Detecting False Arrhythmias Alarms in the ICU Using a Deep Learning Approach

In the Intensive Care Unit (ICU), false arrhythmia alarms become a constant disturbance that generates stress for both medical staff and patients. The high rate of these reports, coupled with the inability of monitoring systems to accurately discern between normal cardiac signals and arrhythmias, compromises the quality of care and puts patients' health at risk. The aim of this research was to identify false alarms using convolutional neural networks (CNNs) trained with data from the PhysioNet Computing in Cardiology Challenge 2015 database. In addition, we explored the effectiveness of data augmentation techniques in the training of the model; the performance evaluation metrics used included true positive rate (TPR), true negative rate (TNR), F1 score, area under the curve (AUC), and final score of the PhysioNet Computing in Cardiology Challenge 2015. The results suggest that the model achieved a Physionet score of 65.24. Additionally, they reveal that data augmentation techniques did not have a significant impact on false alarm identification.