Incremental real time support vector machines for health monitoring system

In this paper, we propose a new approach to deal with the high rate of false alarms generated by the health monitoring system (HMS). It consists of an intelligent alarm algorithm based on a new version of the support vector machines (SVM). Actually, medical staff face many issues when using the current HMS in intensive care unit (ICU). This latter generates a large number of alarms due to the exceed of the thresholds of the measured physiological parameters. Care-givers should set the thresholds each time there is a new monitored patient or when a new physiological parameter is added. However, in real situations, the default thresholds are often used for all monitored patients which causes a high level of false (and irrelevant) alarms. In order to overcome the ICU issues and improve the current system, we propose the incremental real-time SVM (IRTSVM) for health monitoring system. This new system can deal with data changing over time especially when the state of a patient is not stable. It also deals with incremental aspect by adding new monitoring parameters. Empirical study shows the efficiency of our new system when using real-world databases by providing important results. The new system guarantees the reduction of the rate of false alarms. Besides, it keeps a high level of sensitivity and detects relevant alarms. As a result, it provides doctors by all their needs which makes their decisions more accurate.