20 results about "Systolic phase" patented technology

The invention relates to the technical field of cardiovascular examination, in particular to a cardiovascular ultrasonic examination quality control method and system based on artificial intelligence, and the method comprises the following steps: obtaining a valve motion image through ultrasound, extracting a valve leaflet edge coordinate, recognizing a valve opening center and a valve leaflet tip through a convolutional neural network, and generating motion data; the method comprises the following steps: calculating a deviation degree based on trajectory fitting, judging stability, analyzing peak values of a systolic period and a diastolic period, calculating a symmetry difference, extracting a valve opening area, carrying out self-encoder analysis in combination with the symmetry difference to generate a fluctuation amplitude, and carrying out normalization processing to complete hierarchical classification so as to generate a quality control result. By acquiring leaflet edge points and combining a convolutional neural network to identify key features, accurate extraction of motion data, finer trajectory fitting and offset calculation, more scientific quantitative evaluation of symmetry difference and more stereoscopic area fluctuation and feature fusion analysis are realized, hierarchical and hierarchical synchronization is realized through normalization processing, and the accuracy of the motion data is improved. And the result is more credible, more stable and higher in clinical value.

The invention provides a left heart data analysis method and system based on image segmentation, and relates to the technical field of medical image processing. Comprising the following steps: acquiring cardiac ultrasound image data and preprocessing; performing image segmentation on the preprocessed image data to obtain a segmentation result including a left ventricle, a left ventricular wall and a left atrium; searching a boundary line of the left ventricle and the left atrium, selecting two boundary points corresponding to the minimum and maximum abscissas in the boundary line as left and right mitral valve ring points respectively, and establishing a long axis based on the mitral valve ring midpoint and the cardiac apex point; modeling the ventricle into a structure formed by superposing a plurality of discs along the long axis direction, and obtaining the diameter of the corresponding disc; based on the obtained diameters of the corresponding discs, the left ventricular volumes in the diastolic period and the systolic period are calculated respectively, and the left ventricular ejection fraction is calculated. According to the method, image segmentation and geometric modeling are fused to achieve accurate calculation of the cardiac ejection fraction, meanwhile, the accuracy of cardiac structure image segmentation is improved, and an interpretable ejection fraction estimation process is achieved.

The invention discloses an aortic valve function evaluation device which comprises a processor and a memory, and the memory stores programs or instructions capable of running on the processor. When the program or the instruction is executed by the processor, the following steps are realized: acquiring a first echocardiogram of a target object corresponding to a final systole and a second echocardiogram of a target object corresponding to a final diastole in a cardiac cycle, and acquiring medical record data of the target object; determining a first mask pattern corresponding to the first echocardiogram and a second mask pattern corresponding to the second echocardiogram according to the first echocardiogram, the second echocardiogram and an aortic valve segmentation model; determining an aortic valve morphological index of the target object according to the first mask pattern and the second mask pattern; determining aortic valve functional indexes of the target object according to the medical record data of the target object; and determining and outputting an aortic valve function evaluation result of the target object according to the first mask pattern, the second mask pattern, the aortic valve morphological index and the aortic valve functional index.

The invention discloses a myocardial stress determination method, device and equipment based on cardiac ultrasound and a medium, and relates to the field of myocardial stress evaluation.The method adopts a double-mapping network mode and comprises the steps that a cardiac medical image is acquired and labeled, and a feature database of the cardiac bimodal image in the systolic period and the diastolic period is constructed; constructing a first mapping network and performing training, and then performing segmentation and three-dimensional reconstruction on a to-be-processed cardiac ultrasound image to obtain a double-ventricle three-dimensional model; obtaining a finite element simulation model of the double ventricles and carrying out finite element simulation calculation to obtain a ventricle wall stress-strain simulation result; constructing a heart model-simulation result pairing database; constructing a second mapping network; training the second mapping network based on the heart model-simulation result pairing database; and determining a myocardial stress result based on the trained second mapping network. According to the method, the current situation that the myocardial stress cannot be effectively measured and evaluated by a traditional cardiac ultrasound method is effectively improved.

