3135 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 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 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 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 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.

The invention discloses a lung injury evaluation system based on a vascular endothelial cell protection effect, and the system comprises a sample processing module which is configured to execute sample preprocessing and multi-dimensional biomarker collection operation; the feature calculation module is connected to the sample processing module and is configured to perform endothelial function core index quantification and feature extraction operation; the data fusion module is connected to the feature calculation module and is configured to execute lung injury degree and endothelial protection efficiency association mapping operation; and the strategy generation module is connected to the data fusion module and is configured to execute a dynamic risk assessment and assessment strategy generation stage. The method has the following advantages and effects: systematic fusion and dynamic analysis of multi-source heterogeneous data are realized, and quantitative association mapping between biomarkers and iconography features is established, so that the comprehensiveness and predictive ability of state evaluation are remarkably improved.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention discloses an anesthesia depth accurate prediction system and method based on time sequence alignment. The method comprises the steps that a cross-modal joint time sequence characteristic data matrix is acquired and generated; outputting a time sequence alignment feature sample; outputting a future advanced prediction label set; obtaining a first-stage anesthesia depth prediction model; in the first anesthesia depth prediction model training stage, a logic auditing link is introduced, and a self-adaptive online updating model is formed; starting an anti-fact dose simulation module, and outputting a candidate anti-fact administration scheme set; and inputting the candidate anti-factual administration scheme set into a dynamic security constraint optimizer, rejecting non-compliant schemes by the dynamic security constraint optimizer according to single maximum dose limitation, single additional dose and administration times in a minimum administration interval, and outputting an optimal administration suggestion. According to the method, combined classification judgment is carried out on the anesthesia state grade and the change trend of each prediction step, and the sensitivity of clinical risk identification and abnormal situation early warning is effectively improved.

The invention relates to the field of bile monitoring and analysis, in particular to a dynamic bile monitoring and analysis method and device based on real-time image recognition. The method comprises the following steps: continuously collecting a bile monitoring image sequence of a drainage bag of a patient, and carrying out global image resolution optimization and multi-frame color drift analysis to obtain a multi-time-point bile color evolution sequence; performing comprehensive bile physical state vector modeling according to the bile monitoring image sequence, performing personalized bile state characteristic evolution, and constructing a bile color-property comprehensive characteristic spectrum; and performing deep color component visual detection based on the multi-time-point bile color evolution sequence, performing pathological concentration change mining, and constructing a concentration anomaly evolution model. According to the method, the real-time bile state characteristics of the patient are accurately analyzed based on precision visual identification, the risk level and deterioration prediction are combined, the dynamic adjustment of the precise early warning time point is realized, and the response speed and the early warning accuracy are improved.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

The invention provides a respiratory tract phlegm blockage monitoring method and device, and belongs to the technical field of respiratory tract phlegm blockage monitoring, and the monitoring method specifically comprises the following steps: obtaining respiratory tract multi-mode biophysical signals of a target domain patient and a plurality of source domain patients; the method comprises the following steps: acquiring a target domain signal through a micro sensor array, and constructing an optimal source domain set by using a signal processing algorithm formed by a coding module and a classification module; inputting a target domain signal and the optimal source domain set into a global adapter, extracting shallow layer features through a feature extraction module, and realizing conditional distribution alignment through a global adaptation unit; edge distribution alignment is guided through adversarial training containing a gradient inversion layer; and the discriminator and the global adapter parameters are fixed, and a classifier is trained to identify and evaluate the sputum blockage state. According to the method, the sputum blockage monitoring accuracy is improved through multi-mode signal processing and confrontation training.

The invention provides a health state prediction method for an 8K display device, and relates to the technical field of display health prediction.The health state prediction method comprises the steps that a multi-modal feature vector is extracted from each pixel node of a multi-modal frequency spectrum image, a mutual information feature vector of the multi-modal feature vector is calculated, and subtle changes of cooperative work among different physical attributes in the nodes are captured; on the basis, each pixel node is further regarded as a node in a pixel node grid, the connection weight is quantized by calculating the norm of a mutual information feature vector of the pixel node and obtaining the similarity between adjacent nodes, then the attenuation rate of the connection weight of a continuous time window is calculated, the distribution entropy value of the attenuation rate is analyzed, and a stability degradation entropy index is obtained; trivial connection change information is condensed into a quantitative index representing the overall orderliness decline degree of the system, then time sequence prediction is carried out on an entropy index historical sequence, the expected time of the entropy index historical sequence reaching the dynamic control upper limit is calculated, and the crossing from current state monitoring to future trend prediction is achieved.

