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Algorithms & software for intelligent patient monitoring

University of British Columbia

In Canada, more deaths occur after a patient experiences an adverse event in a hospital than from breast cancer, motor vehicle accidents and AIDS combined. Current technology contributes to the problem, by adding greater complexity to the clinician's workload. There is still significant promise that computerized assistance can improve patient safety. A clinical monitoring expert system, incorporating algorithms and expert knowledge, could automatically diagnose problems and provide advice on how best to avoid hazard. This thesis investigates the design and performance of algorithms and software for an intelligent patient monitor, which forms the foundation of a clinical monitoring expert system. An algorithm has been developed for detecting electrocautery noise in the electrocardiogram (ECG) using wavelet analysis. Electrocautery noise can lead an expert system to make incorrect diagnoses. In 15 surgical cases spanning 38.5 hours of ECG data, we achieved a false positive rate of 0.71% and a false negative rate of 0.33%. While existing hardware approaches detect activation of the noise source without any ability to assess its impact on the measured ECG, our software approach detects the presence of noise in the signal itself. Furthermore, the software approach is cheaper and easier to implement in a clinical environment than existing hardware approaches. A software framework, called iAssist, has been developed for intelligent patient monitoring. The framework is extensible, flexible, scalable, and interoperable. It supports plugins to perform data acquisition, signal processing, graphical display, data storage, and output to external devices. iAssist currently incorporates the electrocautery noise detection algorithm as a plugin for artifact rejection, as well as two plugins to detect change point events in physiological trends. In 38 surgical cases, iAssist detected 868 events, of which clinicians rated more than 50% as clinically significant and less than 7% as artifacts. Clinicians found iAssist intuitive and easy to use.

Text

2011-02-17

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http://hdl.handle.net/2429/31437

Electrical and Computer Engineering

University of British Columbia

Graduate