MOR RESEARCH APPLICATIONS LTD.

There is provided a method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving EEG datasets from EEG electrodes monitoring a head of the subject, computing events for the EEG datasets, clustering the events into clusters, computing a p-adic representation of the clusters, extracting a p-adic topology from the p-adic representation of the clusters, computing a personal universal dendrogramic holographic signature (PUDHS) of the p-acid topology relative to a personalized threshold that separates between a relative large distance between events and a relatively small distance between events, the PUDHS denoting number of events below the personalized threshold, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the PUDHS relative to a medical threshold that separates between presence of the medical state and non-presence of the medical state.

There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

There is provided a method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving EEG datasets from EEG electrodes monitoring a head of the subject, computing events for the EEG datasets, clustering the events into clusters, computing a p-adic representation of the clusters, extracting a p-adic topology from the p-adic representation of the clusters, computing a personal universal dendrogramic holographic signature (PUDHS) of the p-acid topology relative to a personalized threshold that separates between a relative large distance between events and a relatively small distance between events, the PUDHS denoting number of events below the personalized threshold, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the PUDHS relative to a medical threshold that separates between presence of the medical state and non-presence of the medical state.

Disclosed herein is a multi-modal sensor system, including a wearable device configured to receive signals relating to a swallowing process of a subject, the wearable device including one or more surface Electromyograph sensors configured to receive signals relating to electrical potential in muscles of the throat, one or more bio-impedance sensors, one or more memories, one or more processors configured to operate one or more sensors of the wearable device, synchronize the signals to one or more predetermined events to generate a synchronization feature, receive the signals as a first diagnostic data set, analyze the first diagnostic data set, assess, based on the analysis, the swallowing process of the subject to yield an assessment output, and present the assessment output and determine a bio-impedance signal.

Disclosed is a device including at least 4 sections with a unique layout which includes a surface functionalized with an agent having specific binding affinity to a target molecule, and which allows flow. Disclosed are also a kit and a method for determining and quantifying the presence of a biomarker of a thyroid medical condition in a sample.

Using microstate data to evaluate Alzheimer's disease including dementia and/or mild cognitive impairment and/or depression and optionally provide treatment based on the diagnosis and/or modify treatment and/or diagnosis based on response to treatment, optionally as measured using microstate data. In one example, the method includes analyzing EEG data to generate one or more EEG parameter; and generating a risk for one or both of depression and MCI (mild cognitive impairment) based on said one or more EEG parameters. Optionally, the disease comprises MCI. Optionally, the disease comprises depression. Optionally, the one or more EEG parameter comprises a microstate-derived parameter. Optionally, the one or more EEG parameter comprises a microstate duration derived parameter.

A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.

There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

The present invention relates to a system and method for determining sleep quality parameters according to audio analyses, comprising: obtaining an audio recorded signal comprising sleep sounds of a subject; segmenting the signal into epochs; generating a feature vector for each epoch, wherein each of said feature vectors comprises one or more feature parameters that are associated with a particular characteristic of the signal and that are calculated according to the epoch signal or according to a signal generated from the epoch signal; inputting the generated feature vectors into a machine learning classifier and applying a preformed classifying model on the feature vectors that outputs a probabilities vector for each epoch, wherein each of the probabilities vectors comprises the probabilities of the epoch being each of the sleep quality parameters; inputting the probabilities vectors for each epoch into a machine learning time series model and applying a preformed sleep quality time series pattern function on said probabilities vectors that outputs an enhanced probabilities vector for each epoch; determining a final sleep quality parameter for each epoch by calculating the most probable sleep quality parameters sequence.

A method of assessing a subject including: measuring a plurality of respirations of the subject; determining one or more respiration parameter from the plurality of respirations; and determining, using the one or more respiration parameter, one or more of: a state of consciousness of the subject; and a prognosis of the subject.