The disclosure provides a subclinical valve leaflet thrombosis auxiliary identification method, system and signal acquisition device after transcatheter aortic valve replacement, belonging to the technical field of heart information acquisition and identification. The subclinical valve leaflet thrombosis auxiliary identification method is used for auxiliary diagnosis of subclinical valve leaflet thrombosis after transcatheter aortic valve replacement, comprising: collecting a heart sound signal, processing the heart sound signal, obtaining preset parameters of the heart sound signal, and the preset parameters at least including time, frequency and energy; determining a systolic period according to the heart sound signal; identifying the highest energy point in the characteristic interval of the systolic period; obtaining the characteristic frequency corresponding to the highest energy point, and judging whether the characteristic frequency is greater than the threshold frequency, if yes, marking the heart sound signal as an abnormal heart sound signal caused by subclinical valve leaflet thrombosis; if not, marking the heart sound signal as a normal heart sound signal. The method solves the problem that the subclinical valve leaflet thrombosis detection mainly uses multi-dimensional computed tomography, which leads to limited scope of application.

Systems and apparatus are provided for using a non-invasively monitored uncalibrated pressure signal from a patient to compute cardiac output and other cardio-vascular parameters, thereby providing safe, effective, reliable and low cost monitoring of patient health. Wearable devices are disclosed that include a sensor array for obtaining patient data that is used to provide an estimate of aortic systolic blood pressure, cardiac output, and end-systolic elastance that may be determined without the need for a cuff.

Methods, systems, and devices for cardiovascular health metric determination are described. A system may be configured to receive a photoplethysmogram (PPG) signal representative of a pulse waveform for a user. The pulse waveform may include a first local maximum, a downward slope following the first local maximum, and a curved feature representative of a transition from a systolic phase to a diastolic phase of a cardiac cycle. Additionally, the system may extract one or more morphological features from the pulse waveform and compare the one or more morphological features with one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The system may determine a cardiovascular health metric that indicates a cardiovascular health of the user relative to a chronological age of the user and cause a graphical user interface to display an indication of the cardiovascular health metric.

The application provides a kind of ventricular assist device control device, method, ventricular assist system and electronic equipment, it is related to the field of medical instrument control.The control device includes: timing judgment module, characteristic flow calculation module and motor control module;Timing judgment module is used to divide target cardiac cycle into systolic phase and diastolic phase;Characteristic flow calculation module is used to divide the real-time flow of ventricular assist device in target cardiac cycle into systolic flow in systolic phase and / or diastolic flow in diastolic phase, and calculate the characteristic cardiac output of ventricular assist device in target cardiac cycle according to systolic flow and diastolic flow;Motor control module is used to adjust motor speed.Using the control device provided by the embodiment of the application can make the cardiac output of ventricular assist device more in line with the physiological law of heart, realize the stable control of cardiac output.

The invention provides a method and a device for identifying a diastolic period and a systolic period of a CBCT image, and the method comprises the steps: scanning a heart region through CBCT equipment, and collecting a plurality of two-dimensional projection images of the heart region; preprocessing the plurality of two-dimensional projection images so as to suppress scanning errors of CBCT equipment and enhance the distinction degree of heart motion signals and non-motion signals in the CBCT images; based on a PCA algorithm, extracting a feature value of each preprocessed two-dimensional projection image, the feature values being used for representing a heart motion state; fitting the characteristic values of all the two-dimensional projection images into a characteristic curve according to an acquisition time sequence; and identifying a peak value and a valley value of the characteristic curve, and determining that the two-dimensional projection image corresponding to the peak value is a diastolic period projection image and the two-dimensional projection image corresponding to the valley value is a systolic period projection image. According to the application, accurate and efficient identification of the key cardiac stage of the heart can be realized, and stable and reliable image support is provided for subsequent clinical operation.

This invention relates to a prediction system, electronic device, and readable storage medium for sepsis-associated encephalopathy (SEEE). The system includes: a data acquisition module for acquiring measurements of neuron-specific enolase levels, transcranial ultrasound-measured peak systolic velocity of the left middle cerebral artery, and an APACHE II score reflecting disease severity; and a SEEE prediction module for obtaining the risk probability of sepsis-associated encephalopathy in sepsis patients using a pre-built and trained prediction model based on the acquired neuron-specific enolase levels, transcranial ultrasound-measured peak systolic velocity of the left middle cerebral artery, and APACHE II score reflecting disease severity. Compared with existing technologies, this invention can predict the risk of sepsis-associated encephalopathy in patients at an early stage and has good accuracy, simplicity, and practicality.

