66 results about "Heart beat" patented technology

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

The invention provides a regional anesthesia puncture path planning method and system under ultrasonic guidance, and relates to the technical field of ultrasonic image.According to the regional anesthesia puncture path planning method and system, dynamic motion information of a tissue structure is obtained by collecting ultrasonic image data of a target region and conducting motion analysis, and electrocardiosignals are synchronously collected to determine heart beat time phase information; determining a vascular movement mode based on the two, learning a time change rule by using a long-short-term memory network, and establishing an association relationship between a vascular spatial position and a heart beat phase; constructing a motion prediction model to predict the future motion trend of the blood vessel, and fusing the respiratory motion information to correct to obtain the blood vessel motion information; and finally, a safe puncture window is calculated according to the information, a regional anesthesia puncture path is planned, vascular movement prediction and safe puncture window calculation based on vascular movement rule learning and breathing correction in regional anesthesia under ultrasonic guidance can be achieved, and then the puncture path is precisely planned.

The invention discloses a magnetocardiogram P-wave detection method based on wavelet analysis and a local noise self-adaptive threshold, which is characterized in that accurate and robust detection of a P wave is realized by searching on a characteristic scale which can highlight the P wave, a self-adaptive threshold confirmation mechanism based on local noise estimation is introduced, and a self-adaptive threshold confirmation mechanism is established for a time domain search window of each cardiac cycle. According to the method, the internal noise level is dynamically evaluated, and an estimated value capable of accurately reflecting the local noise condition of the current heart beat can be obtained by carrying out statistical analysis on wavelet coefficients in a characteristic scale in an area where P waves are most likely to appear.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

The application discloses an old person free breathing coronary artery scanning image lesion analysis system and relates to the technical field of scanning image lesion analysis. In order to solve the problem that the accurate condition of a patient cannot be obtained according to a scanning image. The application adopts a diameter method, an area method and a contrast agent filling condition to judge the stenosis degree and the hemodynamic change, analyzes the lesion from the morphological and functional double angles, provides comprehensive information for clinical decision-making, identifies the R wave peak value and divides the cardiac phase, matches the respiratory signal and the projection data in time, can accurately capture the characteristics of the heart in different motion states and the respiratory stage, effectively avoids the interference of the artifacts caused by the heart beat and the respiratory motion, makes the reconstructed image clearer and more accurate, sets the CT and the injector parameters in sequence from the scanning type confirmation, each link is closely related and the target is clear, improves the work efficiency, and is convenient for quality control and process management.

The present application relates to the field of respiratory heart rate monitoring, and discloses a non-contact heart rate and respiration recorder, which comprises a host computer, a wireless charging base, a charging cable and a terminal device, wherein the host computer is internally provided with a non-contact sensor module, a processor, a battery and a wireless communication module, and the shell adopts a waterproof sealing structure. In use, the host computer is placed under a pillow, and a heart beat and respiration channel signal is collected through the non-contact sensor, and after filtering, normalization and artifact removal, the signal forms a heart beat observation and a respiration observation under the action of parameters such as a sampling rate, a time window length, a vertical offset and a coupling stiffness, and is subjected to joint optimization under the constraints of spectral matching and cross-modal coherence, and finally outputs a heart rate and a respiration rate. The device does not need to wear an electrode, is stable in detection, and is suitable for long-term home health monitoring and clinical assistance.

An apparatus includes a photoplethysmography (PPG) sensor configured to measure a PPG signal associated with a user, an accelerometer configured to measure an acceleration signal associated with the user, and a processor operatively coupled to the PPG sensor and the accelerometer. The processor is configured to determine whether the user is engaging in movement based on the acceleration signal, in response to determining that the user is engaging in movement, determine a type of movement of the user based on the acceleration signal, generate a probability distribution associated with a pulse rate of the user based on the type of movement, and determine the pulse rate of the user based on the PPG signal, the acceleration signal, and the probability distribution.

