14 results about "Cardiac risk" patented technology

Methods, systems, and devices for cardiac risk assessment are described. A method includes retrieving a first set of historical activity data for a first time period; retrieving a second set of historical activity data for a second time period; wherein the first set of historical activity data and second set of historical activity data each comprise at least: location data and associated heart rate data, and the location data of the first set of historical activity data and the location data of the second set of historical activity data comprise at least a partial overlap with respect to at least one of: a geographic location and an elevation profile, the method further including the steps of: comparing the heart rate data of the first set of historical activity data and the heart rate data of the second set of historical activity data, and determining an indication of cardiac associated risk based on the comparison.

Method and apparatus for performing automatic cardiac diagnosis. The apparatuses described herein may be handheld devices which enables self-recording of cardiac signals by the patient, including entering relevant data by patients regarding their cardiac history, including cardiac disease risk factors, and / or current conditions and symptoms. Based on recorded cardiac signals, cardiac risk factors and the current symptoms, the apparatus may calculates a cardiac risk score and may provide simplified diagnostic information and actionable instructions to the patient.

A cardiac stress test monitoring system including a controller is provided. The controller is configured to receive a plurality of echocardiogram images, where each echocardiogram image of the plurality of echocardiogram images corresponds to one or more stress phases, receive measurement data corresponding to the one or more stress phases, generate wall motion data based on the plurality of echocardiogram images via a cardiac wall motion tracking model, generate measurement abnormality data based on the measurement data via a measurement abnormality detection model, generate peak stress RWMA prediction data based on the wall motion data and the measurement abnormality data via a RWMA prediction model, generate cardiac risk data based on the peak stress RWMA prediction data, and output the cardiac risk data. The cardiac risk data can include a cardiac risk dashboard including one or more risk visualizations.

The invention relates to the technical field of medical treatment and health data processing, and discloses a method for identifying heart risks by utilizing night vital sign data mutation, which comprises the following steps: carrying out scene classification on respiratory signals, and when the classification result is an ambiguous fluctuation artifact state, further acquiring a signal quality index of an ECG signal; the method comprises the following steps: acquiring a signal quality index, performing cross validation on a fluctuation artifact state by using the signal quality index, correcting the fluctuation artifact state into a physiological wave state or a signal artifact state, and adaptively selecting an ECG analysis model according to a final classification result. According to the method, the inherent ambiguity problem of the fluctuation artifact state is solved, and the method can effectively distinguish the real physiological fluctuation from the technical artifact, so that a targeted analysis model is scheduled for the two scenes with different properties, and the accuracy of risk identification is improved.

The present application relates to the technical field of medical health data processing, and discloses a method for identifying heart risk by using night vital sign data mutation, comprising: performing scene classification on a breathing signal, and when the classification result is a fluctuation artifact state with ambiguity, further acquiring a signal quality index of an electrocardiogram (ECG) signal; cross-verification is performed on the fluctuation artifact state by using the signal quality index, the fluctuation artifact state is corrected to a physiological fluctuation state or a signal artifact state, and an ECG analysis model is adaptively selected according to the final classification result. The present application cross-verification is performed on the breathing scene classification by introducing the electrocardiogram signal quality index, the inherent ambiguity problem of the fluctuation artifact state is solved, the method can effectively distinguish the real physiological fluctuation from the technical artifact, thereby the analysis model of the two different nature scenes is targeted, and the accuracy of risk identification is improved.

Systems and methods for detecting worsening cardiac conditions such as worsening heart failure events are described. A system may include sensor circuits to sense physiological signals and signal processors to generate from the physiological signals first and second signal metrics. The system may include a risk stratifier circuit to produce a cardiac risk indication. The system may use at least the first signal metric to generate a primary detection indication, and use at least the second signal metric and the risk indication to generate a secondary detection indication. The risk indication may be used to modulate the second signal metric. A detector circuit may detect the worsening cardiac event using the primary and secondary detection indications.

This invention provides a benzothiadiazine compound that inhibits histone deacetylases, its preparation method, and its applications. The general structural formula is shown in Formula (I), or an isomer thereof, or a pharmaceutically acceptable salt, ester, or prodrug thereof. The benzothiadiazine compound of this invention has a novel structure and, as an inhibitor of histone deacetylases HDAC6 and HDAC1, can effectively inhibit the proliferation of K562 (human chronic myeloid leukemia cells) and NCI-H929 (human myeloma cells). It exhibits low cytotoxicity to normal cells and low potential cardiac risk, and holds promise as a highly effective and low-toxicity antitumor therapeutic agent.

