5964 results about "Cardiology" patented technology

An auto-occlusion safety device is provided for use in preventing rupture of infusion components during rapid infusion procedures. An occlusion valve is disclosed having a threshold pressure that is less than a rupture tolerance of the infusion system components. As the fluid pressure of the infusion system increases beyond the threshold pressure of the occlusion valve, the occlusion valve is automatically activated thereby occluding fluid flow upstream from a vulnerable infusion component. An accumulator is further disclosed for use in combination with the safety device to compensate for time delays in full activation of the occlusion valve.

A noninvasive blood pressure monitor. The noninvasive blood pressure monitor may include an inflatable cuff, a pressure transducer, one or more air pumps, and a processor. The processor may control the air pump(s) so as to initiate inflation of the cuff. The processor may also identify an oscillometric signal in an output of the pressure transducer, and may determine an envelope of the oscillometric signal. The processor may also determine one or more characteristics of the envelope of the oscillometric signal, and may control the air pump(s) so as to stop inflation of the cuff based on the one or more characteristics of the envelope of the oscillometric signal.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to a method for operating an implanted, ventricular assist system (2), comprising the following steps: a) determine a first impedance parameter at a first point in time by means of the assist system (2), b) determine a second impedance parameter at a second point in time by means of the assist system (2), c) at least determine a change of the impedance parameter using the first impedance parameter and the second impedance parameter, or compare at least the first or second impedance parameter to a threshold value.

An intravascular heart pump assembly can include a rotor with at least one impeller blade, and a cannula. The present application describes various cannulas that can be manufactured from multiple layers of material to improve flexibility, manufacturability, and durability without increasing an outer diameter of the cannula. In one embodiment, the cannula includes an inflow section having a sheet formed of a shape memory material embedded within a polymer and having at least one lateral hole or aperture in the inflow section. The at least one lateral hole is defined by a first hole in the sheet and a second hole in the outer polymer layer of the cannula. The first hole and the second hole overlap so that blood can enter the cannula through the holes.

A transport container and methods for preserving a heart at a hypothermic temperature. The transport container can maintain the heart at a temperature of 6-8° C. for improved results after transplantation. The preservation of the heart within this range can improve transplantation outcomes.

Systems and methods for determining the positioning of intracardiac devices, such as intracardiac blood pump assemblies, using electrical sensors configured to sense electrical potential as it propagates through the heart. In one example, the present technology provides an intracardiac device with one or more electrical sensors mounted thereon, and a controller configured to determine the absolute or relative location of the intracardiac device based on the timing, shape, and / or amplitude of the electrical signals received from the one or more sensors.

An intravascular blood pump for percutaneous insertion into a patient's blood vessel is provided. The blood pump comprises a pump casing and an impeller arranged in the pump casing. The blood pump further comprises a drive unit for rotating the impeller, the drive unit comprising a plurality of posts. Each of the posts has an impeller-side end pointing towards the impeller with a front surface facing the impeller. A coil winding is disposed around each of the posts so as to create magnetic field lines running through the front surface of each of the posts. The front surface of at least one of the posts comprises a concavity in which the front surface is inclined downwards towards a central area of the front surface so as to concentrate at least a part of the magnetic field lines running through the front surface.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

Apparatus and methods are described including a ventricular assist device that includes a frame having struts that define a plurality of cells. In its non-radially-constrained configuration, the frame includes a generally cylindrical portion. A tube defines blood outlet openings, and a portion of the tube is disposed outside the frame and is coupled to the generally cylindrical portion of the frame, such that the portion of the tube conforms with a structure of struts of the frame. An inner lining is coupled to an inside of the generally cylindrical portion of the frame, such as to provide the generally cylindrical portion of the frame with a smooth inner surface. An impeller is disposed at least partially inside the generally cylindrical portion of the frame and is configured to pump blood through the tube and out of the one of more blood outlet openings. Other applications are also described.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention relates to a bearing device (100) for a cardiac support system. The bearing device (100) comprises a stand unit (105) and an impeller (110). The stand unit (105) is designed to support the impeller (110) such that it can rotate. The impeller (110) is designed to rotate during an operation of the cardiac support system in order to convey a pump fluid flow (115). The impeller (110) is designed to enclose at least one subsection (120) of the stand unit (105) in the assembled state of the bearing device (100), wherein an intermediate space (125) for guiding a flushing fluid flow (130) is provided between the subsection (120) and the impeller (110). At least one flushing outlet (135) is formed in the impeller (110). The flushing outlet (135) is designed to discharge the flushing fluid flow (130) from the intermediate space (125) by means of centrifugal force when the cardiac support system is in operation.

Systems and methods are disclosed for creating an interactive tool for determining and displaying a functional relationship between a vascular network and an associated perfused tissue. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; receiving a patient-specific tissue model, including a tissue region associated with the at least one vessel of the patient; receiving a selected area of the vascular model or a selected area of the tissue model; and generating a display of a region of the tissue model corresponding to the selected area of the vascular model or a display of a portion of the vascular model corresponding to the selected area of the tissue model, respectively.

