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. 2021 Jun 30:15:684469.
doi: 10.3389/fnins.2021.684469. eCollection 2021.

Monitoring and Prognostic Analysis of Severe Cerebrovascular Diseases Based on Multi-Scale Dynamic Brain Imaging

Suting Zhong  1 , Kai Sun  2 , Xiaobing Zuo  1 , Aihong Chen  1
Affiliations

Monitoring and Prognostic Analysis of Severe Cerebrovascular Diseases Based on Multi-Scale Dynamic Brain Imaging

Suting Zhong et al. Front Neurosci. .

Abstract

Severe cerebrovascular disease is an acute cerebrovascular event that causes severe neurological damage in patients, and is often accompanied by severe dysfunction of multiple systems such as breathing and circulation. Patients with severe cerebrovascular disease are in critical condition, have many complications, and are prone to deterioration of neurological function. Therefore, they need closer monitoring and treatment. The treatment strategy in the acute phase directly determines the prognosis of the patient. The case of this article selected 90 patients with severe cerebrovascular disease who were hospitalized in four wards of the Department of Neurology and the Department of Critical Care Medicine in a university hospital. The included cases were in accordance with the guidelines for the prevention and treatment of cerebrovascular diseases. Patients with cerebral infarction are given routine treatments such as improving cerebral circulation, protecting nutrient brain cells, dehydration, and anti-platelet; patients with cerebral hemorrhage are treated within the corresponding safe time window. We use Statistical Product and Service Solutions (SPSS) Statistics21 software to perform statistical analysis on the results. Based on the study of the feature extraction process of convolutional neural network, according to the hierarchical principle of convolutional neural network, a backbone neural network MF (Multi-Features)-Dense Net that can realize the fusion, and extraction of multi-scale features is designed. The network combines the characteristics of densely connected network and feature pyramid network structure, and combines strong feature extraction ability, high robustness and relatively small parameter amount. An end-to-end monitoring algorithm for severe cerebrovascular diseases based on MF-Dense Net is proposed. In the experiment, the algorithm showed high monitoring accuracy, and at the same time reached the speed of real-time monitoring on the experimental platform. An improved spatial pyramid pooling structure is designed to strengthen the network's ability to merge and extract local features at the same level and at multiple scales, which can further improve the accuracy of algorithm monitoring by paying a small amount of additional computational cost. At the same time, a method is designed to strengthen the use of low-level features by improving the network structure, which improves the algorithm's monitoring performance on small-scale severe cerebrovascular diseases. For patients with severe cerebrovascular disease in general, APACHEII1, APACHEII2, APACHEII3 and the trend of APACHEII score change are divided into high-risk group and low-risk group. The overall severe cerebrovascular disease, severe cerebral hemorrhage and severe cerebral infarction are analyzed, respectively. The differences are statistically significant.

Keywords: ApacheII score; dynamic brain imaging; monitoring and prognostic analysis; multi-scale features; severe cerebrovascular disease.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flow chart of emergency care for patients with severe cerebrovascular disease.
FIGURE 2
FIGURE 2
Schematic diagram of information fusion control of human body intention EEG and EMG multi-source sensing system.
FIGURE 3
FIGURE 3
Schematic diagram of dense connection block structure.
FIGURE 4
FIGURE 4
Densely connected network structure.
FIGURE 5
FIGURE 5
Implementation method of feature pyramid based on MF-Dense Net.
FIGURE 6
FIGURE 6
Schematic diagram of reconstruction operation.
FIGURE 7
FIGURE 7
ROC curve of APACHEII1, APACHEII2, and APACHEII3 predicting the risk of death.
FIGURE 8
FIGURE 8
Image performance of severe cerebrovascular disease based on multi-scale dynamic brain imaging. (A) Imaging manifestation of severe cerebrovascular disease in 18 h brain imaging; (B) Imaging manifestation of severe cerebrovascular disease in 36 h brain imaging; (C) Imaging manifestation of severe cerebrovascular disease in 54 h brain imaging; (D) Imaging manifestation of severe cerebrovascular disease in 72 h brain imaging.
FIGURE 9
FIGURE 9
APAHCEII1 score predicts overall mortality and actual mortality.
FIGURE 10
FIGURE 10
APAHCEII2 score predicts overall mortality and actual mortality.
FIGURE 11
FIGURE 11
The relationship between APAHCEII3 score predicting overall mortality rate and actual mortality rate.
FIGURE 12
FIGURE 12
The ROC curve of APACHEII1, APACHEII2, APACHEII3 predicting the risk of death from severe cerebral hemorrhage.
FIGURE 13
FIGURE 13
The relationship between APACHEII1 and the mortality of patients with severe cerebral hemorrhage.
FIGURE 14
FIGURE 14
APAHCEII2 predicts the relationship between mortality and actual mortality in patients with severe cerebral hemorrhage.
FIGURE 15
FIGURE 15
APAHCEII3 predicts the relationship between mortality and actual mortality in patients with cerebral hemorrhage.
FIGURE 16
FIGURE 16
Comparison of evaluation methods and prognostic logistic regression analysis.
See this image and copyright information in PMC

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