Medicine-Engineering Interdisciplinary
DING Lihui1, 2(丁黎辉), FU Lijun1, 3 (付立军), YANG Guang4(杨光), WAN Lin4, 5 (万林), CHANG Zhijun7(常志军)
Behavioral scoring based on clinical observations remains the gold standard for screening, diagnosing,and evaluating infantile epileptic spasm syndrome (IESS). The accurate identification of seizures is crucial for clinical diagnosis and assessment. In this study, we propose an innovative seizure detection method based on video feature recognition of patient spasms. To capture the temporal characteristics of the spasm behavior presented in the videos effectively, we incorporate asymmetric convolution and convolution–batch normalization–ReLU (CBR) modules. Specifically within the 3D-ResNet residual blocks, we split the larger convolutional kernels into two asymmetric 3D convolutional kernels. These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features, both horizontally and vertically. In addition, we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently. To improve the generalization ability, we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements. We train and evaluate our method using the PLA IESS-VIDEO dataset, achieving an average seizure recognition accuracy of 90.59%, precision of 90.94%, and recall of 87.64%. To validate its generalization capability further, we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers. The final test results demonstrate that our method achieved a recall of 0.647 6, surpassing the average level achieved by human experts (0.559 5), while attaining a high F1-score of 0.721 9. These findings have substantial significance for the long-term assessment of patients with IESS.
