Interpreting ECG data by integrating statistical and artificial intelligence tools

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