 This paper proposes a novel approach to predicting infantile spasms. The authors first analyze the electrical activity of the brain during different stages of the condition, including interictal, preictal, postictal, and seizure phases. They then use a deep learning model to identify patterns in the electrical activity that may indicate when a seizure will occur. Finally, they validate their findings through a clinical trial involving 25 patients with infantile spasms. The results demonstrate that the proposed model can accurately predict when a seizure will occur with an average accuracy of 9.78% for 0.46%, 5.46% for specificity, sensitivity, and recall, respectively. This research provides valuable insight into how infantile spasms develop and could lead to more effective treatments for the condition. This article was authored by Run Zhang, Zhou Wenqiao, Yuan Mengfeng, and others. We are article.tv. Links in the description below.