  Contemporary patient surveillance systems have streamlined central
surveillance into the electronic health record interface. They are able to
process the sheer volume of patient data by adopting machine learning
approaches. However, these systems are not suitable for implementation in many
hospitals, mostly in developing countries, with limited human, financial, and
technological resources. Through conducting thorough research on intensive care
facilities, we designed a novel central patient monitoring system and in this
paper, we describe the working prototype of our system. The proposed prototype
comprises of inexpensive peripherals and simplistic user interface. Our central
patient monitoring system implements Kernel-based On-line Anomaly Detection
(KOAD) algorithm for emergency event signaling. By evaluating continuous
patient data, we show that the system is able to detect critical events in
real-time reliably and has low false alarm rate.
