37 results about "Physiologic measurement" patented technology

Various example of the present disclosure provide sensing apparatuses configured for wearable and wireless use for deep tissue physiological monitoring. The sensing apparatuses may be embodied by a thin flexible patch configured to conform with a skin surface of a subject. A sensing apparatus may include a plurality of microneedles oriented to extend towards and penetrate into the subject to a shallow depth. The microneedles may be configured as waveguides for a given sensing modality (e.g., light, ultrasound), such that sensing wave signals propagate to deep tissues. For the sensing, the sensing apparatus includes waveform generators (e.g., light-emitted diodes) and waveform detectors (e.g., photodiodes). Machine learning models may be used to process and denoise sampled data from the waveform detectors and to generate accurate and reliable physiological measurements, including heart rate, respiratory rate, pulse intensity, respiratory intensity, blood oximetry, tissue oximetry, blood flow rate, and / or the like.

One aspect described herein is a data communication apparatus. The apparatus may comprise: electronic components operable to gather and output different types of physiological measurements associated with a user's chest, the electronic components comprising a multimodal sensor array, a transceiver, and a power source; and a housing that contains the electronic components and comprises an attachment element operable to maintain an alignment of the multimodal sensor array over the user's chest, wherein, when the housing is maintained over the user's chest, the multimodal sensor array is operable with the power source to measure the different types of physiological measurements, and the transceiver is operable with the power source and the sensor array to output the different types of physiological measurements to an external device. Related apparatus, devices, methods, and systems also are described.

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.

The present invention relates to a system and method for, for example, periodically collecting physiological parameters in real-time from a plurality of subjects, for example temperature from cancer patients, heartbeat rates from persons being treated for coronary conditions, physiological orientation information mechanical shock and related parameters, for example from football players in danger of head trauma, and other physiological measurements which it real-time convey useful information. This information is coupled to a system which integrates the information, subjects it to criteria (for example doctor-specified dangerous condition criteria), communicates information and, optionally provides alarms to clinicians. The invention, through the deployment of computing functions on smart phones connected to the skin applied transducer collecting physiological parameters reduces power consumption, and this objective is further achieved through the use of sampling strategies and timing, and the limitation of access to the skin applied transducer.

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.

Systems, methods, computer medium, and program products are disclosed for maintaining data integrity in a computerized health analysis platform. For example, a method includes (i) filtering existing health care data by first determining or extracting a first subset of data of a dataset such that the first subset is concentrated in a common health-related attribute (s), (ii) generating a reference measurement range from the extracted first subset, and (iii) determining measurement units for laboratory test data lacking measurement units or exhibiting mismarking errors based on the reference measurement range. For example, a first subset of the data sets represents measurements of the physiological parameter (s) of the entity. For example, the laboratory test data differs from the first subset or existing healthcare data used to determine the first subset. After the measurement unit is determined, a data structure representing the measurement unit is generated and stored.

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.

A system having a combination of several sensors that measure the following physiological measurements: heart rate (HR), skin temperature (Tsk), and movement, and a method receiving the system data and using the interplay of these data to reveal individual's signature(s) for use in a multi-factor assessment to provide contextual and clinically relevant information to determine the individual's Exertional Heat Stroke (EHS) risk. When the risk is above a threshold alerting the monitored individual or somebody else for the monitored individual's benefit to take action to lower the EHS risk and thereby lessen the associated consequences of EHS.

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.

Presented herein are techniques for monitoring a condition associated with a recipient of a medical device. A model is trained based on historical electrophysiological measurements associated with a recipient of a medical device over a period of time. One or more current electrophysiological measurements associated with the recipient of a medical device are obtained. A condition associated with the recipient is monitored based on the one or more current electrophysiological measurements and the model.

A maternal and fetal monitoring system comprises a first ultrasound transducer configured to be positioned on a maternal patient abdomen to acquire fetal ultrasound measurements of a fetus, the first ultrasound transducer housed in a first housing; a maternal measurement patch configured to be secured on the maternal abdomen and to obtain UA physiological measurements indicative of uterine activity (UA) of the maternal patient; a connection cable connecting the maternal measurement patch to the first housing and configured to transmit the UA physiological measurements from the maternal measurement patch; and a controller configured to determine fetal heart rate (fHR) values for the fetus based on the fetal ultrasound measurements and to determine UA values for the maternal patient based on the UA physiological measurements.

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.

A personalized targetable optimal level of sedation is derived through automated real-time analysis of an electroencephalogram (EEG) entropy / variability metric and a cerebrovascular reactivity metric. Ongoing real-time collection is made of both EEG entropy / variability metric values and physiological measurements. Artifact detection and removal on raw signals precedes and derivation of real-time cerebrovascular reactivity metric values from the physiological measurements, and calculation of a first and second summary metrics of first and second respective sample sets of the EEG entropy / variability metric values and cerebrovascular reactivity metric values collected in a given period of time. More data points are curated over longer windows of time, and for each such window, are binned by the first summary metric. A curve is fitted to the grouped data points, and a point of lowest cerebrovascular reactivity metric value on the curve is identified, whose EEG entropy / variability metric value denotes the personalized targetable optimal level of sedation.

Methods, systems, and devices for photoplethysmogram (PPG) analysis are described. Techniques described herein may enable a system to convert a PPG signal from a time-domain signal into an attractor reconstruction representation in three-dimensional (3D) space. Visualizations of such 3D attractor reconstruction projections may be displayed to a user via a smart device. Further, the system may use machine learning models to identify morphological features within 3D attractor reconstruction projections to perform physiological measurements and determine health-related insights for the user. For example, a smart device may input time-domain PPG signals and 3D attractor reconstruction projections into machine learning models that are configured to identify morphological features within the time-domain PPG signals and attractor reconstruction projections. The smart device may perform various physiological measurements, such as cardiovascular age, blood pressure, heart rate variability, and the like.

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.

Methods and systems to validated physiologic waveform reliability and uses thereof are provided. A number of embodiments describe methods to validate waveform reliability, including blood pressure waveforms, electrocardiogram waveforms, and / or any other physiological measurement producing a continuous waveform. Certain embodiments output reliability measurements to closed loop systems that can control infusion rates of cardioactive drugs or other fluids in order to regulate blood pressure, cardiac rate, cardiac contractility, and / or vasomotor tone. Further embodiments allow for waveform evaluators to validate waveform reliability based on at least one waveform feature using data collected from clinical monitors using machine learning algorithms.

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.

Systems and methods for maintaining data integrity in a computerized health analysis platform are disclosed. For instance, a method includes (i) filtering existing healthcare data by first determining or extracting a first subset of data of data sets, such that the first subset is focused on common health-related attribute(s), (ii) generating a reference measurement range from the extracted first subset, and (iii) determining, based on the reference measurement range, measurement unit for lab test data that lack measurement unit or exhibit mislabeling error. For instance, the first subset of data sets represents measurements of physiological parameter(s) of entities. For instance, the lab test data are different from the first subset or the existing healthcare data that is used to determine the first subset. After the measurement unit is determined, a data structure representing the measurement unit is generated and stored.

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Systems and methods for controlling sacral nerve stimulation for treating Inflammatory Bowel Disease (IBD) using measured physiologic parameters are described herein. For example, measurement of physiologic parameters can be obtained via a swallowable capsule, implant, or wearable device, and can be used to adjust stimulation parameters. The physiologic measurement data can also be collected over time to build a predictive model that can be used to pre-emptively provide therapy, and / or can be used to screen a plurality of candidate stimulation programs.