The present disclosure provides a method and system for analyzing unipolar electrophysiological (EP) signals acquired by a multi-electrode catheter placed in a ventricle of a patient's heart. The method includes receiving unipolar EP signals, extracting unipolar far-field signals from the unipolar EP signals, and analyzing the extracted unipolar far-field signals to estimate the distribution of scar regions across a thickness of wall tissue of the ventricle. Using the estimated distribution, a unipolar far-field EP map showing the scar regions is generated. The method further includes displaying the unipolar far-field EP map including the scar regions to a user. The system comprises a display device and a processor configured to perform the steps of the method.

Various aspects of the present disclosure are directed towards apparatuses, systems, and methods that may include a blood pump. The blood pump may include a flexible distal tip having a distal end segment, an intermediate segment, and a proximal segment, each segment having various shapes and / or hardness. The blood pump may include a cannula and a can element coupling the flexible distal tip to the cannula.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

The invention discloses an anesthesia early warning method based on multi-source signal fusion. The anesthesia early warning method comprises the following steps that physiological signals in the anesthesia process are collected in real time and preprocessed; a multi-modal Transform attention network is adopted to extract correlation features in the time sequence physiological signals, and multi-modal fusion features are obtained; predicting the fusion feature at the next moment by using a long-short-term memory neural network to obtain a prediction error; updating a noise and observation covariance matrix of the Kalman filter according to the prediction error; the dynamically adjusted Kalman filter calibrates the multi-modal fusion features in real time; according to the calibration characteristics, anesthesia depth and consciousness state prediction values are calculated in real time, and early warning is output. The real-time performance, the stability and the accuracy of anesthesia state monitoring are improved.

The invention relates to a motor housing module (110) for sealing a motor compartment of a motor of a heart support system. The motor housing module (110) has at least one feed-through portion (205), at least one feed-through line (210), and at least one contact pin (215). The feed-through portion (205) is designed to establish an electrical connection between the heart support system and a connection cable in order to externally contact the heart support system. The at least one feed-through line (210) is embedded in the feed-through portion (205) and extends through the feed-through portion (205). The feed-through line (210) can be connected to the motor and to the connection cable. A first end of the at least one contact pin (215) is embedded in the feed-through portion (205) and a second end of the contact pin (215) projects from the feed-through portion (205) on a side facing away from the motor compartment. The second end of the contact pin (215) can be connected to a sensor line to at least one sensor of the heart support system and to the connection cable.

Systems and methods for providing hemodynamic support to a patient with an intravascular blood pump are disclosed. In some implementations, the blood pump includes a motor and an improved motor cable for delivering electrical power to the motor. The motor includes a stator with one or more coils. The motor cable includes one or more electrical conduits. The motor cable also includes a tail portion and a head portion. In some implementations, the head portion may have an O-shape or a C-shape. The motor cable may reduce the complexity of assembling the blood pump. For example, the motor cable may reduce the risk of shorting the one or more coils and / or the one or more electrical conduits.

The invention relates to an electronics module (102) for a ventricular assist device, wherein the ventricular assist device has a motor housing for accommodating a pump motor. The electronics module (102) comprises an electronics section (204) for accommodating at least one electronic component (206) and / or at least one electrically conductive contacting element (208), and a coupling section (202) designed as a joint between the motor housing (104) and the electronics section (204) or as a separate component to be joined, wherein the motor housing (104) and the electronics section (204) are combined or can be combined via the coupling section (202) with one another to form a fluid-tight module housing (104) to be arranged in a blood vessel.

The invention discloses a non-contact heart rate detection method, system and device based on visual Transform and multi-scale feature aggregation and a medium. The method comprises the steps that a face visible light video is obtained, the unified video frame rate is re-sampled through the frame rate, face key point positioning and region division are carried out on each frame of image, and a face visible light video sequence is obtained; performing time migration operation on the video sequence to obtain a difference frame sequence, performing channel fusion on the video sequence and a difference frame, and performing down-sampling on a spatial dimension to obtain a low-resolution feature tensor; constructing a non-contact heart rate extraction model, inputting the low-resolution feature tensor into the non-contact heart rate extraction model, and outputting a heart rate value of the target; the non-contact heart rate extraction model comprises a feature enhancement module, a multi-scale mask feature aggregation module, a Transform time sequence modeling module and an rPPG predictor, the system, the device and the medium are used for achieving the non-contact heart rate detection method based on visual Transform and multi-scale feature aggregation, and the precision of rPPG signal extraction is improved.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention belongs to the technical field of medical signal processing, and particularly relates to a heart sound and electrocardio combined coronary heart disease detection method based on time-frequency fusion, which comprises the following steps: S1, performing data preprocessing on PCG and ECG original signals, including noise reduction, baseline drift elimination, power line interference elimination and the like, and performing normalization processing on the PCG and ECG signals to obtain a normalized PCG and ECG signal; a Welch method is adopted to generate power spectrum signals of heart sound and electrocardio, and time domain signals and power spectrum signals of PCG and ECG are segmented into equal-length fragments. In order to effectively solve the problem of inaccurate diagnosis caused by poor single-modal data quality, a heart sound and electrocardio dynamic fusion module is constructed based on a time-frequency fusion result of two signals, dynamic weight distribution is carried out, a proper classifier is selected for classification, and the diagnosis accuracy is improved by supplementing frequency domain information. And the weight is distributed according to the contribution of the heart sound and the electrocardiosignal, so that the detection and classification result of the coronary heart disease is more accurate.