Embodiments of the present application relate to a kind of electrocardiosignal processing method and device, the method includes: receiving first electrocardiosignal;Filtering processing generates second electrocardiosignal;Baseline drift elimination processing is respectively carried out to first, second electrocardiosignal, and third, fourth electrocardiosignal is generated;R point identification processing is carried out to fourth electrocardiosignal, and first R point sequence is generated;Electrocardiosignal segment intercepting processing is carried out to third, fourth electrocardiosignal, and first, second electrocardiosignal segment sequence is generated;Heart beat classification processing is carried out to first electrocardiosignal segment sequence, and first segment type sequence is generated;Interference classification processing is carried out to second electrocardiosignal segment sequence, and second segment type sequence is generated;Segment type fusion processing is carried out to first, second segment type sequence, and third segment type sequence is generated;First R point sequence and third segment type sequence are as electrocardiosignal processing result output.The processing efficiency of electrocardiosignal processing can be improved in the present application and the stability of processing quality is guaranteed.

The present application relates to magnetic resonance examination device technical field, specifically to a kind of cardiac magnetic resonance examination device, including fixed plate and electrocardiogram control patch, fixed plate is equipped with chest coil;Fixed plate is detachably connected with detection plate, detection plate is equipped with breathing training component for exerting chest pressure, breathing training component is electrically connected with control panel;Control panel records and draws fluctuation curve based on the pressure data detected by breathing training component, then obtains the breathing interval of patient based on fluctuation curve, sends breath-holding reminding instruction to voice module according to breathing interval;Detection plate side away from fixed plate is equipped with several electrocardiogram monitoring electrode pieces for detecting the quality of heart beat signal strength, detection plate is equipped with driving mechanism for driving electrocardiogram monitoring electrode piece to move in range;The present application is used to assist patient breathing breath control, realize the accurate matching of patient breathing breath opportunity and imaging sequence, reduce the generation of breathing artifact, guarantee the progress of cardiac magnetic resonance.

The application discloses a method and device for arrhythmia analysis based on heart beat classification, equipment and medium, wherein the method comprises: generating a morphology amplitude coding vector of a target heart beat segment as a target morphology amplitude coding vector, the target heart beat segment being any one of the heart beat segment time series set; performing inner product calculation on the target morphology amplitude coding vector and each heart beat template vector in a preset heart beat template vector set to obtain an inner product value set; determining a target heart beat type corresponding to the target heart beat segment according to the inner product value set; and performing arrhythmia analysis on each target heart beat type corresponding to the heart beat segment time series set to obtain an arrhythmia analysis result. The application avoids the technical problems that the traditional method may have a large amount of calculation, ignores the amplitude difference of the waveform and easily ignores the morphology difference of the waveform when the amplitude is low, has simple steps and small calculation amount, and is thus suitable for real-time monitoring scenes.

1. Name of the designed product: running shoes (2405P031 heart beat). 2. Use of the designed product: the designed product is used for shoes. 3. Design points of the designed product: combination of shape and pattern. 4. Picture or photo best indicating the design points: perspective view.

The present application relates to the technical field of pattern recognition, in particular to a blood glucose detection region selection method based on blood perfusion imaging, comprising collecting continuous laser speckle original images of a to-be-detected skin region and a peripheral pulse wave signal of a subject, and constructing a bottom layer data matrix. The image sequence is converted into a perfusion contrast evolution sequence by calculating the ratio of the standard discrete degree and the average brightness of the pixel intensity in the three-dimensional data block. Frequency locking operation is performed using the main frequency of the pulse wave to separate out the perfusion fluctuation component synchronized with the heart beat rhythm, and the phase lag distribution map of each pixel point is determined through cross-correlation time delay analysis. The tissue damping coefficient of each pixel point is obtained by combining the tissue viscoelasticity kinetic damping model inversion, and the pixel set in the target response interval is screened and clustered according to the mapping relationship between the blood glucose concentration and the damping coefficient to determine the blood glucose detection target region. The present application realizes the locking of the detection region through tissue damping characteristic inversion and clustering screening.

This invention provides a deep learning (DL) model for fast deformable image registration using 2D sagittal cine MRI acquired during radiation therapy. A DL model for fast deformable image registration is trained using cine MRI scans acquired during MR-Linac treatments of thoracic and abdominal tumors. The model uses a pair of cine MRI images as inputs and outputs a dense motion vector field (MVF) which aligns the images. The trained model is applied to predict frame by frame motion from cine MRIs in which both cardiac and respiratory motion are visible. The number of respirations and heart beats is automatically extracted by performing peak detection on high-frequency and low-frequency components of the MVF displacements corresponding to the chest wall and cardiac regions.

