36 results about "Cardiac status" patented technology

The invention relates to a data processing method and device for a multi-parameter health monitoring sensor, and the method comprises the following steps: carrying out the continuous recording of a target user, and obtaining a multi-channel monitoring data flow; carrying out heartbeat profile description on the multi-channel monitoring data stream based on a heartbeat feature reconstructor to obtain heart activity parameters; carrying out heartbeat rule analysis on the target user based on the heart activity parameters to obtain heart working characteristics; performing heart state evaluation on the heart working characteristics to obtain heart operation data; and if the heart running data is not within the preset heart running range, performing dynamic health tracking on the target user based on the heart running data to obtain a heart health report, thereby solving the problem that the traditional heart health monitoring means mainly depends on regular physical examination and static electrocardiogram detection, and the monitoring accuracy is high. Dynamic changes and individual differences of heart activities are difficult to capture, so that potential heart risks cannot be early warned and intervened in time.

The invention relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the steps that cardiac medical image data of a target patient is collected, a standardized data set is generated through preprocessing, and a morphological and hemodynamic feature set is extracted; establishing a heart state evolution characteristic spectrum according to a characteristic dynamic evolution rule, dividing a pathological state space, and calculating the characteristic distribution density of a historically diagnosed case; acquiring real-time image data of a patient to be diagnosed, and constructing a real-time diagnosis feature vector; mapping the vector to a pathological state space, and calculating a space matching degree to generate a pathological association index; and combining the association index and the two types of feature sets to construct a heart pathology probability prediction model, outputting a pathology probability prediction value and generating a hierarchical diagnosis suggestion. According to the method, through multi-dimensional feature analysis and space matching analysis, precise and graded diagnosis of the cardiovascular diseases is realized, and an efficient and feasible technical path is provided for diagnosis of the cardiovascular diseases.

The invention relates to the technical field of medical data processing, in particular to a construction method and system for a cardiology nursing knowledge base. The system is used for performing mapping relation analysis of heart states and nursing knowledge according to heart pathology data and corresponding heart nursing data, and generating a heart state nursing relation feature matrix; performing pathological mode identification processing of heart state nursing relation characteristics on the heart state nursing relation characteristic matrix to generate a pathological mode heart state nursing relation characteristic matrix; based on the pathological mode heart state nursing relation feature matrix, an optimized prediction model of heart pathological mode and nursing information derivation is designed, and an optimized heart nursing derivation prediction model is generated; and performing intelligent feedback engine design processing of the cardiology nursing knowledge base based on the optimized heart nursing derivation prediction model so as to construct an intelligent cardiology nursing knowledge base engine. According to the invention, efficient and intelligent cardiac nursing knowledge base establishment is realized.

A system for reducing undesired treatments provided to a heart of a patient. The system includes a first sensor configured to measure at least a first characteristic of the heart of a patient and a second sensor configured to measure at least a second characteristic of the heart different from the first characteristic. An implantable cardioverter defibrillator (ICD) configured to be implanted in the patient is in communication with the first sensor and the second sensor. The ICD includes a generator configured to generate treatment energy and a lead configured to deliver the treatment energy. The generator configured to determine a cardiac status based on data from each of the first sensor and the second sensor, determine, based on the cardiac status, if a health event is occurring, and responsive to determining that the health event is an adverse health event, operate the generator to generate the treatment energy.

The invention discloses a physiological signal monitoring method and wearable equipment, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of heart state judgment for an electrocardiogram in the prior art. Comprising the steps that basic information of a user and physiological signals collected in real time are obtained, the physiological signals comprise electrocardiosignals, electromyographic signals and acceleration signals of the heart, the physiological signals are collected based on a multi-mode sensor on the wearable device, and the wearable device is fixed to the body of the user through a connecting assembly of a strip type structure; the physiological signals are subjected to multi-modal fusion based on a multi-modal fusion algorithm, physiological parameter data are obtained, a monitoring task is determined, and the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; and calling a monitoring prediction model which is matched with the monitoring task and has completed model training to perform monitoring processing on the physiological parameters and the user basic information to obtain a monitoring result, and performing feature comparison based on the monitoring result.

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.

