A method of performing impedance measurements on a subject. The method includes using a processing system to determine at least one impedance measurement to be performed, and at one electrode arrangement associated with the determined measurement. A representation of the arrangement is displayed so the impedance measurement can be performed once the electrodes have been arranged in accordance with the displayed representation.

A system and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions which uses data obtained while a person is engaged in simultaneously in a range of primary physical tasks combined with defined types of associative activity, such as listening, reading, speaking, mathematics, logic puzzles, navigation of a virtual environment, recall of past stimuli, etc. The data from the physical and associative activities are combined to generate a composite functioning score visualization indicating the relative functioning of areas aspects of neurological functioning; including those in which deficiencies may be present, which are early indicators of possible neurological conditions. Through algorithmic recommendations combined with expert and user input, a conditioning regimen targeting neurological aspects of interest paired with periodic testing allows the user to track their progress in these areas over time.

Methods, systems, and apparatus for assessing a state of an epilepsy disease or a comorbidity thereof are provided. The methods comprise receiving at least one autonomic index, neurologic index, stress marker index, psychiatric index, endocrine index, adverse effect of therapy index, physical fitness index, or quality of life index of a patient; comparing the at least one index to at least one reference value; and assessing a state of an epilepsy disease or a body system of the patient based on the comparison. A computer readable program storage device encoded with instructions that, when executed by a computer, perform the method described above is also provided. A medical device system capable of implementing the method described above is also provided.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.

A method for processing physiological signals (S.sub.EMG; S.sub.EEG) acquired from a subject (S) allows the detection of an imminent loss of balance of the subject and the generation of a signal (Aout) indicating the imminent loss of balance. The method comprises: the reception of a plurality of electromyographic signals (S.sub.EMG) representative of a detected muscle activity of a plurality of selected muscles of the subject, as well as a plurality of brain signals (S.sub.EEG) acquired by means of an electroencephalogram and representative of a cortical activity of the subject during said muscle activity; the analysis and processing of the electromyographic signals (S.sub.EMG) in order to extract a muscle activity pattern, MAP, and generate an indicator of normality/abnormality of the detected muscle activity pattern; the analysis and processing of the brain signals (S.sub.EEG) in order to generate one or more cortical response indicators of the subject upon occurrence of said detected muscle activity (I.sub.EGg; LF(k)); and a classification step, wherein at least one indicator (MA(k)) of normality/abnormality of the MAP and one or more of said cortical response indicators are correlated to generate a signal (Aout) indicating an imminent loss of balance.

The present invention relates to a big data platform based analysis algorithm, system, and method for identifying, tracking, and preventing a target individual in a virus incubation period by using a terminal that collects biosignal measurement values and provides an epidemiological investigation according to digital anti-epidemic through a storage, distribution, collection, and analysis processing of the biosignal measurement data values.

A computer implemented method for providing output data comprising an indication regarding the affliction of a patient with an infectious respiratory disease, the method comprises receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patience with the infectious respiratory disease; and proving the output data.

The present disclosure relates to use of pulsed dose inhaled nitric oxide for treatment of infection, including infection with SARS-CoV2 and the disease state COVID-19.

This invention relates to a prediction support system and associated methods for supporting prediction of the severity of a disease. The systems and methods may perform operations that include continuously detecting whether a patient with the disease is in bed, and acquiring, based on the detection result of the detecting, an in-bed pattern indicating, as a time series, whether the patient is in bed. The systems may include a detection device and an acquisition unit.