Abstract
New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.
Highlights
As healthcare costs are increasing and the world population is ageing [1], the need for monitoring patients in their home environment is growing
Our work focuses on a real time processing health system that addresses the needs of patients with epilepsy
We present a health monitoring system, which by utilizing a Data Stream Management System (DSMS) is able to monitor and process a large continuous flow of physiological data in a real time and efficient manner
Summary
As healthcare costs are increasing and the world population is ageing [1], the need for monitoring patients in their home environment is growing. Information and communication technologies are expected to respond to this problem by providing personalized, low-cost, citizen centered healthcare services [3]. Recent advances in sensor technology and microelectronics have enabled the long term monitoring and management of chronic disease patients and detect urgent or emergent events. Alert services are provided in case of possible imminent health threatening conditions. To achieve these goals, these systems process the data flow continuously and are expected to achieve low latency and high throughput. The data processing must keep up with data ingest rates, while providing high quality results as quickly as possible
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