Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.

Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.

A electrocardiogram (EKG) lead attachment with a visual aid device including a plurality of retractable leads, a housing member, a plurality of label designations, a plurality of label designation tags, a lead placement visual aid, and a plurality of color tags. The lead placement visual aid includes an anatomical image of a chest cavity including color coordinated label designations which are each positioned on the anatomical image at a location where a corresponding lead in the first set should be placed on the human body. The plurality of color tags are coordinated to match the colors of the color coordinated label designations in the visual aid. The color designations and tags are used to provide an easy to follow visual aid to an operator for placement of the leads on a human body.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.

A mobility augmentation system monitors a user's motor intent data and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

Systems and methods are described herein related to the evaluation and adjustment of left bundle branch (LBB) pacing therapy. Evaluation of the LBB pacing therapy may utilize electrical activity monitored from a plurality of external electrodes. The electrical activity may be used to provided one or more metrics of dispersion of surrogate cardiac electrical activation times, which may then be used to evaluate, and potentially adjust the LBB pacing therapy.

An example method is performed by an electrocardiogram (ECG) device and includes determining a number of lead wires of an ECG cable assembly that is attached to the ECG device. The method also includes receiving ECG signals using electrodes of the ECG cable assembly. Further, the method includes using the number of lead wires as a basis for selecting a live-lead view from among a first live-lead view and a second live-lead view. Still further, the method includes displaying a representation of the ECG signals in the selected live-lead view in accordance with the selection.

An example method is performed by an electrocardiogram (ECG) device and includes determining a number of lead wires of an ECG cable assembly that is attached to the ECG device. The method also includes receiving ECG signals using electrodes of the ECG cable assembly. Further, the method includes using the number of lead wires as a basis for selecting a live-lead view from among a first live-lead view and a second live-lead view. Still further, the method includes displaying a representation of the ECG signals in the selected live-lead view in accordance with the selection.

A new born temperature and heart monitor wrap apparatus and method includes a wrap where the wrap is configured such that a new born is substantially covered by the wrap when the wrap is applied to the new born. A heart monitor device is attached to the wrap and a temperature lead retainer is attached to the wrap. A substrate is provided where the wrap is removably connected to the substrate.