A biopotential measurement device including a plurality of sensing electrodes and a controller is described. The plurality of sensing electrodes is adapted to measure one or more biopotential signals when the plurality of sensing electrodes is worn. The controller is coupled to the plurality of sensing electrodes and memory that stores instructions that when executed by the controller cause the biopotential measurement device to perform operations. The operations include collecting electrical signals over a first time period, each of the electrical signals associated with at least one of the plurality of sensing electrodes, mixing the electrical signals to generate a biopotential dataset that includes permutations of the electrical signals, and identifying a target biopotential signal included in the one or more biopotential signals based, at least in part, on the biopotential dataset.

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.

A device and system allows for simplified connection, disconnection, and mitigation of signal interference for electrophysiological testing. One or more electrophysiological electrodes are attached to the patient. These electrodes are connected to leads which are bundled based on region with a single connector associated with each bundle. The bundles are individually keyed and colored for ease of connection and disconnection. Leads within a bundle are organized through the use of a retainer. Electrode bundles are connected to a junction box within proximity to the patient. A single cable connects the junction box to an electrophysiological testing and monitoring interface. A system for visualizing lead bundle data, organizing individual electrode data by the lead bundles that they are associated, facilitates a workflow allowing for rapid and simple channel identification in association with specific electrode regions.

An object is to provide an electrode catheter excellent in terms of torque transmission compared with a hitherto publicly known electrode catheter. An electrode catheter of the present invention comprises a catheter shaft 10; ring-shaped electrodes 201 to 210; and leads 301 to 310 of the ring-shaped electrodes 201 to 210, in which the catheter shaft 10 is constituted by an inside tube 11 that has a guide-wire lumen 11L and an outside tube 13 that forms a lumen 12L into which the leads 301 to 310 are inserted, the outside tube 13 being configured by a braided tube reinforced by a braid 135 made of a resin throughout the entire length thereof; in which side holes 15 are formed in the tube wall of the outside tube 13 at the distal end part 101 of the catheter shaft 10 in correspondence to the attachment positions of the ring-shaped electrodes 201 to 210; and in which the leads 301 to 310 enter the lumen 12L through the side holes 15 and extend in said lumen 12L.

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 are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

In accordance with some embodiments, systems, apparatus, interfaces, methods, and articles of manufacture are provided for ascertaining aspects of a user, such as the user's identity, competence, health, and state of mind. In various embodiments, data is captured about a user via a headset worn by the user. Based on the data, a determination may be made about an aspect of the user, and the user may accordingly be granted or denied access to a resource.

A system for VT localization comprises an electrocardiograph (ECG) device for detecting electrical signals of a ventricular fibrillation event in a patient and producing ECG data, a localizer, and a display device. The localizer comprises a memory and a processor, the memory storing a model and one or more parameters of the model. The memory further stores instructions that when executed by the processor cause the processor to receive the ECG data, and generate a determination indicating whether a detected VT signal arose from an endocardial surface or epicardial surface based on signal averaged waveform for each of the leads. The display device is configured to output the determination, which can also be communicated to follow-on provider systems.

A system for VT localization comprises an electrocardiograph (ECG) device for detecting electrical signals of a ventricular fibrillation event in a patient and producing ECG data, a localizer, and a display device. The localizer comprises a memory and a processor, the memory storing a model and one or more parameters of the model. The memory further stores instructions that when executed by the processor cause the processor to receive the ECG data, and generate a determination indicating whether a detected VT signal arose from an endocardial surface or epicardial surface based on signal averaged waveform for each of the leads. The display device is configured to output the determination, which can also be communicated to follow-on provider systems.

An emergency cardiac and electrocardiogram (ECG) electrode placement device is disclosed herein. The emergency cardiac and electrocardiogram (ECG) electrode placement device incorporates electrical conducting materials and elastic material into a pad that is applied to a chest wall of a patient, which places multiple electrodes in the appropriate anatomic locations on the patient to quickly obtain an ECG in a pre-hospital setting.