Disclosed are valve replacement devices, systems, and methods. Valve replacement devices may comprise one- or two-piece systems comprising a receiver body (also called an adapter) and a valve assembly with replacement leaflets attached to and located within the receiver body. In two-piece systems, the valve assembly may be removable from the receiver body such that both can be delivered together or separately, and the receiver body may remain implanted while the valve assembly may be removed and replaced. Also described are devices, systems, and methods related to delivering, removing, and replacing a valve replacement. Such delivery methods may include transseptal insertion of a new minimum leaflet structure, and securement of the valve replacement using several securement type (e.g., supra-annular, sub-annular, radial, leaflet securement, etc.). Also described is a braided helical design that mimics the heart's natural movement, and a flange structure for assisting the functioning of the valve replacement.

A method for predicting a cardiac index (CI) for a patient includes receiving a set of a measurements from the patient that characterizes an anatomical condition, a physical condition, a physiological condition, or a combination thereof. The method also includes receiving a non-invasive physiological measurement from the patient. The method also includes performing feature extraction on the non-invasive physiological measurement to produce one or more extracted features. The method also includes combining the set of measurements and the extracted features to produce a dataframe. The method also includes predicting the CI for the patient. The CI is predicted using a prediction model based upon the dataframe.

The invention provides an injury condition simulation system and method, and belongs to the technical field of computers, the system comprises a trauma simulation device, a blood vessel chip and a processing unit, the trauma simulation device can apply different injury signals to a carrier where the blood vessel chip is located so as to simulate different injury conditions of a to-be-tested object; the blood vessel chip is arranged in the corresponding carrier, can simulate the blood circulation condition in an organ corresponding to the carrier, and can detect blood data in the carrier; the processing unit is used for acquiring blood data detected by the blood vessel chip under different injury conditions, and predicting the injury evolution condition of the to-be-detected object based on the blood data. Therefore, the development condition of the injury condition can be predicted under the condition of realizing real-time collaborative simulation of the multi-part injury.

Apparatus and methods are described including an impeller (50) that includes a proximal bushing (64) and a distal bushing (58). Two or more helical elongate elements (52) extend from the proximal bushing (64) to the distal bushing (58), and an axial structure (54) is disposed inside of the two or more helical elongate elements (52), and along an axis around which the helical elongate elements (52) wind. The impeller (50) includes an impeller-overexpansion-prevention element (72). The impeller-overexpansion-prevention element is a single integrated structure that includes a ring (73) disposed around the axial structure (54), and a plurality of elongate elements (67) each of the elongate elements (67) extending from the ring to a respective helical elongate element (52) and being coupled to the respective helical elongate element (52) so as to prevent radial expansion of the impeller (50). Other applications are also described.

The invention discloses a cardiovascular and cerebrovascular disease risk prediction method and system based on multiple modalities, and particularly relates to the technical field of medical prediction.The method comprises the steps that frequency domain decomposition is conducted on all modal signals of standardized time sequence data, and rhythm component time sequence signals corresponding to a preset physiological frequency band are extracted; performing Hilbert transform on the rhythm component time sequence signal to obtain an instantaneous phase of the rhythm component time sequence signal; based on the instantaneous phase difference between different modal rhythm components, stability quantification is carried out to obtain the phase synchronization intensity; constructing a multi-modal physiological rhythm synchronous dynamic network by taking the rhythm components as nodes and taking the phase synchronization intensity as a connecting edge weight; according to the method, through deep mining of multi-physiological-mode time sequence data in a continuous time window, phase synchronization dynamic changes among different-mode physiological rhythms are accurately captured, and the multi-mode physiological rhythm synchronous dynamic network construction and variation connected edge identification are combined; the normal fluctuation of the physiological system and the abnormal signal at the early stage of the disease can be effectively distinguished.

The invention relates to a simulation-based target removal area determination method and device. The method comprises the following steps: acquiring a heart simulation model and corresponding blood flow field information; according to the left heart lumen model and the blood flow field information, determining a first target area in the ventricular septum model; stress calculation is carried out according to the ventricular septum model, and a second target area in the ventricular septum model is determined; determining an initial target area according to the first target area and the second target area; according to the ventricular septum model, determining the thickness range of the initial target area at the end of contraction; determining a target cutting thickness according to a preset basic thickness and a thickness range; according to the heart simulation model, the initial target area, the target resection thickness and the blood flow field information, a first target resection area is determined. By adopting the method, the cardiac muscle tissue area needing to be cut off can be accurately determined.

