| dc.description.abstract | Post-stroke epilepsy is one of the most common neurological complications following a stroke, significantly impacting patients′ quality of life and long-term prognosis. Despite extensive researches, accurately predicting and preventing post-stroke epilepsy remains challenging. This study aims to comprehensively analyze the incidence, risk factors, and clinical features of post-stroke epilepsy to develop a predictive model that helps healthcare professionals better identify high-risk patients and intervene early. Through a retrospective analysis of numerous patients with post-stroke epilepsy, we found significant correlations between factors such as age, stroke type, and a history of epilepsy with the occurrence of post-stroke epilepsy. We developed a predictive model based on these factors, and its accuracy has been thoroughly validated. This model not only aids in predicting the risk of epilepsy in individual patients but also provides guidance for clinicians, enhancing the prevention and management of post-stroke epilepsy.
This study adopts a deep neural network structure with the aim of establishing a predictive model to assist in the clinical prediction of post-stroke epilepsy. The study uses neuroimaging data from a total of 132 patients from 2012 to 2017, divided into four experimental groups: post-stroke epilepsy vs. no post-stroke epilepsy, early-onset post-stroke epilepsy vs. no post-stroke epilepsy, late-onset post-stroke epilepsy vs. no post-stroke epilepsy, and early-onset post-stroke epilepsy vs. late-onset post-stroke epilepsy. The model predictions are performed through cross-validation, and the results of each group of experiments are obtained.
For predicting post-stroke epilepsy, the accuracy is 70.44%±8.78% (68.94-71.94), sensitivity is 60.44%±20.19% (57.00-63.88), precision is 77.68%±13.77% (75.33-80.03), and AUC reaches 70.53%±8.63% (69.06-72.00). For predicting early-onset post-stroke epilepsy, the accuracy is 79.20%±0.36% (79.13-79.27), sensitivity is 71.21%±6.56% (69.93-72.49), precision is 69.49%±0.49% (69.40-69.57), and AUC reaches 79.72%±4.17% (78.90-80.53). For predicting late-onset post-stroke epilepsy, the accuracy is 74.76%±1.13% (74.54-74.99), sensitivity is 66.77%±3.18% (66.13-67.40), precision is 61.17%±2.67% (60.64-61.70), and AUC reaches 79.02%±2.46% (78.53-79.51). Lastly, for predicting early-onset vs. late-onset post-stroke epilepsy, the accuracy is 81.82%±7.87% (80.47-83.16), sensitivity is 91.67%±8.34% (90.24-93.09), precision is 78.21%±6.6% (77.08-79.33), and AUC reaches 88.51%±5.43% (87.58-89.43).
The results demonstrate that our study, based on deep learning and neuroimaging, proposes a method for clinically predicting the occurrence of epilepsy after a stroke, aiding clinicians in assessing the risk of epilepsy in patients. | en_US |