A computer system for determining onset of an acute coronary syndrome (ACS) event in a remote computing environment comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories is provided. The stored program instructions include capturing, using a camera, a first image at a first time of an iris and a pupil of a first eye of a user; following the capturing of the first image, identifying in the first image a first iris information; capturing, using the camera, a second image at a second time of the iris and the pupil of the first eye of the user; following the capturing of the second image, identifying in the second image a second iris information; determining whether the first iris information is within an allowable range of the second iris information; and providing an indication of a likely ACS event based on a determination of whether the first iris information is within the allowable range of the second iris information.

An intelligent psychological assessment and intervention system based on an independent space, comprising a psychological intervention cabin and a central database which achieves data interaction with the psychological intervention cabin. The psychological intervention cabin comprises a housing, a data acquisition module, a data processing module, and an intervention module; the data acquisition module acquires physiological and psychological data of a subject; the data processing module performs operations on the acquired data, establishes a multi-dimensional state point of the subject according to a mathematical model of the data processing module, matches it with data in the central database, finds a suitable intervention procedure for the subject, and guides the intervention module to carry out intervention; in the intervention process, the data acquisition module continuously acquires the physiological and psychological data of the subject, and the data processing module forms a new multi-dimensional state point according to new data and re-matches it to adjust the intervention procedure. By repeating this cycle, the intervention procedure is continuously adjusted in the intervention process to seek a most suitable intervention solution for the subject, and an optimal intervention effect is achieved.

A signal acquisition circuit is provided in the present disclosure. The signal acquisition circuit includes a signal acquisition electrode, wherein the at least one signal acquisition electrode is provided with a signal acquisition branch circuit, and the signal acquisition electrode is provided with a feedback network branch circuit. A first terminal of the signal acquisition branch circuit is electrically connected to the signal acquisition electrode, a second terminal of the signal acquisition branch circuit is electrically connected to a signal input terminal of a signal processing module. A first terminal of the feedback network branch circuit is electrically connected to the signal acquisition electrode, a second terminal of the feedback network branch circuit is electrically connected to a Driven-Right-Leg Circuit.

A method includes, based on respective signals acquired by a plurality of electrodes on an anatomical surface of a heart, computing respective local activation times (LATs) at respective locations of the electrodes. The method further includes, based on the LATs, computing respective directions of electrical propagation at the locations. The method further includes selecting pairs of adjacent ones of the electrodes such that, for each of the pairs, a vector joining the pair is aligned, to within a predefined threshold degree of alignment, with the direction of electrical propagation at the location of one of the electrodes belonging to the pair. The method further includes associating respective bipolar voltages measured by the pairs of electrodes with a digital model of the anatomical surface. Other examples are also described.

A method includes obtaining multiple local activation times (LATs) at different respective measurement locations on an anatomical surface of a heart. The method further includes computing respective directions of electrical propagation at one or more sampling locations on the anatomical surface, by, for each sampling location, selecting a respective subset of the measurement locations for the sampling location, constructing a set of vectors, each of at least some of the vectors including, for a different respective measurement location in the subset, three position values derived from respective position coordinates of the measurement location and an LAT value derived from the LAT at the measurement location, and computing the direction of electrical propagation at the sampling location based on a Principal Component Analysis (PCA) of a 4×4 covariance matrix for the set of vectors. The method further includes indicating the directions of electrical propagation on a display.

Disclosed herein is a system and method directed to detecting placement of a medical device within a patient body, where the system includes a medical device including an optical fiber having core fibers, each of the one or more core fibers including a plurality of sensors each configured to (i) reflect a light signal having an altered characteristic due to strain experienced by the optical fiber. The system further includes logic configured to cause operations of providing an incident light signal to the optical fiber, receiving reflected light signals of different spectral widths of the incident light from the sensors, processing the reflected light signals to detect fluctuations of a portion of the optical fiber, and determining a location of the portion of the optical fiber based on the detected fluctuations. In some instances, the detected fluctuations are caused by anatomical movement of the patient body.

Disclosed herein is a system and method directed to detecting placement of a medical device within a patient body, where the system includes a medical device including an optical fiber having core fibers, each of the one or more core fibers including a plurality of sensors each configured to (i) reflect a light signal having an altered characteristic due to strain experienced by the optical fiber. The system further includes logic configured to cause operations of providing an incident light signal to the optical fiber, receiving reflected light signals of different spectral widths of the incident light from the sensors, processing the reflected light signals to detect fluctuations of a portion of the optical fiber, and determining a location of the portion of the optical fiber based on the detected fluctuations. In some instances, the detected fluctuations are caused by anatomical movement of the patient body.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

Provided herein is a method for diagnosing and treating a subject at risk for atrial fibrillation (AF) or related health conditions, the method including: collecting one or more physiological signals from the subject in a sleep state or an awake state; extracting time series data from the one or more physiological signals; performing dynamic analyses of the time series data using artificial intelligence, wherein the artificial intelligence calculates a series of dynamic measurements, said dynamic measurements being indicative of a probability of an onset of an abnormal atrial rhythm; providing an integrated personalized risk score including the dynamic measurements, wherein the integrated personalized risk score is indicative of a probability of an onset of AF in the subject; diagnosing the subject as being at risk for AF when the integrated personalized risk score exceeds a threshold value, wherein the threshold value is calculated by the artificial intelligence based on a library of stored data; and treating the diagnosed subject with an effective therapy to prevent or treat AF or AF-related health conditions.