Devices, systems and methods affect cardiac function of a patient by delivering a stimulation to a right hemisphere of a diaphragm of the patient during a diastolic phase of a cardiac cycle of the patient, wherein the stimulation results in an asymptomatic, transient, partial contraction of the right hemisphere of the diaphragm; and delivering a stimulation to the left hemisphere of the diaphragm during a systolic phase of the cardiac cycle, wherein the stimulation results in an asymptomatic, transient, partial contraction of the left hemisphere of the diaphragm.

This invention discloses a parameter identification method for a lumped parameter model of systemic circulation, belonging to the field of lumped parameter model technology. The method includes: constructing the state equation of the lumped parameter model of systemic circulation based on Kirchhoff's voltage and current laws to obtain the systemic circulation control equation; calculating the duration ratio of systolic to diastolic phases and stroke volume based on routine clinical measurement data, constructing classic aortic pressure and flow waveforms, and performing registration processing; calculating central venous pressure and flow based on routine clinical measurement data; and using the registered aortic inlet pressure and flow, central venous pressure and flow, and systemic circulation control equations to calculate the values ​​of each parameter in the parameter model. This invention, by periodically stretching the classic reference waveform, matches the waveform extreme values ​​with the patient's measured blood pressure and cardiac output, indirectly reconstructing the aortic inlet pressure and flow curve reflecting individual characteristics under non-invasive conditions, significantly improving the model's individualization and clinical compatibility.

The application discloses an intervention postoperative femoral artery compression force analysis method based on multi-source physiological signals, which is used in the intervention postoperative hemostasis control field, and the method comprises the following steps: collecting an electrocardio signal, a dorsalis pedis pulse wave signal and a compression force with a pressure safety upper limit as a constraint, taking the electrocardio ventricular systolic wave peak as a trigger to calculate a damping rate and a pulsatility index; inputting the damping rate, the pulsatility index and the compression force into a fuzzy reasoning model to obtain a compression force instruction; applying a compression force pulse in the ventricular systolic period and withdrawing a compression force increment in the ventricular diastolic period; when rhythm fluctuation exceeds a preset threshold, the periodic pressurization is suspended and a constant pressure mode is switched; when the compression force is stable, the amplitude recovery meets the standard, and the pulsatility index variance is continuously lower than a convergence threshold for multiple periods, it is determined that the hemostasis is completed, and a graded decompression is performed. The application can improve the compression safety and accuracy, eliminate the risk of abnormal heart rhythm control misplacement and false standard of hemostasis endpoint.

The application provides a method and device for identifying diastolic and systolic phases of a CBCT image. The method comprises: scanning a heart region by a CBCT device to collect a plurality of two-dimensional projection images of the heart region; preprocessing the plurality of two-dimensional projection images to suppress scanning errors of the CBCT device and enhance the distinguishability of the heart motion signals and non-motion signals in the CBCT images; extracting eigenvalues of each of the preprocessed two-dimensional projection images based on a PCA algorithm, wherein the eigenvalues are used to represent the heart motion state; fitting the eigenvalues of all the two-dimensional projection images into a characteristic curve in the order of acquisition time; identifying the peak and valley of the characteristic curve, determining the two-dimensional projection image corresponding to the peak as a diastolic phase projection image, and determining the two-dimensional projection image corresponding to the valley as a systolic phase projection image. The application can accurately and efficiently identify the key heart phases, and provide stable and reliable image support for subsequent clinical operations.

The application relates to the technical field of medical artificial intelligence, and particularly discloses a heart mechanical efficiency evaluation method based on a strain-blood flow coupling model, which comprises the following steps: synchronously collecting electrocardio, heart sound and photoelectric volume pulse wave signals, adopting master-slave clock synchronization and R wave triggering alignment to realize multi-modal signal timing calibration; extracting myocardial strain rate, stroke volume and systolic energy consumption coefficient based on the calibrated signals, and constructing a strain-blood flow coupling efficiency index; dynamically calibrating the coupling efficiency index through a double-parameter self-calibration mechanism of heart rate drift and heart rate change rate; and finally combining multi-scale feature extraction and a gradient boosting decision tree model to output a heart mechanical efficiency grade and an ischemia risk probability; the application solves the problems of missing dynamic load evaluation, isolated multi-modal parameters and insufficient individual difference adaptability in the prior art, and realizes early myocardial ischemia detection sensitivity and wearable device-level real-time monitoring capability.

