Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

Methods and systems are provided for detecting a mental health condition. Structured and unstructured information is analyzed using natural language processing to extract information including clinical data values and medical concepts pertaining to a user. Reference medical information is evaluated using natural language processing to correlate medical data with mental health conditions. A classification for a mental health condition of the user is determined using a machine learning model and based on the extracted information and correlations, wherein the extracted information includes blood analysis for the user. The user is assigned to a segment of users based on the extracted information. A treatment for the mental health condition of the user is indicated based on the classification and the assigned segment of users.

A data processing method for detecting and classifying a segment of a signal that is obtained from a single-channel EEG-recording as a target signal segment or as a non-target signal segment. The method includes a voting process to determine whether classification of a first detected segment of the signal as a target signal segment or classification of a second detected segment of the signal as a non-target signal segment is correct. A device and a system that are configured and arranged to perform the data processing method.

Devices, systems, and methods herein relate to non-invasive patient monitoring for infection detection and infection resolution. These systems and methods may receive and measure patient biosignals to estimate an infection level of a patient. In some embodiments, a method may include the steps of receiving physiological data of a patient. An infection measure may be estimated based on the physiological data. An infection state of the patient may be detected based at least in part on the estimated infection measure.

This application discloses a method for controlling an electronic device to perform photoplethysmography detection. The method includes obtaining historical detection data of a photoplethysmography sensor, calculating a confidence of the historical detection data, determining, based on the calculated confidence of the historical detection data, whether a light source to be turned on by the photoplethysmography sensor in a next detection time segment includes a light source different from a light source turned on when the historical detection data is obtained, and determining that a photoelectric sensing element to be turned on in the next detection time segment is a photoelectric sensing element corresponding to the determined light source to be turned on.

Provided are computer-implemented methods and systems for generating and/or utilizing data analysis algorithm(s) for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.

A first value for each of a plurality of parameters is received, each of the first values being associated with a user and a first day. A second value for each of the plurality of parameters is received, each of the second values being associated with the user and a second day that is subsequent to the first day. For each of the plurality of parameters, a trend indication is determined, the trend indication being based on the first values, the second values, and a first time period. A base weight value for each of the plurality of parameters is determined, the base weight value being based on the first time period and the determined trend indication associated with the one of the plurality of parameters. A mood score is determined, based on the base weight value for each of the plurality of parameters.

A patient care method through artificial intelligence-based monitoring in accordance with one example of the present disclosure comprises steps of: obtaining image information relating a user by a first collecting portion, obtaining speech information relating to the user by a second collecting portion and obtaining biometrics information relating to the user by a third collecting portion (Step 1); representing at least a part of a plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion, through a display portion of a user table (Step 2); determining health condition of the user, based on a part of the plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion by a server (Step 3); controlling the display portion of the user table to represent a first information automatically generated based on the determined heath condition by the server (Step 4), wherein the first information is converted in real time based on user's feedback on the first information and represents a change in the determined health condition on the display portion.

An electronic device for identifying occurrence of hypotension and a method therefor are provided. The electronic device includes a housing, a user interface, a photoplethysmogram (PPG) sensor exposed through at least part of the housing, a motion sensor disposed in the housing, at least one processor operatively coupled with the user interface, the PPG sensor, and the motion sensor, and a memory operatively coupled with the at least one processor. The memory may store instructions which, when executed by the at least one processor, cause the at least one processor to, based on first data from the motion sensor indicating a change of a selected pattern, identify a blood pressure value based at least in part on second data from the PPG sensor, and based on the identified blood pressure value being lower than a first threshold, provide a notification through the user interface.