The invention discloses a household heart beat detection system based on multi-feature fusion and adaptive optimization, and the system achieves the stable collection of non-contact BCG signals through a dual-mode piezoelectric sensor array and a signal collection subsystem of a wireless transmission module. In order to solve the problem of weak anti-interference capability caused by dependence on a single feature in a traditional method, a multi-dimensional feature extraction scheme fusing a first-order derivative extreme point, a second-order derivative inflection point and a wavelet high-frequency feature is provided; the dynamic threshold detection subsystem is used for completing preliminary positioning of heart beats, the density clustering optimization subsystem is combined for filtering wrong heart beats of noise interference, a particle swarm optimization algorithm is used for conducting self-adaptive optimization on a heart beat template, and finally high-precision heart beat detection is achieved through intelligent template matching. The non-contact design adapts to various special crowds, the robustness and accuracy of heart beat detection are remarkably improved through the multi-feature fusion and self-adaptive optimization algorithm, and the method is suitable for time-history heart beat monitoring scenes of parents.

The application relates to an inter-board heartbeat monitoring method and system, which comprises the following steps: determining the access state of a sub-board on a main board; based on the determined access state, performing heartbeat count verification on each sub-board once per heartbeat monitoring period for all accessed sub-boards; wherein the heartbeat count verification comprises: sending main board information for requesting sub-board feedback of sub-board information to the accessed sub-board, wherein the sub-board information comprises sub-board heartbeat count; in the case of receiving the sub-board information, judging whether the sub-board heartbeat count falls between a minimum critical heartbeat count and a maximum critical heartbeat count; in the case of not falling therebetween, increasing the verification error count and judging whether the verification error count is greater than a fault tolerance threshold; and in the case of the verification error count being greater than the fault tolerance threshold, determining that the sub-board is abnormally running. The heartbeat monitoring of the main board on the sub-board is realized through inter-board communication, which is convenient to implement and low in cost, and has universality.

The invention discloses a row-column addressing area array flexible ultrasonic transducer integrated with electrocardiogram monitoring and a preparation method thereof.An ultrasonic function unit takes a flexible circuit board as a core carrier, integrates independent piezoelectric array elements, a top flexible electrode layer and a flexible acoustic matching layer which are distributed in rows and columns, and is controlled through a row-column addressing electrode array; and the number of the leads is reduced from N2 to 2N. The electrocardio monitoring unit comprises electrocardio sensing electrodes which are integrated together. The package structure covers the non-functional surface to provide protection. The ultrasonic array and the electrocardio sensing electrode are conformally integrated on the same flexible substrate, so that synchronous and homologous monitoring of mechanical movement and electrophysiological activity of the heart is realized. Due to the flexible design, the transducer can be adaptive to the curved surface of the chest of a human body and tolerate deformation caused by cardiac pulsation and respiration, and the accuracy, stability and comfort in wearable long-term dynamic heart monitoring are remarkably improved.

The application discloses a kind of multi-channel sensing fusion systems for exposure stress judgment, it is related to sensing fusion technical field, for solving the problem of insufficient accuracy of stress exposure state judgment, by the monitoring equipment corresponding to the working individual, the respiratory rhythm, the heart beat interval, the skin electricity response and the action trajectory such as multi-dimensional physiological and behavioral parameters in the working process are continuously collected, the multi-channel stress state observation basis is constructed, the feature extraction and abnormal discrimination of each channel data are carried out, the state mode is classified modeling using pattern recognition method, combined with cross-channel consistency analysis and causal order constraint, the abnormal response is comprehensively evaluated, when the evaluation result meets the preset stress exposure determination condition, combined with action trajectory characteristics, the stress exposure grade of working individual is graded determination, and according to the determination result, continuous monitoring or event record and information upload operation is selected, realize the automation, real-time judgment of stress exposure state.