The present disclosure relates to a vascular closure device adapted for use in closing a puncture in a blood vessel after e.g. a percutaneous interventional procedure. The disclosure also relates to a method for vascular closure using such a vascular closure device.

The invention discloses an external diaphragm pacemaker regulation and control method and device based on a respiratory radar. The method comprises the following steps: acquiring a chest radar echo signal of a target; analyzing the radar echo signal to obtain a target breathing signal; analyzing the target breathing signal to obtain breathing parameters, and generating a synchronizing signal; the treatment parameters are adjusted by adopting the breathing parameters and the synchronizing signals; the treatment parameters are adopted to output control signals; and regulating and controlling the external diaphragm pacemaker based on the control signal. The breathing state of the target is detected and sensed in real time through the breathing radar, key parameters are extracted, and the opportunity and intensity of electrical stimulation are dynamically adjusted based on the key parameters, so that personalized synchronization with the natural breathing rhythm is realized, and the treatment efficiency and the user comfort are improved.

The invention relates to the technical field of audio signal processing and biomedical signal analysis, and still has a further optimized space for the recognition of anti-noise requirements, signal individual differences and complex pathological modes in a noise environment. The invention provides a double-path CNN heart sound classification method based on time-frequency and double-spectrum fusion features, and the method comprises the steps: carrying out the preprocessing of an original heart sound signal of a data set which is classified into a normal heart sound and an abnormal heart sound, and obtaining a to-be-recognized heart sound signal; based on dynamic continuous wavelet transform, adaptively selecting parameters to extract time-frequency characteristics, introducing bispectrum analysis, capturing nonlinear characteristics, generating a dual-channel characteristic pattern, and efficiently storing the dual-channel characteristic pattern in an HDF5 format; and based on a designed double-path convolutional neural network structure, respectively processing the extracted time-frequency and double-spectrum features, performing classification after fusion, and training a model in combination with category weighted loss and an optimization strategy to obtain a heart sound classification result. The heart sound recognition accuracy can be improved.

The invention discloses an underground breathing flow monitoring method and system based on adaptive filtering, and the method comprises the steps: collecting an airflow signal at the mouth and nose of a miner in real time through an MEMS hot film sensor, and synchronously collecting a body vibration signal through an acceleration sensor, and obtaining original breathing data containing dynamic interference and corresponding vibration data; the method comprises the following steps: preprocessing original breathing data containing dynamic interference and corresponding vibration data to obtain a digital breathing flow signal and a standardized reference vibration signal; jointly inputting the digital respiratory flow signal and the standardized reference vibration signal into a filtering processing model based on an LMS adaptive algorithm to obtain a target respiratory flow signal; performing data analysis on the target respiratory flow signal, and calculating to obtain a real-time respiratory flow value, respiratory frequency and tidal volume parameters of the monitored object; the real-time breathing flow value, the breathing frequency and the tidal volume parameters obtained through calculation are locally displayed in real time, and monitoring of breathing flow data during underground operation is achieved.

The invention discloses an abnormal heart beat detection method and system based on a magnetic cardiogram, and relates to the technical field of medical data processing, and the method comprises the steps: carrying out the preprocessing of original magnetic cardiogram data; magnetic cardiogram features are extracted based on discrete wavelet transform, model parameters are adjusted, and an abnormal heart beat detection model is obtained; and judging whether abnormal heart beat exists or not and the position. According to the method, the problem that the magnetocardiogram data lacks a large number of heart beat level labeling samples is effectively solved, dependence on labeling data is reduced, automatic, efficient and accurate detection of abnormal heart beats is achieved, the good clinical application prospect is achieved, and powerful support can be provided for early screening, auxiliary diagnosis and risk assessment of arrhythmia.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

Systems and methods for blood flow control are described herein. In some variations, a blood flow control system may comprise a blood flow control device. The blood flow control device may be placed within a body of a patient and may comprise an expandable member and a sensor. The sensor may be configured to measure at least one of a physiologic condition of the patient and a pressure associated with the expandable member. The blood flow control system may include at least one controller communicably coupled to the sensor to: receive data indicative of at least one of the physiologic condition of the patient and the pressure associated with the expandable member from the sensor, compare the received data with target data, identify at least one error based on the comparison, and in response to identifying the error, inhibit at least one function of the blood flow control system.

1. Name of the product in this design: Wearable Dynamic Electrocardiogram Recorder (ECG-D01H02). 2. Purpose of this design: It is a wearable dynamic electrocardiogram recorder used to monitor and record the electrocardiogram waveform of a human body. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.