Diagnosis support on cardio-vascular signal monitoring by using cluster computing

The support for remote data processing and analysis is a necessary requirement in future healthcare system. Likewise interconnect/manage medical devices and distributed processing of data collected through these devices are crucial processes for supporting personalised healthcare systems. This work introduces our research efforts to build a monitoring application hosted on a cluster computing environment supporting personalised healthcare systems (pHealth). The application is based on a novel distributed clustering algorithm that is used for medical diagnosis of cardio-vascular signals. The algorithm collects different statistics from the cardiac signals and uses these statistics to build a distributed clustering model automatically. The resulting model can be used for diagnosis purposes of cardiac signals. A cardio-vascular monitoring scenario in cluster computing environment is presented and experimental results are described to demonstrate the accuracy of cardio-vascular signals diagnosis. Advantages of using data analysis techniques and cluster computing in medical diagnosis also discussed in this work.