The invention discloses an intelligent assessment method and system for coronary stenosis based on chest radiography, and relates to the technical field of health risk assessment, and the method comprises the steps: obtaining chest radiography image data of a target patient; based on the time sequence fluctuation of the calcification focus area in the chest radiograph image data, calculating to obtain a calcification activity index, determining a cardiac pulsation key point in the chest radiograph image data, and based on the displacement change of the cardiac pulsation key point between each image frame, calculating to obtain a collateral circulation compensation index of the calcification focus area; based on the collateral circulation compensation index and the calcification activity index, calculating a dynamic risk factor corresponding to the coronary artery stenosis; and inputting the dynamic risk factor into a preset neural network model for iterative training, and performing prediction processing on the chest radiograph image data based on the trained preset neural network model to obtain a coronary artery stenosis risk assessment result. The technical effect of reducing the clinical misjudgment risk of coronary artery stenosis is achieved.

The invention discloses an electrocardiosignal identification method based on a diffusion model and Bi-LSTM (Bidirectional Long Short Term Memory). The method comprises the steps that firstly, an electrocardiosignal is preprocessed, and wavelet transform is used for conducting noise reduction on the electrocardiosignal; then learning real electrocardiosignal data characteristics by using a diffusion model; the method comprises the following steps of: firstly, acquiring heart beat data, then utilizing a convolutional neural network and a Bi-LSTM feature extractor to learn heart beat features, finally, inputting the learned features into a full connection layer, and then utilizing softmax to obtain the probability that the heart beat data belong to a corresponding category, thereby realizing classification of electrocardiosignals. Finally, in order to detect the heart state of the patient in real time, the fully trained classification model is stored and migrated to a cloud end, then signals, collected by electrocardiosignal collecting equipment in real time, of the patient are input into the model, whether the heart rhythm of the patient is normal or not is judged, a result is fed back to a user, and therefore the patient can conveniently see a doctor and treat the patient in time.

This disclosure is directed to systems and techniques for determining an evidence level of an atrial tachycardia (AT) episode based on heart beat intervals in the cardiac activity data over a pre-determined time period. Based on a determination that the evidence level indicates relatively regular heart beat intervals, the example techniques apply a first set of AT detection criteria and indicate a detection of an AT episode based on satisfaction of at least one of the first set of AT detection criteria. Based on a determination that the evidence level indicates relatively irregular heart beat intervals, the example techniques apply a second set of AT detection criteria and indicate a detection of an AT episode based on based on satisfaction of at least one of the second set of AT detection criteria.

The invention relates to the technical field of electrocardiosignal analysis, and discloses an autonomic nerve function evaluation method based on a phase whole sequence signal averaging technology, and the method comprises the steps: selecting a plurality of deceleration anchor points and / or acceleration anchor points from a plurality of sequence values of a heart beat interval sequence of a first to-be-detected object; for each deceleration anchor point / acceleration anchor point, selecting a deceleration sequence segment / acceleration sequence segment corresponding to each deceleration anchor point / acceleration anchor point from the heart beat interval sequence of the first to-be-tested object; averaging values with the same sequence in the plurality of deceleration sequence segments / acceleration sequence segments to obtain a deceleration signal sequence / acceleration signal sequence of the first to-be-detected object; performing power spectral density analysis on the deceleration signal sequence / acceleration signal sequence of the first to-be-detected object to obtain a deceleration index / acceleration index of the first to-be-detected object; and evaluating the autonomic nerve function of the first to-be-tested object according to the deceleration index and / or the acceleration index of the first to-be-tested object. The autonomic nerve function can be evaluated more accurately.

