Automated patient monitoring and diagnosis assistance by integrating statistical and artificial intelligence tools

Automated data collection from patients has created new challenges for health care professionals in their efforts to extract useful information from raw data. Online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence (AI) tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (ECG) are investigated. The development of a methodology and associated tools for real-time patient data monitoring and diagnosis assistance was accomplished by using MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various DSP and statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis assistance, and to report these findings to medical personnel.