The invention relates to the technical field of biomedical signal processing and intelligent health monitoring, and particularly discloses an intelligent heart rate monitoring method and system based on heart sound and electrocardiosignals, heart sound signals and electrocardiosignals of a user are collected in real time, quality evaluation is conducted on the collected signals through signal quality indexes, and accurate signals are extracted; extracting time domain and frequency domain features of the first heart sound and the second heart sound of the heart sound signal, fusing multiple features by adopting wavelet transform and a main frequency analysis algorithm, and calculating a heart sound abnormal coefficient to evaluate the abnormal degree of the heart sound signal; the vibration amplitude and frequency characteristics of P waves in the electrocardiosignals are extracted, and an electrocardiograph abnormal coefficient is calculated through a vibration amplitude abnormal coefficient and a vibration frequency fluctuation coefficient and used for evaluating the stability of the electrocardiosignals; based on the heart sound abnormal coefficient and the electrocardiogram abnormal coefficient, a decision tree model is constructed, a heart rate abnormal index is dynamically calculated, dynamic monitoring of the heart rate is achieved, a dynamic early warning mechanism is conducted, and heart rate abnormal information is output.

The invention relates to a sensor head device for a heart support system, wherein the sensor head device has at least one sensor carrying element, wherein the sensor carrying element has at least one sensor cavity for accommodating at least one sensor and / or at least one signal transmitter cavity for accommodating at least one signal transmitter.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention discloses a radio frequency ablation electrode control method which comprises the following steps: S1, initializing an ablation temperature target value, a safety limit value and a power adjusting range, and loading a pre-stored anal fistula ablation three-stage feature library and impedance-temperature correlation degree thresholds of all stages; s2, collecting the electrode temperature, the electrode-tissue impedance and the ablation duration in real time, and judging the current ablation stage according to the matching relationship between the ablation duration and the three-stage feature library; s3, formulating a state judgment rule according to the current stage; s4, a fuzzy PID algorithm is adopted to calculate a power basic adjusting amount, a power dynamic compensation amount is calculated, and final output power is determined; and S5, repeating the steps S2-S4 to realize closed-loop control, triggering first-level early warning when the parameters are close to the boundary, triggering second-level alarm when the parameters exceed the safety limit value, and recording abnormal data. The stage adaptability is high, the problem of temperature adjustment lag can be solved, the adjustment precision is accurate enough, and the ablation effect can be improved.

The invention discloses a peripheral vein puncture blood vessel pressure monitoring system for image enhancement examination based on multi-modal fusion, and relates to the technical field of medical image assistion.The peripheral vein puncture blood vessel pressure monitoring system comprises the steps that image data, sensor data and personalized data of a patient are collected; generating an image feature vector, a sensor feature vector and a personalized feature vector based on the preprocessed multi-modal data, carrying out feature interaction on a multi-modal feature line, carrying out representation fusion by combining Transform with contrast learning, and generating a fusion representation vector; the maximum tolerance pressure, the extravasation risk level and the recommended injection flow rate range of the target puncture point under the current injection flow rate are predicted and evaluated in the blood vessel pressure resistance prediction model, injection suggestions and injection alarms of the target puncture point are generated, and injection parameters are automatically adjusted. According to the method, the personalized safety pressure threshold values of patients with different blood vessel conditions are accurately evaluated, and the safety and accuracy of peripheral vein puncture are remarkably improved.

The invention provides a multi-source heterogeneous data integrated coronary heart disease accurate prediction method, which comprises the following steps: acquiring various physiological signals of a patient in real time, the physiological signals comprising ST segment change characteristics and basic cycle function parameters in electrocardiosignals, and obtaining a multi-source signal data set; according to the joint data set, analyzing instantaneous fluctuation characteristics of a vascular resistance index in a postprandial hyperlipemia window period, extracting a vascular resistance fluctuation amplitude from the instantaneous fluctuation characteristics, and if it is detected that the fluctuation amplitude exceeds a preset threshold range, marking the window as a high-risk time window; if the score value exceeds a preset threshold value, triggering a coronary heart disease early warning signal according to the comprehensive risk score value in combination with a feature mode of a coronary heart disease hidden period in historical data; and storing the current high-risk time window characteristics and the blood sugar and blood fat curve change trend through a triggered coronary heart disease early warning signal to obtain structured risk archive data.