Real Time SVM for Health Monitoring System

In this paper, we propose a new health monitoring system (HMS) based on a new classification method consisting of the real time support vector machines (RTSVM). The new HMS denoted by RTSVM-MS deals with problems of monitoring systems in intensive care unit (ICU). The main aim of this new system is to considerably reduce the rate of false alarms and keep a high and stable level of sensitivity. 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. In addition, the thresholds set has to be modified when patients are getting better. 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 has the ability to generate an initial model that classifies states of patients to normal and abnormal (critical) using the LASVM. Then, it updates its model by considering the evolution in the states of patients using RTSVM. As a result, the new system gives what the medical staff wants as information and alarms relative to monitored patient.

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