Abstract: Systems, apparatus, instructions, and methods for configuring and controlling medical equipment using a digital assistant are disclosed. An example digital assistant apparatus includes at least one processor to execute the instructions to at least: form a digital model of at least one aspect of a medical equipment; transform the model into a configuration for the medical equipment to perform a task; process input to adjust the configuration; provide the configuration to the medical equipment; monitor execution of the task by the medical equipment; and adjust the configuration of the medical equipment based on the monitored execution.

Abstract: Various methods and systems are provided for analyzing an electrocardiogram (ECG) in real-time using machine learning to identify heartbeats, calculate a cycle length for each heartbeat, and display the cycle length for each heartbeat at a user interface. Waveform morphology of ECG data is continuously learned to identify recurrent signals and generate templates based on recurrent signals, to which ECG data is compared to identify and display heartbeats. Generated templates are continuously updated to reflect changing waveform morphologies.

Abstract: Systems, apparatus, instructions, and methods for configuring and controlling medical equipment using a digital assistant are disclosed. An example digital assistant apparatus includes at least one processor to execute the instructions to at least: form a digital model of at least one aspect of a medical equipment; transform the model into a configuration for the medical equipment to perform a task; process input to adjust the configuration; provide the configuration to the medical equipment; monitor execution of the task by the medical equipment; and adjust the configuration of the medical equipment based on the monitored execution.