A portable respiration and cardiac monitoring device includes a detection finger band and a wearable heart rate relay watch. The detection finger band comprises a light emission unit for emitting a detection light beam and a response light receiver unit for receiving a physiological response signal. The detection light beam enters the user's fingertip to generate the physiological response signal. The wearable heart rate relay watch is connected to the detection finger band via a connecting cable. The wearable heart rate relay watch receives the physiological response signal and includes a gravitational accelerometer. The gravitational accelerometer detects the acceleration of the wearable heart rate relay watch to generate an acceleration detection signal. A detection datum comprises the acceleration detection signal and the corresponding physiological response signal.The wearable heart rate relay watch transmits the detection data to an external device. A wearable, artificial intelligence-based system for monitoring respiration and cardiac status includes a control system that communicates with the wearable respiration and cardiac status monitoring device.

The invention discloses a heart rate regulation and control device, method and system based on light-operated otolith oscillation, and relates to the field of heart rate control. The scanning optical tweezers system comprises a laser scanning light path, an imaging light path, an illumination light path and an electric objective table; the laser scanning light path is used for generating a high-speed scanning optical potential well at the otolith of a target living body; the imaging light path comprises a high-magnification inverted objective lens imaging light path and a low-magnification bypass imaging light path which are respectively used for real-time image acquisition of a target living body and heart state monitoring of the target living body; a first illumination light path in the illumination light paths is used for illuminating an imaging view field of a target living body on the electric objective table, and a second illumination light path is used for exciting the target living body to generate a calcium ion fluorescence signal; the electric objective table is used for bearing a target living body, and the position of the target living body is moved through displacement control of the objective table. The heartbeat rhythm can be accurately regulated and controlled.

To provide a heart state display device, and the like which facilitate a behavior modification, such as, continuous cardiac rehabilitation for a patient who has suffered a heart disease including cardiac failure.SOLUTION: A heart state display device 1 includes: a reference data acquisition section 12 for acquiring biological information of a patient, which includes data related to a state of a heart of the patient, at a plurality of timings; a level setting section 13 for setting a plurality of levels indicating the heart state of the patient on the basis of the plurality of pieces of acquired biological information; an object data acquisition section 22 for acquiring new biological information of the patient; a level allocation section 23 for allocating a level based on the acquired new biological information from the plurality of levels; and a state output section 26 for outputting an illustration representing the allocated level to a touch-screen display 47.SELECTED DRAWING: Figure 1

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.

The invention belongs to the technical field of data analysis, and provides a dynamic electrocardiogram analysis method and device and terminal device.The method comprises the steps that firstly, electrocardiogram data of a to-be-detected object is obtained; generating change trend data based on the historical electrocardio data and the current electrocardio data; and analyzing the change trend data to generate event description information. According to the method, the multi-day electrocardiogram data of the to-be-detected object can be independently analyzed day by day, and the electrocardiogram analysis result of each day can be obtained; the analysis result of the whole electrocardiogram data is further analyzed, the statistical result of the development and change process of the related illness state of the to-be-detected object can be obtained, the change process of the heart state of the to-be-detected object can be visually displayed, and the electrocardiogram data analysis efficiency and accuracy are improved.

The invention belongs to the technical field of medical auxiliary supplies, and particularly relates to a meridian auxiliary therapeutic apparatus and a use method thereof. The meridian auxiliary therapeutic apparatus comprises a wearing part and a pressing main body, the wearing part can be worn on a limb, and a main control board is arranged on the wearing part; the number of the pressing bodies is multiple, the pressing bodies are arranged on the wearing body in the length direction of the wearing part in a sliding mode, monitoring probes used for monitoring the heart rate are arranged on the pressing bodies, and the monitoring probes are in signal connection with the main control panel. According to the meridian auxiliary therapeutic apparatus, the heart rate of a wearer can be monitored through the monitoring probe, the heart state of the wearer is judged according to the monitored heart rate information, and if the heart rate is not uniform, the main control panel controls the pressing body to act, so that the monitoring probe on the pressing body can press the corresponding acupuncture point of the wearer, and the therapeutic effect is improved. Therefore, the symptom of arrhythmia of the wearer is relieved, and the situation of excessive fatigue of the heart is avoided.

The application is suitable for the technical field of data processing, and provides a heart state evaluation method and device, a terminal equipment and a storage medium. The method comprises the following steps: acquiring heart rhythm sample data, wherein the heart rhythm sample data comprises sample heart rhythm features and sample labels; training a preset scoring model according to the heart rhythm sample data to obtain a heart rhythm scoring model; inputting to-be-identified heart rhythm features into the heart rhythm scoring model to obtain a heart state evaluation score. The application can solve the problems that the existing heart health prompt scheme is not intuitive, the evaluation granularity is coarse, and users are difficult to recognize the heart health degree.

