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Abstract：
Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz\'s and Higuchi\'s fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier\'s performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice.
摘要：
有效治疗痴呆症需要及时发现轻度认知障碍（MCI）。本文介绍了一种多目标优化方法，用于选择EEG通道（和特征）以检测MCI。首先,使用变分模式分解（VMD）或离散小波变换（DWT）将来自每个通道的每个EEG信号分解为子带。然后使用以下度量之一从每个子带中提取特征：标准偏差，四分位数间距,频带功率,Teager的能量，Katz和Higuchi的分形维数，香农熵,确定熵，或阈值熵。使用不同的机器学习技术将MCI病例的特征与健康对照的特征进行分类。分类器的性能使用留一主题(LOSO)交叉验证(CV)进行验证。非支配排序遗传算法（NSGA）-II的设计目的是最小化EEG通道（或特征）的数量并最大化分类精度。使用公开的在线数据集评估性能，该数据集包含来自24位参与者记录的19个频道的EEG。结果表明，使用NSGA-II算法时，性能有了显着提高。通过只选择几个合适的脑电图通道，与使用所有19个通道相比，基于LOSOCV的结果显示显着改善。此外,结果表明，通过从不同通道中选择合适的特征可以进一步提高准确性。例如,通过结合VMD和Teager能量，使用所有通道获得的SVM精度为74.24%。有趣的是,当使用NSGA-II仅选择五个通道时，精度提高到91.56%。当只使用从7个通道中选择的8个功能时，精度进一步提高到95.28%。这表明，通过选择信息特征或通道，同时排除嘈杂或不相关的信息，噪音的影响降低，从而提高准确性。这些有希望的研究结果表明,通道和功能数量有限，MCI的准确诊断是可以实现的，这为其在临床实践中的应用打开了大门。
