State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China.
J Healthc Eng. 2021 Oct 27;2021:1834123. doi: 10.1155/2021/1834123. eCollection 2021.
The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus ( < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.
熵模型参数验证的瓶颈限制了该模型在现代功能成像技术中的应用，如静息态功能磁共振成像（rfMRI）。本研究开发了一种可以选择多尺度熵（MSE）模型参数的优化算法，通过有监督机器学习方法的分类率验证了优化算法在定位致痫半球方面的有效性。该研究收集了 20 例左侧或右侧海马结构有阳性指标（临床提示致痫半球的指标）的内侧颞叶癫痫患者的 rfMRI 数据，并对其进行预处理。然后，通过接受者操作特征（ROC）曲线和 ROC 曲线下面积值的敏感性分析对 MSE 模型中的三个参数进行了统计优化，并利用组间优化熵值的显著性来确认对致痫半球敏感的生物标记脑区。最后，将这些生物标记脑区的优化熵值作为特征向量输入支持向量机进行致痫半球分类，并进行交叉验证。通过优化熵值确认了 9 个生物标记脑区，其中内侧额上回和顶上回（P<0.01）。平均分类准确率大于 90%。可以得出结论，将优化的 MSE 模型与机器学习模型相结合，可以通过 rfMRI 准确地确定致痫半球。借助 5G 通信强大的信息交互能力，可以将需要计算能力的癫痫侧定位算法集成到云平台中。需求方只需将患者数据上传到服务平台，即可实现癫痫的术前评估。
