4292 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.

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.

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.

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.

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.

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 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 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.

The invention discloses a BMI-driven 3D organ obesity visualization system based on multi-modal fusion, and relates to the technical field of medical science, the system comprises a BMI-driven prediction model, the BMI-driven prediction model is used for establishing a quantitative mapping model of a BMI index and three-dimensional organ fat deposition, and the quantitative mapping model is used for establishing a BMI index and three-dimensional organ fat deposition. The BMI driving type prediction model comprises a multi-modal data fusion unit, a model mapping unit and a residual compensation unit. By installing the BMI driving type prediction model, precise nonlinear mapping of the BMI index and organ fat deposition is achieved, the problem that traditional single-mode prediction is large in deviation is solved, and organ fat volume prediction precision and individual difference adaptability are improved.

The invention relates to a painless gastrointestinal endoscope self-adaptive drug delivery method and system based on multi-source physiological signal fusion, and belongs to the technical field of medical artificial intelligence and automatic control. An artificial intelligence model is utilized to deeply extract features and quantitatively evaluate the sedation-analgesia level of a patient, a pharmacokinetics-pharmacodynamics model constructed by a big data platform is utilized to dynamically predict the drug effect according to the individual features and real-time state of the patient, and the anesthetic infusion rate is automatically and accurately adjusted through a closed-loop control algorithm, so that the patient can be accurately infused. Personalization and self-adaption of drug delivery are achieved, an independent safety monitoring mechanism is set, and real-time intervention is conducted on risk events such as respiratory depression. The problems that traditional anesthesia depends on artificial experience, single parameter monitoring is not comprehensive and individualized dynamic adjustment is lacked can be effectively solved, and the safety, comfort and medical efficiency of painless gastrointestinal endoscopy are remarkably improved.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

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

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention discloses a vertebral artery dissection risk assessment method and system, and relates to the technical field of risk assessment, and the method comprises the steps: collecting ultrasonic dynamic blood parameter data of a vertebral artery of a patient; based on the ultrasonic dynamic blood parameter data, constructing a local blood flow kinetic energy disturbance time sequence diagram by adopting a hemodynamics time sequence analysis method, and performing time sequence prediction to generate a blood flow function disturbance prediction result; dynamically adjusting a risk score threshold by adopting a reinforcement learning algorithm, combining the adjusted risk score threshold with a blood flow function disturbance prediction result, and generating a personalized vertebral artery dissection risk score according to a risk score calculation model; and generating a vertebral artery dissection risk assessment result by using a multi-modal fusion mechanism in combination with the personalized vertebral artery dissection risk score and the multi-source heterogeneous medical feature set. According to the method, the patient static characteristics and the future disturbance trend serve as input, the optimal risk score threshold value is dynamically selected, and the personalized score is generated according to the disturbance threshold exceeding proportion and time weight information.

The invention relates to a coronary artery risk prediction method and system for a coronary heart disease patient, and aims to solve the limitation that a traditional static evaluation model is difficult to integrate multi-source dynamic data. Collecting multi-source heterogeneous data of a patient from a hospital information system, a gene database and wearable equipment; the method comprises the following steps: respectively extracting key features through a special processing module: analyzing unstructured text and image plaque features by adopting a deep learning model, calculating a multi-gene risk score, capturing a physiological parameter attenuation trend by utilizing time sequence analysis, constructing a three-stage cascade integration model, screening high-contribution features at the first stage, and constructing a three-stage cascade integration model; the secondary stage integrates space-time dynamic characteristics through an attention mechanism network, the final stage integrates static and dynamic factors to generate a dynamic risk score, a visual risk trend chart and a high-risk factor thermodynamic diagram are output, and real-time early warning and linkage are performed through a medical terminal to generate a personalized intervention scheme. According to the method, collaborative analysis and dynamic risk tracking of multi-dimensional data are realized, and the early warning capability and the clinical decision-making efficiency are remarkably improved.