The embodiment of the invention relates to the field of image analysis, in particular to a system for forming images for identifying lesions. The system for forming the images for recognizing the lesion comprises a plurality of image acquisition parts, a plurality of image processing parts and a plurality of image processing parts, wherein the image acquisition parts are used for acquiring pulse signals of a user; the wearing piece is arranged to be capable of being worn on the hand of a user and can be attached to the hand, and the multiple image acquisition pieces are arranged on the wearing piece; and the image recognition piece is arranged to receive the signals from the plurality of image acquisition pieces and process the signals to form an image for recognizing the state of the heart of the user. The wearing part is arranged to be attached to the hand of the user, so that the multiple image acquisition parts arranged on the wearing part can comprehensively and accurately collect the pulse signals of the user, and then the image recognition part can reflect the heart condition of a patient more accurately according to the images formed by the comprehensive and accurate pulse signals; and auxiliary screening based on the formed heart state image is facilitated.

The invention discloses a sudden cardiac arrest recognition, analysis and judgment system based on an infrared photoplethysmogram, and relates to the technical field of medical data processing, and the system comprises a data collection module which collects an infrared photoplethysmogram signal through an infrared photoplethysmogram sensor worn on a wrist; the signal preprocessing module is used for preprocessing the acquired signals; the feature extraction module is used for extracting time domain, frequency domain and time-frequency domain features from the pre-processed signals and carrying out data segmentation; the model training module is used for constructing and training a multi-feature fusion classification model based on a Transform architecture on the basis of the features extracted by the feature extraction module; and the state recognition module is used for carrying out heart state classification recognition on the real-time infrared photoplethysmogram signals by utilizing the trained multi-feature fusion classification module, and heart state categories comprise normal heart pulsation, pulse-free heart (similar to sudden cardiac arrest) and non-physiological (in-vitro) states. The system can effectively distinguish normal heart pulsation, pulse-free heart and non-physiological states, and obviously reduces the false alarm rate of sudden cardiac arrest.

The application discloses a non-contact electromagnetic heart monitoring method based on signal semantic deconstruction, comprising the following steps: collecting non-contact reflected electromagnetic signals to generate electromagnetic phase signal data; performing semantic bottleneck modeling to compress and retain key semantic information related to the heart to generate a latent semantic representation tensor; constructing a semantic invariant disturbance signal, which is input into an encoder together with the electromagnetic phase signal for self-supervised reconstruction; constructing an electrocardiogram semantic space and performing cross-modal semantic alignment between the latent semantic representation tensor and the electrocardiogram semantic space; outputting an enhanced latent semantic representation tensor by using an improved TSMixer model; extracting rhythm drift information based on the enhanced latent semantic representation, constructing an individual heart rhythm semantic atlas, and outputting a heart state recognition result. The application realizes semantic-level stable modeling of non-contact heart monitoring, improves the accuracy, robustness and explainability of heart rhythm recognition, and is suitable for intelligent health monitoring and remote medical scenarios.

A system and method for diagnosing cardiovascular disease is provided. The system comprises a plurality of different types of sensors used for sampling physiological parameters of a target user; and a processor for fusing the records of physiological parameters being sampled to obtain fusion information associated with the heart state of the target user, and analyzing the fusion information to predict the health state of the cardiovascular system of the target user.

Provided are an information processing method and the like capable of supporting determination of a state of a heart on the basis of feature amounts calculated from an electrocardiogram, a heart sound waveform, or a pulse wave waveform. According to the present invention, a computer acquires an electrocardiogram, a heart sound waveform, and a pulse waveform of a patient. The computer acquires: a first feature amount by a first formula using a I sound position or a II sound position obtained from the heart sound waveform, a Q wave position obtained from the electrocardiogram, and a notch position and a pulse wave rising position obtained from the pulse wave waveform; a second feature amount by a second formula using a maximum amplitude of the I sound or an area obtained by time-integrating a waveform of the I sound, or the maximum amplitude of the II sound or the area obtained by time-integrating a waveform of the II sound; and a third feature amount by a third expression using an element feature amount obtained by using the pulse wave waveform. In a case in which the first feature amount to the third feature amount are input, the computer inputs the first feature amount to the third feature amount thus acquired to a trained model trained so as to output an estimated value of an intracardiac pressure of the patient, thereby acquiring the estimated value of the intracardiac pressure of the patient.

