Abstract: A system for analyzing noise in one or more electrophysiology studies is provided. The system includes at least one processor and at least one memory device. The memory device stores an application that adapts the at least one processor to: identify, based at least in part on a machine learning model, one or more noise fingerprints within data derived at least in part from one or more study signals acquired from one or more patients during the one or more electrophysiology studies.

Abstract: A method for integrating multiple data sources into an X-ray image referential includes generating an X-ray image of a subject. The method also includes collecting information from sensors of a catheter disposed within the subject. The method further includes generating the X-ray image referential on a coordinate system by merging a model of an anatomy and the collected information from the sensors of the catheter to the X-ray image, wherein the X-ray image referential includes a display of a trace of a catheter. In addition, the method includes displaying the X-ray image referential on a display.

Abstract: A method for determining the value of service knowledge is provided. In one embodiment, the method includes providing a database of service knowledge, including one or more service knowledge objects, corresponding to a system. The method may also include associating the service knowledge with a plurality of taxonomic service categories associated with respective service events. Further, the method may include establishing a value of the one or more service knowledge objects. Such value may be established through a comparison of a first and second expected maintenance costs for the system determined from the service knowledge without and with, respectively, the one or more service knowledge objects. Additionally, the method may include storing or outputting the established value. Various other methods, systems, and devices are also disclosed.

Abstract: A patient treatment method is provided. In one embodiment, the method includes inputting physiological data of a patient into a computer model that is configured to simulate a physiological activity of the patient. The method may also include operating the computer model for each of a plurality of potential treatment options to generate a plurality of personalized modeled results based at least in part on simulated effects of each potential treatment option on the physiological activity. Additionally, the method may include selecting an optimal treatment option from the plurality of potential treatment options based at least in part on the plurality of personalized modeled results, developing a personalized treatment plan comprising the optimal treatment option, and treating the patient based at least in part on the personalized treatment plan. Other methods, systems, and computer-readable media are also disclosed.