A medical apparatus for a patient comprises an external system and an implantable system. The external system is configured to transmit one or more transmission signals, each transmission signal comprising at least power or data. The implantable system is configured to receive the one or more transmission signals from the external system, and to deliver stimulation energy to the patient. Methods of delivering stimulation energy are also provided.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

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 invention belongs to the technical field of aquaculture and food engineering, and relates to a fish anesthesia recovery activity prediction method based on acoustic signal time-frequency domain feature fusion. The method comprises the following steps of: acquiring acoustic respiratory vibration signals in real time in a fish anesthesia recovery process, performing 0.1-50Hz band-pass filtering, denoising and other pretreatment on the acoustic respiratory vibration signals, and extracting ZCR and Gammatone characteristic spectrums; meanwhile, fish swimming data are collected by adopting a YOLOv11 visual tracking algorithm, and activity grading standards are established by calculating activity indexes; zCR and Gammatone maps are used as double-branch input data of a deep learning model, corresponding activity grading data are used as labels, and a multi-category classification prediction model is constructed. According to the method, the anesthesia recovery state of the fish can be accurately predicted, and excessive stress and death loss in the transportation process are avoided; a non-invasive and intelligent detection means is provided for activity monitoring in the aquatic product transportation process, and the market application prospect is wide.

The invention relates to the technical field of physiological parameter monitoring, in particular to a construction method of a cardiology rehabilitation evaluation model, which comprises the following steps: monitoring a systolic pressure peak value, a diastolic pressure valley value and duration time of a postoperative patient, constructing a waveform parameter set, calculating a peak sequence amplitude change rate through a moving average method, and calculating a peak sequence amplitude change rate. Marking a form abnormal period and a structure drift region; dividing an active phase and a passive phase to extract wave crest characteristics; analyzing time sequence difference to generate a three-dimensional rhythm vector; constructing a regression model; according to the method, heart pressure change is continuously monitored, a waveform structure is identified, compression period change is dynamically captured, a structure drift area is determined and positioned by combining an amplitude change rate and a difference value, multi-dimensional heart rhythm characteristics are extracted, day and night differences are analyzed, and multi-day deviation values are integrated to generate a scoring system; the abnormal recognition, rhythm quantification and trend prediction capabilities are improved, and a high-adaptability quantification basis is provided for rehabilitation evaluation.

The invention relates to the field of heart rate monitoring, in particular to an exercise heart rate monitoring method and device based on a smart watch. The method comprises the following steps: acquiring geographical location information of a user based on a smart watch, and performing user motion scene recognition so as to generate a motion scene type; original heart rate detection parameters are collected, time sequence heart rate feature analysis is carried out, and a heart rate feature set is constructed; performing motion mode depth identification on the motion scene type and the heart rate feature set to obtain a user motion mode; global heart rate situation perception is carried out on the heart rate feature set, and a global heart rate situation map is constructed; and performing comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the motion mode of the user. The heart rate state of the user is adaptively analyzed, and the exercise safety assessment efficiency and accuracy of the user are improved.

The invention discloses a cerebral hemorrhage postoperative gastrointestinal hemorrhage prediction method based on an LGBM model. The method comprises the steps of obtaining a multi-dimensional clinical feature sequence of a target patient, screening out a stable feature subset by adopting a Boruta feature selection algorithm, performing nonlinear relation fitting and integrated decision by utilizing a pre-trained LightGBM machine learning model, and generating an individualized ATH risk probability value; and when the risk probability value exceeds a dynamic risk threshold value, triggering a high-risk early warning signal, and based on a Kaplan-Meier survival analysis model, carrying out association mapping on a prognosis track of poor long-term neural function recovery, and finally generating a comprehensive prediction report. According to the invention, accurate quantitative evaluation of ATH risk is realized, clinical intervention timeliness is improved through a dynamic threshold early warning mechanism, short-term complication risk and long-term function prognosis are organically combined, and a comprehensive and reliable prognosis basis is provided for individualized treatment decision.

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention relates to the technical field of medical prediction, in particular to a digital twinborn prediction method for patient lung protective ventilation supported by ECMO, which comprises the following steps: extracting flow velocity change and jump points under the support of ECMO, constructing a ventilation fluctuation trend map, identifying fluctuation sections by combining compliance and pressure value ratio, delimiting lung partitions and analyzing oxygenation ratio trend. And generating an oxygenation capability classification graph, sequencing ventilation path effects, and outputting a pulmonary protective ventilation digital twinborn prediction scheme. According to the method, dynamic response mapping of ventilation behaviors is established on the basis of timing sequence changes of breathing flow velocity jump characteristics and compliance pressure value ratios, the lung regional ventilation state is accurately recognized, lung function zoning division is achieved by combining oxygenation ratio waveform classification, ventilation path utility sorting is completed through oxygenation descending characteristics under path coverage, and the lung function evaluation accuracy is improved. And a path state prediction sequence is constructed by fusing the change relationship between the tensile stress and the ventilation pressure, so that the dynamic adaptability of the ventilation strategy to the lung mechanics and oxygenation state and the region matching precision are improved.