Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

Zhili Zhang,Guifang Liu,Guo Cheng,Gaixia Li

doi:10.1155/2021/7706782

Abstract

The study aimed to explore the relationship between cerebral ischemic stroke (CIS) and the patient’s limb movement through the blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) based on multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC). Specifically, 20 CIS patients were defined as the experimental group; another 20 healthy volunteers were defined as the control group. All subjects performed finger movement and verb association task. The performance of support vector machine (SVM) and MCRSVMC algorithm was compared and applied to functional magnetic resonance imaging (fMRI) of blood oxygen level in all subjects. The results showed that the average accuracy of MCRSVMC algorithm was significantly higher than that of support vector machine (86.75%, 65.84%; P < 0.05 ). The sensitivity of MCRSVMC algorithm was significantly higher than that of support vector machine (92.52%, 75.41%; P < 0.05 ). In addition, the specificity of MCRSVMC algorithm was significantly higher than that of support vector machine (86.39%, 68.24%; P < 0.05 ). When CIS patients performed finger exercise, the sensory motor areas on both sides were significantly activated, and the activated sensory motor areas on both sides were significantly bigger than the ipsilateral area. The activation rate of the left-sensory motor area (L-SM1) was 87.5%, the activation rate of the right-sensory motor area (R-SM1) was 25%, the activation rate of the left-side auxiliary motor area (L-SMA) was 62.5%, and the activation rate of the right-side auxiliary motor area (R-SMA) was 37.5%. In conclusion, the MCRSVMC algorithm proposed in this study is highly efficient and stable. BOLD-fMRI diagnosis of motor function in CIS patients is mainly related to compensation around the lesion, which occurs on the healthy side after recovery.

Highlights

Cerebral ischemic stroke (CIS) refers to brain tissue ischemia caused by cerebrovascular diseases, accompanied by neuronal necrosis arising from a series of complex ischemic cascade reactions [1]
The performance of support vector machine and multilevel clusteringevolutionary random support vector machine cluster (MCRSVMC) algorithm was compared. e number of base classifiers of both models was set to 400. e average accuracy of MCRSVMC algorithm (86.75%) was significantly higher than that of support vector machine (65.84%), and the difference was statistically significant (P < 0.05). e sensitivity of MCRSVMC algorithm (92.52%) was significantly higher than that of support vector machine (75.41%), and the difference was statistically significant (P < 0.05). e specificity of MCRSVMC algorithm (86.39%) was significantly higher than that of support vector machine (68.24%), and the difference was statistically significant (P < 0.05)
In the images processed by MCRSVMC algorithm, the areas of red and yellow gradient increased, indicating that MCRSVMC algorithm could significantly enhance the diagnosis of abnormal brain regions

Summary

Introduction

Cerebral ischemic stroke (CIS) refers to brain tissue ischemia caused by cerebrovascular diseases, accompanied by neuronal necrosis arising from a series of complex ischemic cascade reactions [1]. It is the most common type of cerebrovascular diseases, accounting for up to 87%, which seriously endangers people’s health [2]. It is characterized by high morbidity, high disability, high mortality, and high recurrence rate. CIS patients are most prone to motor dysfunction. The Scientific Programming high center loses control of the motor system, so that the motor reflex of the subcortical center is released, and the motor system is abnormal [6]

Methods

Results

Discussion

Conclusion