The invention relates to a pair of myocardial biopsy forceps, which comprises a sleeve assembly, a catheter, a handle assembly, a transmission part and a negative pressure adsorption assembly, the near end of the catheter is connected with the handle assembly, the far end of the catheter is connected with the sleeve assembly, a rotary cutter is arranged in the sleeve assembly in a sliding mode, and the rotary cutter is a hollow tubular cutter; a transmission part is arranged in the catheter, the near end of the transmission part is connected with the negative pressure adsorption assembly, the far end of the transmission part is connected with the rotary cutter, and the transmission part is used for driving the rotary cutter to stretch out of the sleeve assembly and rotationally cut myocardial tissue; an adsorption tube is arranged in the transmission part, the negative pressure adsorption assembly is communicated with the near end of the adsorption tube, and the far end of the adsorption tube is communicated with an inner cavity of the rotary cutter, so that negative pressure adsorption positioning is carried out on the myocardial tissue before rotary cutting and in the rotary cutting process, and the tissue is kept tensioned. According to the myocardial biopsy forceps, myocardial tissue is positioned and tensioned in advance through the negative pressure adsorption assembly, crossing of passive clamping orientation and active adsorption is achieved, and the problems that in the heart beating environment, biopsy forceps are prone to slipping off, and sampling and positioning are difficult are effectively solved.

The application provides an impedance cardiogram feature point detection and quality control method based on waveform subtypes, and the dZ / dt signal, the base impedance Z0 signal and the electrocardiogram ECG signal of an impedance cardiogram ICG are synchronously collected; the dZ / dt signal is pretreated, a heart beat segment is intercepted, a quality feature vector is extracted for each heart beat segment, and a quality score is calculated; the comparative learning loss is used for training, and the homologous enhancement view is narrowed; the streaming prototype clustering is performed; the point-level uncertainty of each feature point is calculated, if the point-level uncertainty exceeds a set threshold, then the constrained relocation is performed; the low-confidence counter is established, and the low-confidence closed-loop control is realized based on the quality score and the point-level uncertainty. The application can trigger the closed-loop control such as a prompt or calibration in a continuous low-confidence state, and improves the robustness and availability of the ICG feature point detection and the blood dynamics parameter estimation in a real scene.

The application discloses a kind of based on chest radiograph coronary artery stenosis intelligent evaluation method and system, it is related to health risk assessment technical field, the method includes: obtaining the chest radiograph image data of target patient;Based on the time sequence fluctuation of calcification focus area in chest radiograph image data, the calcification activity index is calculated, determine the heart beat key point in chest radiograph image data, based on the displacement change between each image frame of heart beat key point, the collateral circulation compensation index of calcification focus area is calculated;Based on collateral circulation compensation index and calcification activity index, the dynamic risk factor corresponding to coronary stenosis is calculated;Dynamic risk factor is input to the preset neural network model and is iteratively trained, and based on the preset neural network model after training is completed to chest radiograph image data is predicted to process, obtains coronary stenosis risk assessment result.The application reaches the technical effect of reducing the clinical misjudgment risk of coronary artery stenosis.

The invention relates to a device, system and method for determining a respiration rate of a subject. The presented device comprises an input interface for obtaining a red and / or infrared photoplethysmography, PPG, signal, a feature signal unit configured to extract a first feature sequence from the PPG signal and to generate a first feature signal from the feature sequence, a resampling unit configured to resample the first feature signal with a resampling function to produce a resampled signal with a constant sampling rate and / or to resample a first derivative to produce a resampled first derivative with a constant sampling rate, a transformation unit configured to compute a Fourier transform of the resampled signal to produce a transformed resampled signal or to compute a Fourier transform of the resampled first derivative to produce a transformed resampled first derivative, and an extraction unit configured to extract a first respiration rate from the transformed resampled signal or from the transformed resampled first derivative, wherein the first feature sequence is a sequence of phases of a first harmonic of a heart beat of the subject with respect to a start of the heart beat, and wherein the first feature signal corresponds to the sequence of phases.

The invention relates to the technical field of medical equipment, and provides a heart beat signal identification method and device of a temporary pacemaker and electronic equipment. The heart beat signal identification method comprises the following steps: acquiring a data signal of a time window with a set length, and identifying the positions of a wave crest and a wave trough with the maximum amplitude in the section of signal; calculating an amplitude difference between the wave crest and the wave trough as a first amplitude V-pp, and recording a time interval between the wave crest and the wave trough as an actual window length T-actual; mapping the actual window length T-actual into a standard time window through amplitude standardization transformation to obtain a standardization amplitude Vn; and comparing the standardized amplitude with a preset sensing sensitivity threshold value to realize heart beat signal identification. According to the invention, the contradiction between excessive perception and insufficient perception can be solved at the same time, and the perception reliability of the temporary pacemaker in various clinical environments is improved.

