2026-10-13T00:21:21Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33972457 2026-09-23T09:47:34Z category_15 category_16 category_734 portal_87 item_type_3 month_year_09_2026

Deep learning-based classification of cortical and subcortical ischemic stroke infarction using fNIRS signals Boyang Xu (4568074) Hong Xu (71477) Huabo Liu (578605) Yuhui Chen (150020) Neuroscience Physiology Biological Sciences not elsewhere classified Cortical and subcortical infarctions functional near-Infrared spectroscopy explainable artificial intelligence time-Series classification <p>Distinguishing cortical from subcortical ischemic stroke may help guide management. We developed DBT-Net, which combines a DHR residual module, BiLSTM, and Transformer, to classify stroke subtypes from fNIRS signals. The model was evaluated by patient-wise stratified five-fold cross-validation in 175 participants under verbal fluency, right-hand grasp, and resting-state conditions. Accuracy was 0.886 ± 0.055, 0.913 ± 0.062, and 0.878 ± 0.046, with AUCs of 0.920 ± 0.072, 0.912 ± 0.069, and 0.900 ± 0.034, respectively. SHAP analysis highlighted cortical channels contributing most to the predictions.</p> 2026-09-23T09:47:34Z Dataset Dataset 10.6084/m9.figshare.33972457.v1 https://figshare.com/articles/dataset/Deep_learning-based_classification_of_cortical_and_subcortical_ischemic_stroke_infarction_using_fNIRS_signals/33972457 CC BY 4.0