轻度认知障碍(MCI)的早期诊断对于及时制定治疗计划至关重要。随着可穿戴技术的最新进展,在日常生活中,人们的兴趣越来越转向使用可穿戴脑电图(EEG)设备对MCI进行计算机辅助自我诊断。然而,到目前为止,还没有研究在考虑可穿戴EEG设备的设计因素的同时,研究了有效诊断MCI的最佳电极配置。在这项研究中,我们旨在确定可穿戴EEG设备的最佳通道配置,用于MCI的计算机辅助诊断.
我们采用了从21名MCI患者和21名健康对照受试者收集的EEG数据集。在评估了两种电极的所有可能电极配置的分类精度之后,four-,six-,和使用支持向量机的八电极条件,针对每种电极条件,建议提供最高诊断准确度的最佳电极配置.
对于最佳的两个-,达到了最高的分类精度,分别为74.04%±4.82、82.43%±6.14、86.28%±2.81和86.85%±4.97,four-,six-,和八电极配置,分别,这证明了使用有限数量的EEG电极对MCI进行精确的基于机器学习的诊断的可能性。此外,对EEG数据集的进一步模拟显示,最佳电极配置比具有相同数量电极的商用EEG设备具有更高的分类精度,这表明了基于临床EEG数据集的可穿戴EEG设备的电极配置优化的重要性。
这项研究强调了电极配置的优化,假设可穿戴EEG设备可以潜在地用于MCI的日常生活监测,是必要的,以提高性能和便携性。
Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.
We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.
The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.
This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.