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Interpreting ECG data by integrating statistical and artificial intelligence tools

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2 Author(s)
E. Tatara ; Dept. of Chem. & Environ. Eng., Illinois Inst. of Technol., Chicago, IL, USA ; A. Cinar

The use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and interpret high-frequency physiological data such as the electrocardiogram is investigated. The development of a methodology and its associated tools for real-time patient monitoring and diagnosis is accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. Data interpretation and classification is performed by integrating statistical classification methods and knowledge-based techniques with a graphical user interface that provides quick access to the analysis results as well as the original data. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the healthcare provider

Published in:

IEEE Engineering in Medicine and Biology Magazine  (Volume:21 ,  Issue: 1 )