The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.

A system for achieving optimal sensor placement and enhanced signal quality for monitoring maternal and fetal activities is disclosed. The system includes a monitoring device and a computing unit. The monitoring device is configured for monitoring maternal and fetal activities and providing guidance to the user via the computing unit upon detecting a feature of interest. The monitoring device includes a plurality of sensors, a data acquisition and transmission unit, one or more reference electrodes, and a ground electrode. Based on personal data acquired using the computing unit, the system utilizes a statistical or machine learning model which incorporates one or more subsets of the personal data to determine the optimal sensor placement close to the fetal heart position. Following sensor placement, the monitoring device performs a signal quality assessment and selects the optimal sensors to ensure reliable information on maternal and fetal activities is obtained.

A device including an array of electrodes generates one or more electrical signals from a user, extracts one or more noise signals, and generates one or more de-noised electrical signals upon processing the electrical signal(s) with the noise signal(s). The array of electrodes is coupled to a surface of the device, where the device also includes force sensors in mechanical communication with the surface for detecting user weight and other forces. The device can be configured to generate electrical signals from different subportions of the array of electrodes and to extract noise signals from different subportions of the array of electrodes, where the subportion(s) for electrical signal generation may or may not overlap with the subportion(s) of electrodes for noise signal extraction.

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user’s interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

Methods for capturing medical images associated with a patient using a mobile image-capturing device are disclosed. One example method includes accessing patient identifying information. A user may launch a scan application installed in the mobile image-capturing device by visually scanning a machine-readable optical label, which may also authenticate the user. Upon accessing the scan application, the scan application may be provided with the patient identifying information scanned from the machine-readable optical label, and one or more details of the patient based on the patient identifying information scanned from the machine-readable optical label may be displayed at an interface of the scan application. The scan application may capture medical images using an imaging equipment of the mobile image-capturing device, associate the captured images with the patient identifying information, and transmit the captured images with the patient identifying information to a storage location.

An apparatus for estimating bio-information is provided. According to an example embodiment, the apparatus for estimating bio-information includes: a pulse wave sensor including channels, and configured to measure pulse wave signals from an object at the channels; a force sensor configured to measure a contact force applied by the object to the pulse wave sensor; and a processor configured to determine correlations between the pulse wave signals of the channels, and to estimate bio-information based on the measured pulse wave signals and the measured contact force based on the correlations satisfying a condition.

A biological signal of a subject is acquired so as to calculate biological information from the acquired biological signal. When the biological information has been determined to be anomaly, whether the biological information is one that was calculated under a high-accuracy condition is determined. When the biological information is determined to be one that was calculated under the high-accuracy condition, a notice is given based on a first criterion. In the other cases, a notice is given based on a second criterion. Thereby, it is possible to provide an abnormality notification system that can give a necessary notification appropriately while suppressing unnecessary notification, by changing the criteria for notification in accordance with the accuracy of the determined biological information when the biological information of the subject was determined to be anomaly.

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an Analytics Engine that receives one more signal files that include neural signal data of a user based on voltages detected by one or more electrodes on a set of headphones worn by a user. The Analytics Engine preprocesses the data, extracts features from the received data, and feeds the extracted features into one or more machine learning models to generate determined output that corresponds to at least one of a current mental state of the user and a type of facial gesture performed by the user. The Analytics Engine sends the determined output to a computing device to perform an action based on the determined output.

In a method and system, a medical imaging modality and the parameters to be deployed for the determined imaging modality are determined to produce an image of an examination object using the determined imaging modality and the determined parameters. Information from the preliminary examination(s) of the examination object can be automatically classified to generate classification results corresponding to interfering influence(s) resulting from the production of the image. The classification results can be analyzed to evaluate the classification results. The medical imaging modality and the parameter(s) is determined, based on the evaluated results, to minimize an influence of the interfering influences of the classification results in image(s) of the examination object generated using the determined medical imaging modality and the determined one or more parameters. The image(s) may then be generated using the determined medical imaging modality and the determined parameter(s).