STformer: Spatial-Temporal Transformer for early Warning of Unplanned Extubation in ICU

Patients' Unplanned Extubation (UEX) is dangerous in the intensive care units (ICU), it is necessary to make early warning of UEX. However, the low fine-grained action of UEX and complexity of ICU environment make early warning a great challenging by using RGB video data. To address this issue, we propose a novel lightweight Spatial-Temporal Transformer (STformer) for early warning of patients' UEX action in the ICU. Specially, the SlowFast is used to extract patient's spatial-temporal features initially. Then, in order to improve the representation of features, we introduce spatial attention to enhance the spatial representation of fine-grained actions, and capture the long-term dependency of motions through temporal attention. Finally, a spatial-temporal joint attention is used to reconstruct and strengthen spatial and temporal information. Experiment results illustrate state-of-the-art performance of our STformer on ICU monitory datasets. While ensuring the accuracy of early warning, the computational complexity of STformer are also light.