This study proposes a deep learning model that effectively suppresses the
false alarms in the intensive care units (ICUs) without ignoring the true
alarms using single- and multimodal biosignals. Most of the current work in the
literature are either rule-based methods, requiring prior knowledge of
arrhythmia analysis to build rules, or classical machine learning approaches,
depending on hand-engineered features. In this work, we apply convolutional
neural networks to automatically extract time-invariant features, an attention
mechanism to put more emphasis on the important regions of the input segmented
signal(s) that are more likely to contribute to an alarm, and long short-term
memory units to capture the temporal information presented in the signal
segments. We trained our method efficiently using a two-step training algorithm
(i.e., pre-training and fine-tuning the proposed network) on the dataset
provided by the PhysioNet computing in cardiology challenge 2015. The
evaluation results demonstrate that the proposed method obtains better results
compared to other existing algorithms for the false alarm reduction task in
ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of
92.05% for the alarm classification, considering three different signals. In
addition, our experiments for 5 separate alarm types leads significant results,
where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a
specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular
Tachycardia arrhythmia