This paper describes a data-driven Decision Support System for Electroencephalography (EEG) signals acquisition, and parallel elaboration based on the integration of an Ambient Intelligent (Ami) [1] platform and a GRID enabled Infrastructure. The paper explores the analysis and design of the environment, the real-time data acquisition, the integration of the acquired data in dedicated EHR, and the EEG processing through parallel analysis algorithm available on the GRID infrastructure. After an overview of background concepts, the paper presents a brief description of the environment architecture, and a detailed analysis of the EEG algorithm. The challenge of the work presented is to effectively show how medical data can be shared and processed by exploiting the resources and capabilities of both the AmI platform and the GRID infrastructure. This particular Decision Support System, shows how it is possible to improve patient safety, quality of care, and efficiency in healthcare delivery.
A Decision Support System for the Acquisition and Elaboration of EEG Signals: The AmI-GRID Environment
BARCARO, UMBERTO;RIGHI, MARCO;STARITA, ANTONINA;
2007-01-01
Abstract
This paper describes a data-driven Decision Support System for Electroencephalography (EEG) signals acquisition, and parallel elaboration based on the integration of an Ambient Intelligent (Ami) [1] platform and a GRID enabled Infrastructure. The paper explores the analysis and design of the environment, the real-time data acquisition, the integration of the acquired data in dedicated EHR, and the EEG processing through parallel analysis algorithm available on the GRID infrastructure. After an overview of background concepts, the paper presents a brief description of the environment architecture, and a detailed analysis of the EEG algorithm. The challenge of the work presented is to effectively show how medical data can be shared and processed by exploiting the resources and capabilities of both the AmI platform and the GRID infrastructure. This particular Decision Support System, shows how it is possible to improve patient safety, quality of care, and efficiency in healthcare delivery.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
