42 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.

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 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.

The invention provides an ECG-based ultrasonic 3D modeling method, which specifically comprises the following steps of: periodically scanning an ultrasonic simulation heart model, and synchronously acquiring a simulated ECG signal and a two-dimensional B-ultrasonic image; detecting an R wave peak value of the ECG signal by adopting a PT algorithm, and determining an ultrasonic image corresponding to the R wave peak value moment according to time synchronization; time windows of an isovolumetric systolic period and an isovolumetric diastolic period are dynamically determined based on the R wave peak value and the RR interval, and the ultrasonic images in the time windows are stored; a reference image is selected, similarity calculation and registration processing are carried out on all ultrasonic images of the same phase, and images meeting a threshold value are reserved for three-dimensional modeling. According to the method, the R-wave moment image is selected based on the simulated ECG signal and the ultrasonic heart simulation model, the same phase image is calculated according to the dynamic calculation isometric period time window and is screened, and the ultrasonic two-dimensional image of which the reference image is greater than or equal to the self-adaptive threshold value is screened out and stored for later three-dimensional reconstruction; and the accuracy of the ultrasonic heart simulation model is improved.

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.

Methods, systems, and devices for determining cardiovascular health indicators are described. The system may be configured to receive a photoplethysmogram (PPG) signal representing a pulse waveform of a user. The pulse waveform may include a first local maximum, a downward slope following the first local maximum, and a bending feature representing a transition from a systolic phase to a diastolic phase of the cardiac cycle. Further, the system may extract one or more morphological features from the pulse waveform and compare the one or more morphological features to one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of real foot ages. The system may determine a cardiovascular health indicator indicative of a cardiovascular health condition of the user relative to the user's real foot age, and cause the graphical user interface to display an indication of the cardiovascular health indicator.

An injection system is described that receives, from one or more sensors, a first group of one or more signals indicating a current volume of injection fluid dispensed from a fluid reservoir at a first time. The injection system determines, based on the first group of one or more signals, that a difference between a dispensed volume limit and the current volume of the injection fluid dispensed from the fluid reservoir at the first time is less than a necessary volume of fluid required to complete both a systolic injection phase and a diastolic injection phase. The injection system further, responsive to determining that the difference is less than the necessary volume of fluid required to complete both the systolic injection phase and the diastolic injection phase, controls the injection system to refrain from performing each of the systolic injection phase and the diastolic injection phase.

The invention relates to an SCG feature point recognition method based on ECG reference and waveform curvature features, belongs to the technical field of SCG signal feature extraction, and solves the problems that in the prior art, the robustness is reduced, the feature point recognition application range is limited, and the recognition precision is low due to the fact that the form of an SCG signal changes along with different parameters such as motion artifacts, respiratory cycles, gender and age. The method comprises the following steps: acquiring synchronous ECG and SCG signals, and preprocessing the ECG and SCG signals; performing single-period waveform extraction on the SCG preprocessed signal based on the ECG preprocessed signal to obtain a single-period waveform of the SCG signal; performing curvature method processing on the single-period waveform of the SCG signal to obtain curvature characteristics of the single-period waveform; dividing the curvature characteristics into a systolic period area SCG1 and a diastolic period area SCG2 according to the period length proportion of the predicted systolic period to the equal-length SCG signal segments; and feature point marking is carried out through an extreme value taking method after peak searching. Accurate mapping of heart mechanical activities is realized.

The embodiment of the invention provides a color Doppler ultrasonic hemodynamic analysis method for an artery in a semi-open mouth meninx, which comprises the following steps of: taking a semi-open mouth of a patient, and maintaining an acoustic window through an occlusion object to enlarge a subtemporal fossa; a linear array or convex array probe is selected according to the body type of a subject, and ultrasonic equipment parameters are set; scanning in the subtemporal fossa to identify the trunk of the maxillary artery (MXA) and the branch of the middle meninx artery (MMA); measuring inner diameters and hemodynamic parameters of the MXA and the MMA, wherein the parameters comprise a systolic peak flow rate (PSV), an end-diastolic flow rate (EDV), a resistance index (RI) and a pulsation index (PI); compared with the prior art, the subtemporal fossa is expanded by 5-10 mm through the medical silica gel occlusion object with the occlusion thickness of 3-8 mm, the anterior-posterior diameter of the subtemporal fossa is expanded to 25-35 mm, the upper-lower diameter of the subtemporal fossa is expanded to 18-28 mm, and bone shielding of the condylar process on the initial section of the middle meninx artery (MMA) is thoroughly relieved.

The invention belongs to the technical field of animal model construction, and particularly relates to a method for establishing a heart failure mouse model with an exceptional ejection fraction, which adopts an adenine-containing diet to continuously feed a mouse for 25-30 days. A C57BL / 6J mouse is continuously fed for 25-30 days by adopting adenine-containing diet, the heart function of the mouse is detected by using a vevo2100 small animal ultrasonic image system, and the phenomena of ejection fraction increase, cardiac output reduction, left ventricular diameter in systolic and diastolic periods, left ventricular volume reduction in systolic and diastolic periods, slight heart fibrosis and the like of the mouse are found. It is proved that adenine diet can induce the heart failure with the supernormal left ventricular ejection fraction of the mouse, and a feasible direction is provided for research of the heart failure.