The present application discloses a heart rate control device, method, and system based on optically controlled otolith oscillations, relating to the field of heart rate control. The method includes a scanning optical tweezers system; the scanning optical tweezers system includes a laser scanning optical path, an imaging optical path, an illumination optical path, and a motorized stage; the laser scanning optical path is used to generate a high-speed scanning optical potential well at the otolith of a target living body; the imaging optical path includes an imaging optical path of a high-magnification inverted objective lens and a low-magnification side-branch imaging optical path, which are respectively used for real-time image acquisition of the target living body and for monitoring the cardiac state of the target living body; the first illumination optical path in the illumination optical path is used to illuminate the imaging field of the target living body on the motorized stage, and the second illumination optical path is used to excite the target living body to generate a calcium ion fluorescence signal; the motorized stage is used to carry the target living body and to move the position of the target living body through stage displacement control. The present application can precisely control the heartbeat rhythm.

The invention discloses a data processing method, device, equipment and product for identifying the heart state, and the method comprises the steps: obtaining original first electrocardiogram data, and carrying out the preprocessing, and obtaining second electrocardiogram data; further generating a Gramer angle sum field matrix, a Gramer angle difference field matrix and a Markov transfer field matrix, and respectively generating corresponding two-dimensional images; generating a three-channel image based on the three two-dimensional images; and inputting the three-channel image into a pre-trained neural network model to obtain a heart state recognition result. According to the method, a multi-modal fused three-channel image is generated on the basis of two-dimensional images generated by the Grubrum angle and field matrix, the Grubrum angle difference field matrix and the Markov transfer field matrix respectively, and automatic recognition of the heart state is realized by means of a neural network model; the method has the characteristics of sufficient input image expression information, accurate recognition, high automation degree and wide application range.

A cardiac life support method for adapting a basic cardiac life support device to deliver an advanced cardiac therapy to a patient. The method involves establishing communication between the basic cardiac life support device and an advanced cardiac therapy adapter when the basic cardiac life support device is connected a patient whereby the cardiac life support adapter derives the advanced cardiac therapy from data representative of the cardiac status of the patient received from the basic cardiac life support device, and the cardiac life support adapter manages a delivery by the basic cardiac life support device of the advanced cardiac therapy to the patient.

The present invention relates to the field of medical data processing technology, and in particular to a method and system for constructing a cardiology nursing knowledge base. The system is used to: analyze the mapping relationship between heart state and nursing knowledge based on cardiac pathology data and corresponding cardiac nursing data, and generate a cardiac state nursing relationship feature matrix; perform pathological pattern identification processing of the cardiac state nursing relationship characteristics on the cardiac state nursing relationship feature matrix, and generate a pathological pattern cardiac state nursing relationship feature matrix; design an optimized prediction model for deriving cardiac pathological patterns and nursing information based on the pathological pattern cardiac state nursing relationship feature matrix, and generate an optimized cardiac nursing derivation prediction model; design and process an intelligent feedback engine for the cardiology nursing knowledge base based on the optimized cardiac nursing derivation prediction model, so as to construct an intelligent cardiology nursing knowledge base engine. The present invention realizes the efficient and intelligent establishment of a cardiology nursing knowledge base.

The application relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the following steps: collecting target patient heart medical image data, generating a standardized data set through pretreatment, and extracting a morphological and hemodynamic feature set; a heart state evolution feature map is established according to the feature dynamic evolution law, a pathological state space is divided, and the feature distribution density of a historical confirmed case is calculated; real-time image data of a patient to be diagnosed is acquired, and a real-time diagnosis feature vector is constructed; the vector is mapped to the pathological state space, the space matching degree is calculated to generate a pathological correlation index; a heart pathological probability prediction model is constructed in combination with the correlation index and the two types of feature sets, a pathological probability prediction value is output, and a grading diagnosis suggestion is generated. Through multi-dimensional feature analysis and space matching analysis, the method realizes accurate and grading diagnosis of cardiovascular diseases, and provides an efficient and feasible technical path for cardiovascular disease diagnosis.

A learning device includes a learning unit that sets, as a myocardial activity parameter set, a set of parameters representing an approximate function when a waveform in a time section included in a waveform for one cycle indicating a cardiac cycle of a heart is approximated by an approximate function including a difference or a weighted difference of a cumulative distribution function, and performs learning of an estimation model that obtains heart state information that is information representing a state of a heart corresponding to the myocardial activity parameter with a myocardial activity parameter set as an input, by using a learning data set, in which the learning data set includes a plurality of (Y) pieces of learning data, and each of the plurality of (Y) pieces of learning data includes a myocardial activity parameter set of a heart that is a target of y-th learning data, and heart state information that is information representing a state of the heart that is a target of the y-th learning data, where each of integers greater than or equal to 1 and less than or equal to Y is y.

