Perez-Valero Eduardo, Lopez-Gordo Miguel Ángel, Gutiérrez Christian Morillas, Carrera-Muñoz Ismael, Vílchez-Carrillo Rosa M
Department of Computer Architecture and Technology, University of Granada, Spain; Brain-Computer Interfaces Laboratory, Research Centre for Information and Communications Technologies, University of Granada, Spain.
Department of Signal Theory, Telematics and Communications, University of Granada, Spain; Brain-Computer Interfaces Laboratory, Research Centre for Information and Communications Technologies, University of Granada, Spain.
Comput Methods Programs Biomed. 2022 Jun;220:106841. doi: 10.1016/j.cmpb.2022.106841. Epub 2022 Apr 27.
Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.
早期检测对于控制阿尔茨海默病（AD）的进展和延缓认知衰退至关重要。传统的医学检查方法，如磁共振成像，成本高昂，等待名单长，且需要复杂的分析。另外，在过去几年中，研究人员已成功评估了基于机器学习和脑电图（EEG）的AD检测方法。尽管如此，这些方法常常依赖人工处理，或涉及不可携带的EEG硬件。就自动诊断而言，这些方面并不理想，因为它们需要额外的人员且阻碍了便携性。在这项工作中，我们报告了基于一个使用16通道的商用EEG采集系统的自驱动AD多类别判别方法的初步评估。为此，我们记录了三组参与者的EEG：轻度AD患者、轻度认知障碍（MCI）非AD患者和对照组，并且我们实施了一个自驱动分析流程来区分这三组。首先，我们将自动伪迹去除算法应用于EEG记录。然后，我们从预处理后的时段中提取功率、熵和复杂度特征。最后，我们通过留一法交叉验证，使用多层感知器评估了一个多类别分类问题。我们获得的初步结果与文献中的最佳结果相当（F1分数为0.88），这表明AD有可能通过基于商用EEG和机器学习的自驱动方法进行检测。我们相信这项工作以及进一步的研究可能有助于为在单次会诊中检测AD打开大门，从而降低与AD筛查相关的成本，并有可能推进医学治疗。