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 present invention provides a method, device, and ultrasonic imaging device for determining left ventricular parameters. The method comprises: collecting two-dimensional ultrasonic cardiac data of a target subject over a continuous period of time, the continuous period being greater than one cardiac cycle, the two-dimensional ultrasonic cardiac data comprising multiple frames of two-dimensional ultrasonic cardiac images; determining multiple target frame images from the multiple frames of two-dimensional ultrasonic cardiac images, the target frame images comprising multiple frames of end-diastolic images corresponding to the end-diastolic phase and multiple frames of end-systolic images corresponding to the end-systolic phase; performing image segmentation on the target frame images to determine the left ventricular contour of the target frame images; determining the apex point, the junction point between the left ventricular septum and the mitral valve, and the junction point between the left ventricular wall and the mitral valve in the left ventricular contour; and determining left ventricular parameters based on the left ventricular contour, the apex point, the junction point between the left ventricular septum and the mitral valve, and the junction point between the left ventricular wall and the mitral valve. The present invention balances the efficiency and accuracy of left ventricular measurement.

The application relates to a left ventricular outflow tract stent and a delivery system, wherein the left ventricular outflow tract stent comprises a plurality of connecting beams, the connecting beams abut against the inner wall of the left ventricular outflow tract in a propped state, and at least part of the connecting beams abut against the failed leaflet of the native mitral valve. In the propped state of the left ventricular outflow tract stent, the connecting beams close to the mitral valve side can abut against the native leaflet, so that the native leaflet can be squeezed between the interventional mitral valve prosthesis and the left ventricular outflow tract stent provided in the embodiment; when the left ventricle is in the systolic phase, the native leaflet can be limited by the interventional mitral valve prosthesis and the left ventricular outflow tract stent provided in the embodiment and cannot swing with the blood flow at will, so that the native leaflet can effectively avoid shielding the left ventricular outflow tract, and blood can normally flow from the left ventricle to the aorta.

The invention provides a subclinical valve leaflet thrombus auxiliary identification method and system after transcatheter aortic valve replacement and a signal acquisition device, and belongs to the technical field of heart information acquisition and identification. The subclinical valve leaflet thrombus auxiliary identification method is used for auxiliary diagnosis of subclinical valve leaflet thrombus after transcatheter aortic valve replacement and comprises the steps that heart sound signals are collected and processed, preset parameters of the heart sound signals are obtained, and the preset parameters at least comprise time, frequency and energy; determining a systolic period according to the heart sound signal; identifying an energy highest point in the characteristic interval of the contraction period; acquiring a characteristic frequency corresponding to the highest point of the energy, judging whether the characteristic frequency is greater than a threshold frequency or not, and if so, marking the heart sound signal as an abnormal heart sound signal caused by the subclinical valve leaflet thrombus; and if not, marking the heart sound signal as a normal heart sound signal. According to the method, the problem that the applicable crowd range is limited due to the fact that multi-dimensional computed tomography is mainly adopted in subclinical leaflet thrombus detection is solved.

An intracardiac ventricular pacemaker having a motion sensor is configured to produce a motion signal including an atrial systolic event and a ventricular diastolic event indicating a passive ventricular filling phase, set a detection threshold to a first amplitude during an expected time interval of the ventricular diastolic event and to a second amplitude lower than the first amplitude after an expected time interval of the ventricular diastolic event. The pacemaker is configured to detect the atrial systolic event in response to the motion signal crossing the detection threshold and set an atrioventricular pacing interval in response to detecting the atrial systolic event.

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.

A computer-implemented method for detecting parameters indicative of a change in activation of a sympathetic nervous system and a change in activation of a parasympathetic nervous system in a subject in a transition from a basal state to a perturbed state comprises calculating a power ratio between the powers of a power spectrum of a systolic time interval and a diastolic time interval in LF and HF frequency bands.

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 invention discloses a cardiac cycle determination method, device and equipment and a readable storage medium, and the method comprises the steps: reading a fetal heart video frame by frame, and determining each current frame and a plurality of frames before the current frame as a video segment; using the trained three-dimensional convolutional neural network model to extract the time sequence characteristics of each video segment, and performing state prediction processing on the last frame of each video segment based on the time sequence characteristics to obtain a prediction result of the time phase state of each frame in the fetal heart video; the time phase state is a systolic period or a diastolic period; correcting the prediction result in combination with the cardiac change characteristics to obtain a correction result; and obtaining the cardiac cycle of the fetal heart video by using the correction result. According to the method, the model only predicts whether the last frame of the video frame belongs to the systolic period or the diastolic period based on the time sequence characteristics, the accuracy of the prediction result can be improved, the prediction result is corrected in combination with the cardiac change characteristics, and finally the more accurate cardiac cycle of the fetal heart video is obtained based on the correction result.

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.

Devices, systems and methods provide forms of asymptomatic diaphragmatic stimulation (ADS) therapy that affect pressures within the intrathoracic cavity, including: 1) dual-pulse ADS therapy, during which a first ADS pulse is delivered during a diastolic phase of a cardiac cycle and a second ADS pulse is delivered during a systolic phase, 2) paired-pulse ADS therapy, during which a first ADS pulse is delivered, closely followed by a second ADS pulse, with the second ADS pulse functioning to extend or enhance a phase of a transient, partial contraction of the diaphragm, and 3) multiple-pulse ADS therapy, during which a stream of ADS pulses is delivered, wherein the time between pulses is based on heart rate. Devices, systems and methods also monitor electromyography (EMG) activity of the diaphragm relative to baseline activity to assess the health of a diaphragm subject to ADS therapy and to adjust ADS therapy parameters or sensing parameters.

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.