The present invention discloses a data processing method, device, equipment and product for identifying cardiac state, including: obtaining original first electrocardiogram data and preprocessing to obtain second electrocardiogram data; further generating a Gram angle sum field matrix, a Gram angle difference field matrix and a Markov transfer field matrix, and respectively generating corresponding two-dimensional images; generating a three-channel image based on three two-dimensional images; inputting the three-channel image into a pre-trained neural network model to obtain the recognition result of the cardiac state. The present invention generates a multimodal fused three-channel image based on two-dimensional images generated by the Gram angle sum field matrix, the Gram angle difference field matrix and the Markov transfer field matrix, and realizes automatic recognition of cardiac state with the help of a neural network model, and has the characteristics of sufficient input image expression information, accurate recognition, high degree of automation and wide application range.

The invention discloses an application of plasma mtDNA in predicting congenital heart disease related to gestational hyperglycemia, and the application comprises the following steps: detecting the copy number of plasma mtDNA of offspring, and comparing the copy number with a preset threshold value to predict the risk that the offspring suffers from the congenital heart disease related to the gestational hyperglycemia. According to the invention, animal experiments find that plasma mtDNA level change has clear correlation with fetal congenital heart disease induced by hyperglycemia in gestation period for the first time, show good stability and consistency in the animal experiments, and can objectively reflect the heart state; therefore, the prediction method for reflecting the occurrence risk of the fetal congenital heart disease under the hyperglycemia state in the gestation period is established by taking the plasma mtDNA as the biomarker, can be applied to basic research, risk assessment and related technology development of heart dysplasia related to the hyperglycemia in the gestation period, and has relatively high practical value and popularization significance.

This application provides a multi-source cardiac state assessment system based on adaptive weight allocation. The system includes: a signal quality assessment module for real-time calculation of the signal quality index of electrocardiogram (ECG), photoplethysmography (PPG), and echocardiography signals; a user motion state recognition module for identifying the user's current motion state; an adaptive weight allocation engine for dynamically allocating fusion weights for the ECG, PPG, and echocardiography signals in the cardiac state assessment based on the signal quality index and the user's motion state; and a cardiac state trend score generator for calculating a comprehensive trend score based on the weighted fusion features. The comprehensive trend score reflects the overall state and trend of cardiac function. This technical solution achieves continuous, personalized, and highly robust monitoring and risk warning of cardiac function.

A portable heart monitor includes a detecting unit to output a detecting light beam and receive a physiological response signal, wherein the physiological response signal is generated by the reaction of the detecting light beam and a finger of a user. The physiological response signal includes pulse waves of a perfusion index (PI). A processing and analyzing unit processes and analyzes the physiological response signal to acquire an analysis result, which is to be shown on a prompting unit. The analysis result includes heart status information based on processing of the pulse signals of the perfusion index (PI).

The invention discloses a non-contact electromagnetic heart monitoring method based on signal semantic deconstruction, and the method comprises the following steps: collecting a non-contact reflection electromagnetic signal, and generating electromagnetic phase signal data; semantic bottleneck modeling is carried out, key semantic information related to the heart is compressed and reserved, and a potential semantic representation tensor is generated; constructing a semantic invariant disturbance signal, and inputting the semantic invariant disturbance signal and the electromagnetic phase signal into an encoder for self-supervised reconstruction; constructing an electrocardio semantic space, and performing cross-modal semantic alignment on the potential semantic representation tensor and the electrocardio semantic space; outputting an enhanced potential semantic representation tensor by using an improved TSMIX model; and rhythm drift information is extracted based on the enhanced potential semantic representation, an individual heart rhythm semantic map is constructed, and a heart state recognition result is output. According to the method, semantic-level stable modeling of non-contact heart monitoring is achieved, the accuracy, robustness and interpretability of heart rhythm recognition are improved, and the method is suitable for intelligent health monitoring and remote medical scenes.

The invention provides a heart condition evaluation system which comprises an electrocardiogram sensor, a light volume change sensor and a processing circuit. The electrocardiogram sensor is used for acquiring an electrocardiogram signal about a user. The light volume change sensor is used for obtaining a light volume change signal about a user. The processing circuit generates a cardiac evaluation result based on the light volume change signal sensed for a first time and the electrocardiogram signal sensed for a second time, wherein the first time is longer than the second time. The system can assess the heart condition for a longer time.