The application relates to the technical field of electrocardiosignal analysis, and discloses a long-sequence electrocardiosignal disease recognition system based on a Transformer architecture; the application overcomes the core defects of fixed window segmentation in traditional electrocardio analysis, such as damaging waveform integrity, neglecting lead spatial topological relationship and low efficiency of long-sequence modeling, the P-QRS-T waveform structure is completely reserved through a heart beat self-adaptive segmentation technology, and the risk of fixed window truncation is eliminated; the lead anatomical topological relationship and space-time coding are fused, and multi-lead electro-physiological correlation is modeled; long-sequence efficient processing is realized based on hierarchical sparse Transformer, the recognition sensitivity of complex pathologies such as arrhythmia and myocardial ischemia is improved; gradient directional regulation and flow processing mechanism are combined, the clinical real-time demand is met while the diagnostic accuracy is ensured, and finally, the light-weight deployment is adapted to a mobile terminal, thereby providing reliable, efficient and universal intelligent decision support for early warning of heart diseases.

The invention discloses a continuous blood pressure signal quality evaluation method and device, equipment and a medium, and relates to the technical field of signal monitoring. The method comprises the steps that PPG signals and ACC signals synchronously collected by a local sensor are obtained; calculating a cross-correlation characteristic between the PPG signal and the ACC signal after preprocessing; constructing an artifact intensity index based on the cross-correlation characteristics, the preprocessed PPG signal and the preprocessed ACC signal; inputting the PPG signal, the ACC signal and the artifact intensity index into a preset quality evaluation model, performing fusion analysis on the PPG signal and the ACC signal based on the artifact intensity index to judge whether the PPG signal is used for blood pressure estimation or not, and outputting a corresponding quality evaluation result. According to the method, artifacts, pulled by actions, of waveforms are recognized through cross-modal PPG-ACC cross-correlation, heart rhythm stability and artifact intensity indexes are captured to provide artifact priori, complex artifacts can be accurately recognized, and quality evaluation of blood pressure signals can be achieved through low-cost wearable equipment.

The invention relates to the technical field of physiological monitoring, in particular to a mental state real-time feedback system based on intelligent wearable equipment, which comprises a sequence construction module, a weight analysis module, a weight correction module, an index generation module and a hierarchical judgment module. According to the method, the multi-dimensional breathing fluctuation sequence containing the amplitude difference of the inspiration section, the duration of the expiration section and the change of the breathing rate is constructed, the multi-dimensional breathing fluctuation sequence and the heart beat interval sequence are mapped into the same multi-dimensional space, the Euclidean distance between the multi-dimensional breathing fluctuation sequence and the heart beat interval sequence is calculated to serve as the dynamic weighting coefficient, and the coupling strength between the cardiopulmonary systems can be quantified; therefore, the adjustment state of the autonomic nervous system is disclosed more deeply, state misjudgment possibly generated due to the fact that breathing activities cannot be associated when heart rate changes are analyzed in an isolated mode is avoided, meanwhile, the system can actively detect electrocardiosignal baseline drift and breathing waveform abnormal fluctuation, and the weighting coefficients are screened and corrected based on the interference degree.

The application provides a left ventricular end-diastolic pressure calculation method, device, computer equipment and storage medium, comprising: collecting the current and outlet pressure value of a catheter pump, obtaining a heart beat cycle, an inlet pressure value, an aortic valve opening time and an aortic valve closing time; determining a heart diastolic time according to the heart beat cycle, the aortic valve opening time and the aortic valve closing time; determining a current diastolic pressure value according to the outlet pressure value; determining a left ventricular pressure minimum value according to the inlet pressure value; determining an isovolumic relaxation time constant according to the inlet pressure value and the aortic valve closing time; determining an isovolumic contraction efficiency according to the inlet pressure value and the aortic valve opening time; and calculating the left ventricular end-diastolic pressure based on the heart diastolic time, the current diastolic pressure value, the left ventricular pressure minimum value, the isovolumic relaxation time constant and the isovolumic contraction efficiency. The above method can safely and accurately predict the left ventricular end-diastolic pressure.