ICU False Alarm Identification Based on Convolution Neural Network

Abstract

Background: In intensive care unit(ICU), excessive false alarms burden medical staff greatly, and cause medical resource waste as well. In order to alleviate false alarms in ICU, we constructed models for classification using convolutional neural networks, which can deal directly with time series and avoid extracting features manually. Results: Combining with grouping strategy, we tried two basic network structures, i.e. DGCN and EDGCN. After that, based on EDGCN, which was proved better, ensembling networks were also constructed to elevate the performance further. Considering of the limited sample size, different data expansions were also experimented. Finally, we tested our model in the online sandbox, and got a score of 78.14. Conclusions: Although the performance is slightly lower than the best scores that have been reported, our models are end-to-end, through which the original time series can be automatically mapped into a binary output, without manually feature extraction. In addition, our method innovatively uses grouped convolution to make full use of the information in multi-channel signals. In the end, we also discussed the potential solutions to further elevate performances.