The present disclosure describes various systems and methods of modifying a display to automatically visually trace an abnormality associated with a physiological signal received from a patient (i.e., subject). In particular aspects, the systems and methods described herein utilize three-dimensional display outputs of physiological signals that allow for the immediate differentiation between normal portions of the physiological signal and abnormal portions of the physiological signal.

An monitor includes a host and a first display communicatively connected to the host. In a display screen switching method for the monitor, it is detected that a second display is connected, the second display is provided independently of the monitor. It is detected that a display screen switching instruction is received and a display file corresponding to configuration parameters of the second display is read, the display file includes one or more physiological parameters to be displayed, an interface layout and interface elements. Data of the one or more physiological parameters are acquired according to the display file and generating frame data for representing pixel values of pixels on a display interface. The frame data are output to the second display to display data of the one or more physiological parameters.

A method includes detecting, by an implantable medical device (IMD), attachment to the IMD of at least one implantable medical lead with at least one electrode; and triggering by the IMD, based on the detecting of the attachment to the IMD of the at least one medical lead, a device test sequence in which the IMD performs the following qualification tests over an evaluation period: detecting an impedance for at least one electrical path that includes the at least one electrode to determine a connection status of the IMD to the at least one electrode; and comparing EGM (electrogram) amplitudes of the patient over an EGM test period against a predetermined threshold.

A method includes detecting, by an implantable medical device (IMD), attachment to the IMD of at least one implantable medical lead with at least one electrode; and triggering by the IMD, based on the detecting of the attachment to the IMD of the at least one medical lead, a device test sequence in which the IMD performs the following qualification tests over an evaluation period: detecting an impedance for at least one electrical path that includes the at least one electrode to determine a connection status of the IMD to the at least one electrode; and comparing EGM (electrogram) amplitudes of the patient over an EGM test period against a predetermined threshold.

The present disclosure encompasses an “artifact score” derived from the signal characteristics of an acquired 12-lead ECG, (2) a “patient context score” derived from key elements of the patient's history, presentation, and pre-hospital emergency care, and (3) techniques for integrating these scores into an emergency medical care system.

The present disclosure encompasses an “artifact score” derived from the signal characteristics of an acquired 12-lead ECG, (2) a “patient context score” derived from key elements of the patient's history, presentation, and pre-hospital emergency care, and (3) techniques for integrating these scores into an emergency medical care system.

A method enables analysis of (e.g. bioelectric) signals acquired by measurement. The method provides N signals U for an observation space and each has a time- and space-dependent signal characteristic U. Digitized signals for a time period T have M time points and define an M×N matrix with M tuples of N signal values each. Signal values acquired at time t form an N-tuple Ū.sub.t=(U.sub.1, . . . , U.sub.N).sub.t in a signal space. The method acquires all combinations of k tuples from the M tuples, and calculates distances between all tuples. Distance values are calculated and define edge lengths of a (k−1) simplex (SIM) with one simplex assigned to each combination of k time points. Quantity characteristics of the simplex (SIM) are encoded into color values (COL), and displays the colors in a combinatorial time lattice (CTL). Each lattice point (GP) is displayed with the color encoded for the assigned simplex.

A method enables analysis of (e.g. bioelectric) signals acquired by measurement. The method provides N signals U for an observation space and each has a time- and space-dependent signal characteristic U. Digitized signals for a time period T have M time points and define an M×N matrix with M tuples of N signal values each. Signal values acquired at time t form an N-tuple Ū.sub.t=(U.sub.1, . . . , U.sub.N).sub.t in a signal space. The method acquires all combinations of k tuples from the M tuples, and calculates distances between all tuples. Distance values are calculated and define edge lengths of a (k−1) simplex (SIM) with one simplex assigned to each combination of k time points. Quantity characteristics of the simplex (SIM) are encoded into color values (COL), and displays the colors in a combinatorial time lattice (CTL). Each lattice point (GP) is displayed with the color encoded for the assigned simplex.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.