Health Monitoring System Based on Parallel-APPROX SVM

In this paper, we propose a new approach to deal with the high rate of false alarms generated by the health monitoring system (HMS) in intensive care units (ICU). We propose a new HMS based on a new classification method consisting of the parallel-Approx support vector machines (PASVM). The main aim of the new HMS denoted by PASVM-MS is to considerably reduce the rate of false alarms and to make a fast prediction in each new state of the patient. Besides, it overcomes the main issue of the existing HMS by proposing a classification model that considers the variation of the patient states over time. Also, the number of measured parameters have to be changed when patients are getting better by removing one or more variable each time. However, thresholds are stable and do not translate the states of patients over time, since all existing systems in ICU do not take into account of the patients’ states evolution. Our proposal is able to generate an initial model classifying states of patients to normal and abnormal (critical) using the PASVM. Then, it updates its model by considering the evolution in the states of patients using PASVM especially when we deleting one variable. As a result, the new system gives what the medical staff wants as information and alarms relative to monitored patient.