Smart alarming scheme for ICU using neural networks

In this work a new scheme for intelligent alarming is presented. The idea is that in order for an alarming scheme to be able to be efficient, the definitions of normal, abnormal and intermediate state have to be changed many times on an hour to hour basis, since in ICU the patient state can change dramatically from day to day. In order to do so, unsupervised and supervised learning systems need to be incorporated that can be trained fast and reliably by the medical personnel. Thus the need for a system that can be trained fast and the existence of a user-friendly MMI where the doctor shall be able to modulate the boundaries between normal, abnormal and intermediate values according to the patient's condition is imperative. In this paper, this approach is implemented, using neural networks (NN) for training and learning, and a user friendly MMI using colours and 2-D phase planes of parameters monitored in ICU are used to achieve more efficient alarming schemes.