The application provides a sepsis cardiomyopathy prognosis risk stratification system and a construction method thereof. The method first classifies SIC patients through multi-modal cardiac ultrasound, and then adopts a machine learning algorithm to screen out key ultrasound variables: left ventricular overall longitudinal strain (GLS) and tricuspid annular plane systolic excursion (TAPSE) which are strongly related to prognosis. Based on these variables, cluster analysis is performed, and SIC risk subphenotypes with significant prognosis difference are successfully constructed, and the mortality of the high-risk subtype is significantly higher. The study found that a single indicator, TAPSE, has extremely high predictive efficiency in identifying high-risk subphenotypes and can achieve rapid bedside assessment. The application first establishes a prognosis risk stratification system for SIC, simplifies the evaluation process, realizes early identification of high-risk patients, and provides key technical support for clinical precise intervention and improvement of prognosis.

This invention discloses a diabetic foot treatment and rehabilitation device and control system based on bilateral lower limb assisted circulation. The system includes an electrocardiogram (ECG) signal acquisition module, a signal processing system, and a control module. The ECG signal acquisition module converts acquired ECG signals into digital ECG signals. The signal processing system outputs diastolic and systolic wavebands based on the digital ECG signals and generates a first inflation / deflation command and a second inflation / deflation command. The control module converts the commands into a first inflation / deflation signal and a second inflation / deflation signal, respectively. The execution device controls the healthy side's inflation bladder to perform inflation / deflation actions within the diastolic waveband based on the first inflation / deflation signal, and controls the affected side's inflation bladder to perform inflation / deflation actions within the systolic waveband based on the second inflation / deflation command. This invention obtains the device's pressurization node through ECG wave analysis, enabling the control system to control the device's coordinated operation according to time nodes, thereby improving the device's effectiveness.

An apparatus for treating backflow of blood through a native heart valve (16) comprises a selective occlusion device, the first or second native leaflet (16a, 16b) is sized and configured to be implanted in the native heart valve (16) and selectively operate with at least one of the first or second native leaflet (16a, 16b) to allow blood flow through the native heart valve (16) when the cardiac cycle is in the diastolic period and to reduce blood flow regurgitation through the native heart valve (16) when the cardiac cycle is in the systolic period. A clip structure (50) is coupled with the selective occlusion device. The clip structure (50) is configured to be affixed to an edge of at least one of the first or second native leaflet (16a, 16b) to secure the selective occlusion device to the native heart valve (16).

Disclosed herein is a system for assessing ablation lesions. The system includes an ablation catheter configured to ablate a target cardiac tissue site to form an ablation lesion thereon, and a mechanical probe operable to impart mechanical force to the target cardiac tissue site. The mechanical probe includes at least one sensor configured to measure a mechanical response of the target cardiac tissue site to the mechanical force. The system further includes a controller communicatively coupled to the mechanical probe, and configured to determine systolic and diastolic stiffness values of the target cardiac tissue site based on the mechanical response. The controller is further configured to determine a transmurality value of the ablation lesion based on the determined systolic and diastolic stiffness values.

The application discloses a surgical risk prediction method and device and a storage medium, and relates to the technical field of medical treatment. The method comprises the following steps: acquiring preoperative multidimensional evaluation information of a target object; wherein the multidimensional evaluation information comprises a left ventricular ejection fraction, a first surgical risk score, a second surgical risk score, an NT-proBNP value, a systolic pulmonary artery pressure and an extracorporeal life support application state; inputting the preoperative multidimensional evaluation information into a surgical risk assessment model to obtain a surgical risk prediction result of the target object; wherein the surgical risk prediction result comprises at least one of a surgical risk grade, a total mortality risk value, a composite adverse event risk value and a predicted left ventricular ejection fraction change amount.