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Detection and visualization of subtle changes in physiologic data can be very difficult considering the often repetitive nature of the data. A new method for detection and visualization of these changes is presented that is particularly well suited for the large volumes of data encountered in electrocardiogram (ECG) analysis and critical care monitoring. This technique provides an effective framework for automated detection of signal abnormalities caused by changes in the underlying physiologydoi:10.6084/m9.figshare.3829764 fatcat:bcjzqfgwy5djjisyjr552js6wa