述提供了一个关于使用 EEG 数据进行精神分裂症分类的最新基于机器学习方法的概述，讨论了它们的潜力和局限性。这篇综述旨在为未来基于 EEG 的有效模型的发展提供一个起点，这些模型可能会预测精神分裂症的发病，识别精神病转化的高风险人群，或区分精神分裂症与其他疾病，从而促进更有效的早期干预。
