By Topic

Interpreting ECG data by integrating statistical and artificial intelligence tools

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

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:

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