Further system and methods associated there with can use a combination of big data, machine learning, and/or regression equations to make living care paths based on sensitivities, probability, and/or statistics, which increases the chances of a living care path being successful. System, in some embodiments, can also provide a visual representation of the treatment options and statistics to the patient and HCP. As configured, system can empower patients, making them more informed about their condition, expectations of recovery, and more confident in their HCP's recommended treatment measure.

Further system and methods associated there with can use a combination of big data, machine learning, and/or regression equations to make living care paths based on sensitivities, probability, and/or statistics, which increases the chances of a living care path being successful. System, in some embodiments, can also provide a visual representation of the treatment options and statistics to the patient and HCP. As configured, system can empower patients, making them more informed about their condition, expectations of recovery, and more confident in their HCP's recommended treatment measure.

A computer system or method may be provided for modulating content based on a person's brainwave data, including modifying presentation of digital content at at least one computing device. The content may also be modulated based on a set of rules maintained by or accessible to the computer system. The content may also be modulated based on user input, including through receipt of a presentation control command that may be processed by the computer system of the present invention to modify presentation of content. Content may also be shared with associated brain state information.

A computer system or method may be provided for modulating content based on a person's brainwave data, including modifying presentation of digital content at at least one computing device. The content may also be modulated based on a set of rules maintained by or accessible to the computer system. The content may also be modulated based on user input, including through receipt of a presentation control command that may be processed by the computer system of the present invention to modify presentation of content. Content may also be shared with associated brain state information.

The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more periods of time of cardiac data and motion data for a subject. The method may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.

A system for modeling neurological activity includes a computer having one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions are configured to receive electroencephalogram (“EEG”) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time; generate a graphical brain model representative of the brain; to convert the EEG data into a graphical EEG model representative of electrical activity; integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and communicate the integrated EEG and brain model to a display.

A system for modeling neurological activity includes a computer having one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions are configured to receive electroencephalogram (“EEG”) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time; generate a graphical brain model representative of the brain; to convert the EEG data into a graphical EEG model representative of electrical activity; integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and communicate the integrated EEG and brain model to a display.

The present disclosure generally relates to devices, systems, and methods for detecting, monitoring, predicting, and/or treating medical conditions (e.g., epileptic seizures) using one or more sensors configured to collect biomarker data from a human subject (e.g., vagal tone and/or physiological or other biomarkers).

The present disclosure generally relates to devices, systems, and methods for detecting, monitoring, predicting, and/or treating medical conditions (e.g., epileptic seizures) using one or more sensors configured to collect biomarker data from a human subject (e.g., vagal tone and/or physiological or other biomarkers).

The present disclosure relates to a sensor for monitoring the depth of consciousness of a patient. The sensor includes a plurality of light sources, light detectors, and in some embodiments, electrodes. In an embodiment, the sensor includes reusable and disposable portions.