The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

Methods for treating and managing pain in a patient with therapeutic neuromodulation and associated systems and methods are disclosed herein. Chronic or debilitating pain can be associated, for example, with a disease or condition of the abdominal or reproductive viscera. One aspect of the present technology is directed to methods that at least partially inhibit sympathetic neural activity in nerves proximate a target blood vessel of a diseased or damaged organ of a patient experiencing pain. Targeted sympathetic nerve activity can be modulated at least along afferent pathways which can improve a measurable parameter associated with the pain of the patient The modulation can be achieved, for example, using an intravascularly positioned catheter carrying a therapeutic assembly, e.g., a therapeutic assembly configured to use electrically-induced, thermally-induced, and/or chemically-induced approaches to modulate the target sympathetic nerve.

A system for monitoring fetal wellbeing over time during pregnancy includes a sensor coupled to a pregnant woman; a processor communicatively coupled to the sensor; and a computer-readable medium having non-transitory, processor-executable instructions stored thereon. Execution of the instructions causes the processor to perform a method including: acquiring a signal from a sensor; processing the signal to identify and extract a parameter of interest from the signal; and analyzing the parameter of interest to determine a degree of fetal wellbeing. The parameter of interest may include one or more of: an average fetal heart rate, an average fetal heart rate variability, a fetal kick or movement count, an average placental oxygenation level, an average placental temperature, an average placental pH, an average amount of amniotic fluid, a fetal heart rate profile, a fetal heart rate variability profile, and a fetal movement profile.

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.

Described herein are noninvasive electrical stimulation devices, systems and methods for stimulation of the Vagus nerve through its auricular branch to provide beneficial physiological responses in subjects, including alleviation, mitigation or elimination of symptoms of various disorders, including metabolic and inflammatory disorders.

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.

A wearable device for detecting and/or measuring physiological information from a subject includes a housing, at least one optical emitter supported by the housing, at least one optical detector supported by the housing, a first light guide supported by the housing, a second light guide supported by the housing, a motion sensor supported by the housing, and a processor supported by the housing. The processor is configured to calculate footsteps, distinguish footsteps from heart beats, and to remove footstep motion artifacts from signals produced by the at least one optical detector. Also, the processor is configured to process signals produced by the at least one optical detector to determine subject heart rate and to produce integrity data about the subject heart rate. The process is further configured to generate a multiplexed output serial data string comprising the subject heart rate and the integrity data.

The present invention relates to a method to predict the risk of obtaining a stress related mood disorder or syndrome by a person, comprising a. Measuring at least three parameters comprising at least one sympathetic, one parasympathetic and one hormonal parameter during a stress response, said result of the measurement depicted as RS, RP and RH respectively; b. Estimate the value of one of these parameters by calculating it from the other two parameters; c. Predict the risk on basis of the deviation between calculated and measured value of the parameter that has been estimated in step b).

This disclosure relates generally to physiological monitoring, and more particularly to feature set optimization for classification of physiological signal. In one embodiment, a method for physiological monitoring includes identifying clean physiological signal training set from an input physiological signal based on a Dynamic Time Warping (DTW) of segments associated with the physiological signal. An optimal features set is extracted from a clean physiological signal training set based on a Maximum Consistency and Maximum Dominance (MCMD) property associated with the optimal feature set that strictly optimizes on the objective function, the conditional likelihood maximization over different selection criteria such that diverse properties of different selection parameters are captured and achieves Pareto-optimality. The input physiological signal is classified into normal signal components and abnormal signal components using the optimal features set.