22 results about "Physiologic measurement" patented technology

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

A system for physiological measurement of a physiological process of a body of a patient, the system comprising: at least one physiological sensor operable to collect, at a plurality of different times during a physiological measurement, physiological data from the body of the patient, the physiological data resulting at least from the physiological process; a processor operable to: determine many-valued quality scores for the collected physiological data, each indicative of a suitability of the respective physiological data for the analysis of the physiological process; provide an indication that the physiological measurement was successful in response to determining that an accumulative amount of times, out of the plurality of different times, for which the determined many-valued quality score fulfilled a predetermined criterion, exceeded a predetermined amount; and provide at least part of the physiological measurement to a diagnosing entity.

Embodiments of the present disclosure undertake physiological measurements of a subject whilst the subject is listening to music. The physiological measurements, together with predetermined characteristics of the music, are then used as inputs into a machine learning network, such as a graph convolution neural network (GCNN), which undertakes a classification based on the physiological measurements and musical properties as to whether the subject has a particular disease or pathological condition, such as for example hypertension. The use of the music whilst obtaining the physical measurements resulted in an increase in accuracy of the measurement of the disease or condition prevalence of greater than 10 to 15%. This improvement is due to the modulatory effects of music on the autonomic nervous system augmenting differences between people with distinct autonomic profiles.

This paper provides a system and method for selecting weight-loss medications using machine learning models to address obesity. [Solution] System 100 receives physiological measurements from the user and applies the measurements to a machine learning model. The machine learning model can be trained using multiple examples, each example including sample physiological measurements from a sample user and a corresponding sample weight-loss drug administered to the sample user. The system also generates metrics indicating expected outcomes associated with the user's weight-loss drug based on the application of one or more physiological measurements to the machine learning model, and sends a message indicating the expected outcomes associated with the weight-loss drug to the user's associated user device. [Effects] It can improve the effectiveness of weight-loss drugs when dealing with obesity.

Techniques are provided for analyzing data received from at least one portable physiological measuring device that is configured for temporary attachment to a human body. The techniques involve receiving, from sensors temporarily attached to peripheral artery locations of the body, vascular performance data. A portable signal measuring device determines elasticity of one or more blood vessels based on the vascular performance data. This information is input into at least one algorithm that performs an analysis that includes determining one or more changes in the one or more physiological metrics. Based on the analysis, the algorithm determines whether the one or more changes indicate an immediate physiological response or a delayed physiological response, and a severity of the one or more changes.

There is provided a method of predicting a state of a subject, comprising: feeding temporal sequences of physiological measurements into a machine learning (ML) model, and obtaining the state of the subject as an outcome of a second encoder of the ML model, wherein the ML model is trained by: for each sample dataset including temporal sequence of physiological measurements of a subject: feeding the sample dataset into the first encoder for obtaining a baseline latent representation, creating a copy latent representation(s), masking a time-interval(s) of each copy latent representation(s), and including the copy latent representation(s) and the baseline latent representation in a training dataset, training the ML model on the training dataset using a self supervised approach, creating an updated training dataset by including a ground truth label for each sample dataset, and further training the second encoder on the updated training dataset using a supervised training approach.

Systems and methods are provided herein for using a machine learning model to select a weight loss medication to address obesity. A computing system can receive physiological measurements of a user. The computing system can apply the measurements to a machine learning model. The machine learning model can be trained using a plurality of examples, each example including sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user. The computing system can generate, for the user, a metric indicative of an expected outcome related to a weight loss medication based on applying the one or more physiological measurements to the machine learning model. The computing system can provide, to a user device associated with the user, a message indicative of the expected outcome related to the weight loss medication. The computing system can improve the effectiveness of medications in addressing obesity.

This disclosure relates to methods for measuring one or more physiological signals while the user is engaged in a predetermined activity. Exemplary predetermined activities can include activities such as walking, climbing stairs, biking, and the like. The physiological measurements can include, but are not limited to, heart rate signals. The physiological measurements may be affected by the predetermined activity, so the system may be configured to employ one or more criteria prior to measuring physiological information to minimize the effects. The one or more criteria can include, but are not limited to, an inter-sampling waiting time, continuous motion criteria, predetermined activity criteria, a post-physiological measurement amount of time, and a confidence value. The continuous motion criteria can be based on the type of predetermined activity. For example, walking may have walking state criteria and a step count criteria.

The invention provides a physiological measurement parameter self-adaptive configuration method and system based on a signal separation degree, and relates to the technical field of measurement for diagnosis purposes, and the method comprises the following steps: analyzing prior data of a target object, and determining a corresponding measurement configuration classification; according to measurement configuration classification mapping, obtaining an initial stimulation parameter set, and constructing control instruction output to trigger test stimulation for inducing physiological response; acquiring a physiological feedback signal corresponding to the test stimulation, determining an artifact influence time interval and a response analysis time interval based on the triggering moment, identifying a stimulation artifact component and a physiological response component, and calculating a signal separation index; when the signal separation degree index does not meet the preset threshold value, iteratively adjusting the time domain distribution parameters in the stimulation parameter set until the signal separation degree index meets the threshold value; and outputting the physiological measurement indexes. According to the method, the effective response extractability and the measurement result reliability can be improved under the condition that interference and response are overlapped due to individual differences and detection condition changes.