In one embodiment, a method to track the cardiac health of a patient is described. The method includes connecting to at least one motion sensor configured to sense movement of a patient and receiving motion data from the at least one motion sensor. The method further includes monitoring an activity level of the patient based at least in part on the motion data and detecting a change in the activity level of the patient based at least in part on the motion data. The method also includes altering a monitoring status of the cardiac health of the patient for a predetermined period of monitoring time based at least in part on the change in activity level.

Method and apparatus for performing automatic cardiac diagnosis. The apparatuses described herein may be handheld devices which enables self-recording of cardiac signals by the patient, including entering relevant data by patients regarding their cardiac history, including cardiac disease risk factors, and / or current conditions and symptoms. Based on recorded cardiac signals, cardiac risk factors and the current symptoms, the apparatus may calculates a cardiac risk score and may provide simplified diagnostic information and actionable instructions to the patient.

The application provides a method and device for determining a cardiac risk parameter, the method comprising: obtaining a plurality of lead electrocardio vector signals of a target entity; converting the plurality of lead electrocardio vector signals into a plurality of time domain waveform graphs; obtaining a waveform difference value between a waveform maximum value and a waveform minimum value of each time domain waveform graph to obtain a plurality of waveform difference values; obtaining a time cost for each time domain waveform graph to reach the waveform maximum value to obtain a plurality of time costs; obtaining a time interval between two adjacent waveform maximum values on each time domain waveform graph to obtain a plurality of time intervals; determining an electrocardiogram risk parameter based on a standard deviation and a maximum value of the plurality of waveform maximum values, a standard deviation and a maximum value of the plurality of waveform difference values, a standard deviation and a mean value of the plurality of time intervals, and a standard deviation and a mean value of the plurality of time costs; and determining the cardiac risk parameter based on the electrocardiogram risk parameter. The application can improve the determination accuracy of the cardiac risk parameter.

A system and method for physiological analysis and feedback includes: multiple sensors configured to be worn on a user's skin and measure sweat data in real time, indicating levels of multiple corresponding sweat components; and one or more processors configured to receive the sweat data in real time. The processors apply the sweat data to a biomarker correlation model trained to correlate inputs including multiple sweat components, along with user-specific data including age, weight, and sex, with outputs including multiple physiological parameters. Two of the multiple physiological parameters may be oxygen consumption (VO2) and carbon dioxide elimination (VCO2). Additionally, the risk of coronary artery obstruction can be calculated by applying the calculated VO2 levels to a cardiac risk-related model trained to correlate VO2 levels with a risk level of coronary artery obstruction.

Methods and systems for estimating a left ventricular mass index (LVMI) value of a subject using ambulatory blood pressure of the subject and without need for transthoracic echocardiography & methods for treating LVH based thereon.

The present application relates to the technical field of physiological signal processing, and particularly relates to an early risk prediction method based on multi-modal physiological signal chaotic feature fusion. Through acquisition of multi-modal physiological signal time series data and phase space reconstruction to form a chaotic attractor, identification of an invariant manifold and a saddle point and tracking of a heteroclinic orbit to construct a cross-modal critical state correlation atlas, calculation of a geodesic distance between a current physiological state point and a critical transition path based on the atlas to generate a potential energy function distribution, deduction of an optimal evolution trajectory and a bifurcation point position under multi-modal collaborative constraints to generate an early risk evaluation result, identification of orbit drift using an actual observation sequence and backtracking of a Lyapunov exponent spectrum to rescale the invariant manifold topological dimension and the saddle point position to synchronously correct the correlation atlas to form a dynamically self-consistent closed loop. The method can accurately predict early cardiac risk and realize dynamic self-consistency, thereby improving evaluation accuracy.

The invention relates to the technical field of big data analysis, and discloses an intelligent monitoring method for the health state of an athlete based on big data analysis of wearable equipment. According to the method, by collecting physiological data, motion data, sleep and recovery data and environment data of an athlete in real time, the health state of the athlete can be monitored in real time, the problems of low efficiency and subjectivity caused by manual recording and experience judgment in a traditional health monitoring method are solved, and through data preprocessing and feature extraction, the health state of the athlete can be monitored in real time. In addition, the health risk of the athlete can be automatically identified and the fatigue degree and the heart risk can be accurately evaluated by combining a health state evaluation model established by a deep neural network, and the method not only provides personalized exercise intensity adjustment suggestions and rest and rehabilitation guidance for the athlete, but also improves the accuracy of exercise intensity adjustment. And a continuously tracked health file is formed, and scientific training decision support is provided for a coach team, so that the body health and safety of athletes are effectively guaranteed.