Department of Computing, Xi'an Jiaotong-Liverpool Univeristy, Suzhou 215000, China.
Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK.
Sensors (Basel). 2024 Jul 17;24(14):4634. doi: 10.3390/s24144634.
Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains.
A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection.
The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.
受“医疗 4.0”的启发，本研究旨在基于人工提取的特征（包括时域和频域的统计特征），降低传统 EEG 特征的维数。
使用四阶巴特沃斯滤波器和小波包变换，从 UNM 和 Iowa 数据集提取总共 22 个多尺度特征。基于单通道验证，从 59 个常见通道中选择了 29 个具有最高 R2 分数的通道。该通道选择方案在 UNM 数据集上进行验证，并在 Iowa 数据集上进行测试，以比较其在没有通道选择的情况下训练的模型的通用性。
实验结果表明，所提出的模型达到了 100%的最佳分类准确率。此外，通过基于 Iowa 数据集的样本外测试验证了通道选择方法的泛化能力。
使用单通道验证，我们提出了一种基于传统统计特征的通道选择方案，选择了 29 个通道。该方案显著降低了与帕金森病相关的 EEG 特征向量的维度，降低了 50%。值得注意的是，该方法在 UNM 和 Iowa 数据集上都表现出了相当好的分类性能。对于闭眼状态，最高分类准确率达到 100%，而对于睁眼状态，最高准确率达到 93.75%。