A non-invasive, optical-based physiological monitoring system is disclosed. One embodiment includes an emitter configured to emit light. A diffuser is configured to receive and spread the emitted light, and to emit the spread light at a tissue measurement site. The system further includes a concentrator configured to receive the spread light after it has been attenuated by or reflected from the tissue measurement site. The concentrator is also configured to collect and concentrate the received light and to emit the concentrated light to a detector. The detector is configured to detect the concentrated light and to transmit a signal representative of the detected light. A processor is configured to receive the transmitted signal and to determine a physiological parameter, such as, for example, arterial oxygen saturation, in the tissue measurement site.

The application provides a fish drinking water and physiological measurement synchronous monitoring system and method, which comprises a catheter and shunt pipeline assembly, a liquid drop detection module, a communication interface module and a host computer control software module, and selectively comprises a back-feeding system. The catheter and shunt pipeline assembly shunts intestinal cavity fluid according to a standard ratio and is connected to the liquid drop detection module; the liquid drop detection module receives and detects liquid drop output pulse type event signals; the host computer control software module collects and generates millisecond level time stamp logs through the communication interface module, and outputs drinking water event counts, drinking water rates per unit time and cumulative drinking water amounts; at the same time, the liquid drop events and external physiological measurement data share the same computer clock or aligned time reference, so that drinking water and external physiological measurement data are synchronously recorded and paired output on the same time axis. In a closed loop control mode, the back-feeding volume can be calculated according to the cumulative liquid drop number and single drop volume, and the back-feeding pump is triggered to execute back-feeding.

The application provides a physiological measurement parameter adaptive configuration method and system based on signal separation degree, relates to the field of measurement technology for diagnostic purposes, and comprises the following steps: analyzing prior data of a target object, and determining a corresponding measurement configuration category; obtaining an initial stimulation parameter set according to the measurement configuration category mapping, constructing a control instruction output to trigger a test stimulation for inducing a physiological response; obtaining a physiological feedback signal corresponding to the test stimulation, determining a pseudo-trace influence time interval and a response analysis time interval based on a triggering time, identifying a stimulation pseudo-trace component and a physiological response component, and calculating a signal separation degree index; when the signal separation degree index does not satisfy a preset threshold value, iteratively adjusting a time domain distribution parameter in the stimulation parameter set until the signal separation degree index satisfies the threshold value; and outputting a physiological measurement index. The application can improve the effective response extractability and the measurement result reliability in the case that individual differences and detection condition changes result in interference and response overlap.

To maintain data integrity in a health analysis platform.SOLUTION: Systems and methods for maintaining data integrity in a computer health analysis platform are disclosed. For example, the method includes (i) filtering the existing healthcare data by first determining or extracting a first subset of data of the dataset such that the first subset focuses on a common health-related attribute, (ii) generating a reference measurement range from the extracted first subset, and (iii) determining, based on the reference measurement range, a unit of measurement of the clinical laboratory data that lacks the unit of measurement or exhibits a misdescription error. For example, the first set of data represents measurements of a physiological parameter of the entity. For example, the clinical study data is different from the first subset or the existing healthcare data used to determine the first subset. After the unit of measure is determined, a data structure representing the unit of measure is generated and stored.SELECTED DRAWING: Figure 1

Breathing is fundamental to life. Continuous monitoring of respiratory rate and pattern is crucial for detecting the onset of respiratory failure; however, this vital sign cannot be monitored in most hospital wards. Respiratory distress is caused by dysfunction of oxygenation (insufficient oxygen intake) or dysfunction of ventilation (insufficient carbon dioxide removal). This invention discloses a method and apparatus for monitoring respiratory rate by extracting information from chest acceleration, electrocardiogram, and breath sounds. The invention also discloses a method for quantifying the degree of respiratory deterioration by defining a smart respiratory index, which is defined by combining at least three parameters extracted from physiological measurements such as electrocardiogram, breath sounds, and chest acceleration. These methods are implemented by a small wireless patch attached to the upper part of the patient's chest and communicate with external proprietary software and a monitor via Bluetooth.

This disclosure relates to methods for measuring one or more physiological signals while the user is engaged in a predetermined activity. Exemplary predetermined activities can include activities such as walking, climbing stairs, biking, and the like. The physiological measurements can include, but are not limited to, heart rate signals. The physiological measurements may be affected by the predetermined activity, so the system may be configured to employ one or more criteria prior to measuring physiological information to minimize the effects. The one or more criteria can include, but are not limited to, an inter-sampling waiting time, continuous motion criteria, predetermined activity criteria, a post-physiological measurement amount of time, and a confidence value. The continuous motion criteria can be based on the type of predetermined activity. For example, walking may have walking state criteria and a step count criteria.

A wearable physiological measurement apparatus comprises a base, a bridge member, and a first and second support members movably coupled to the base and the bridge member to surround size-variable an opening for accommodating a care giver's finger therein. A measurement device is coupled to the base and with a probe projecting toward the opening for contacting the finger for vital sign measurement and monitoring. Resilient members are coupled between the base, the bridge member and the first and second support members to bias the base, the bridge member and the first and second support members moving toward each other to grip the finger to maintain proper contact of the probe to the finger.

Extracorporeal fluid management is used to treat a patient in shock. Intravascular fluid optimization is provided by dynamically assessing physiological data acquired from the patient and rebalancing the patient's intravascular volume status to maintain adequate perfusion and hemodynamic state. Physiologic measurements are continuously acquired, monitored, or otherwise measured from the patient as physiological data. These physiological data are input to a control algorithm to autonomously adjust the amounts of fluid to infuse or remove (e.g., ultrafiltrate) to optimize rebalancing of the amount of intravascular fluid volume throughout the stages of shock.