Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase or one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase or one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

A sleep study system comprises a set of sensors for monitoring physiological parameters of a subject during sleep as part of a sleep study and for monitoring the sleep stage of the subject. It is determined if intervention to the subject is needed for maintenance or repair to the sleep study system. If so, a time to perform the intervention is also derived based on the sleep stage of the subject, in particular so as to be least disruptive to the subject.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

A system for collecting data for assessment of cardiovascular function includes a plurality of monitoring devices coupled to different respective body parts. Each monitoring device is configured to measure a respective signal at the respective body part in response to cardiovascular activity. The respective signal includes a cardiovascular component attributable to the cardiovascular activity and an artifact component not attributable to the cardiovascular activity. When the monitoring devices measure the respective signals simultaneously over a same time period, the cardiovascular components are correlated, and the artifact components are not correlated. The system also includes a controller configured to: identify the cardiovascular components included in the signal measurements, according to the correlation of the cardiovascular components; reject the artifact components included in the signal measurements, according to the non-correlation of the artifact components; and determine cardiovascular information from the identified cardiovascular components for an assessment of cardiovascular function.

A system for collecting data for assessment of cardiovascular function includes a plurality of monitoring devices coupled to different respective body parts. Each monitoring device is configured to measure a respective signal at the respective body part in response to cardiovascular activity. The respective signal includes a cardiovascular component attributable to the cardiovascular activity and an artifact component not attributable to the cardiovascular activity. When the monitoring devices measure the respective signals simultaneously over a same time period, the cardiovascular components are correlated, and the artifact components are not correlated. The system also includes a controller configured to: identify the cardiovascular components included in the signal measurements, according to the correlation of the cardiovascular components; reject the artifact components included in the signal measurements, according to the non-correlation of the artifact components; and determine cardiovascular information from the identified cardiovascular components for an assessment of cardiovascular function.

A dental appliance including a dielectric substrate with one or more electric areas having one or more integrated circuits, one or more electric lines, and optionally one or more shielding lines. The dental appliance further includes one or more ground areas including one or more ground pads, one or more sensing areas including one or more sensing pads, and optionally one or more shielding pads. The dental appliance is configured to monitor dental contact, dental forces, and/or for detecting teeth clenching and/or grinding.

A dental appliance including a dielectric substrate with one or more electric areas having one or more integrated circuits, one or more electric lines, and optionally one or more shielding lines. The dental appliance further includes one or more ground areas including one or more ground pads, one or more sensing areas including one or more sensing pads, and optionally one or more shielding pads. The dental appliance is configured to monitor dental contact, dental forces, and/or for detecting teeth clenching and/or grinding.

A method may include identifying a simulated three-dimensional representation corresponding to an internal anatomy of a subject based on a match between a computed two-dimensional image corresponding to the simulated three-dimensional representation and a two-dimensional image depicting the internal anatomy of the subject. Simulations of the electrical activities measured by a recording device with standard lead placement and nonstandard lead placement may be computed based on the simulated three-dimensional representation. A clinical electrogram and/or a clinical vectorgram for the subject may be corrected based on a difference between the simulations of electrical activities to account for deviations arising from patient-specific lead placement as well as variations in subject anatomy and pathophysiology.