104 results about "Fetal heart" patented technology

The invention relates to the technical field of medical health data intelligent processing, in particular to a gestational diabetes patient family doctor management method and system based on artificial intelligence, and the method comprises the steps: 1, collecting patient data through a dynamic glucometer, a fetal heart monitor and a hospital information system; 2, aligning the multi-source data according to timestamps to construct a time sequence data set; 3, generating a complication risk score containing a ketoacidosis risk and a giant child risk; 4, generating a personalized sugar control scheme based on the risk score and a pre-constructed medical knowledge graph; 5, integrating opinions of multiple parties to generate a final edition scheme; and step 6, updating deep learning model parameters based on feedback data. According to the mode, the treatment efficiency and accuracy are improved, the control scheme can be adjusted in real time, and the health and safety of the patient in the pregnancy period are ensured. In addition, a feedback mechanism can continuously optimize the system, a closed loop is formed, and the management effect is further improved.

The invention discloses a fetal heart rate signal data enhancement method based on a conditional diffusion model, and the method comprises the steps: firstly obtaining and preprocessing a fetal heart rate signal, and obtaining a fetal heart rate signal containing noise; then, a noise prediction model based on conditional diffusion is constructed, the noise prediction model based on conditional diffusion comprises a conditional auxiliary information coding module and a noise prediction network module, and the fetal heart rate signal data containing noise are used for training the noise prediction model based on conditional diffusion; and finally, denoising the fetal heart rate signal by using the trained noise prediction model based on conditional diffusion. According to the method, the defects of a traditional generation model in the aspects of training stability, mode diversity and signal quality are effectively overcome, high-quality and high-fidelity data support is provided for fetus health condition evaluation, and a referable mode is provided for a data generation task of a time sequence signal.

The invention relates to the technical field of obstetrical fetal heart monitoring, in particular to an obstetrical fetal heart intelligent monitoring method and system, and the method comprises the following steps: S1, data collection: collecting fetal heart signals through wearable equipment; s2, data preprocessing: acquiring fetal heart monitoring parameters; s3, self-adaptive adjustment of the personalized physiological state: dynamically adjusting fetal heart monitoring parameters by applying a self-adaptive algorithm; s4, data analysis and prediction, wherein the adjusted fetal heart monitoring parameters are analyzed, and the health problem of the fetus is predicted; s5, dynamic multi-mode signal interaction analysis: identifying relevance among multi-mode signals, performing abnormal mode detection, and performing comprehensive evaluation; and S6, visualization and user interaction: developing a user interface based on Web and mobile applications. According to the invention, the relevance among various signals can be comprehensively captured and analyzed, the abnormal mode can be accurately identified, and the accuracy and reliability of fetal health monitoring can be improved.

Techniques for real-time doppler ultrasound signal processing techniques for fetal heart rate (FHR) detection with supervisory error control are provided. In an example, a method comprises receiving, by a system comprising a processor, signal data as the signal data is captured via an ultrasound transducer during a fetal monitoring session using doppler-based ultrasound technology. The method further comprises determining, by the system using a first process, first FHR values for a fetal heart targeted by the ultrasound transducer based on the signal data as the signal data is received, and determining using a second process in parallel with the first process, second FHR values for the fetal heart based on the signal data as the signal data is received. The method further comprises determining, by the system, whether the first FHR values are associated with an error based on differences between the first FHR values and the second FHR values.

The invention relates to the technical field of medical image artificial intelligence, in particular to a multi-prototype cross-modal contrast learning fetal heart ultrasound image intelligent diagnosis system which comprises the steps that a to-be-detected fetal heart ultrasound image is obtained through an obtaining module, and a processing module inputs the to-be-detected fetal heart ultrasound image into a pre-trained image diagnosis model; the method comprises the following steps: performing feature extraction on a to-be-detected fetal heart ultrasound image, performing cross-modal multi-prototype comparison according to a feature extraction result to obtain a diagnosis category and a most matched image-text prototype pair, outputting the diagnosis category and the most matched image-text prototype pair by an output module, and finally performing CHD classification and screening according to the diagnosis category and the most matched image-text prototype pair. The problem that the model generalization ability is insufficient due to the fact that a single prototype center is mainly relied on to represent each category and the significant difference of the same category under different conditions is neglected is solved, and multi-category CHD intelligent screening is achieved by utilizing unlabeled images and a small amount of text priori on the premise that a large number of graph-text pairs are not needed.

The application discloses a kind of fetal heart three-dimensional ultrasound reconstruction methods based on radio frequency identification signal, the device used includes a medical two-dimensional color ultrasound machine, two RFID passive tags, an RFID signal reader connected with three receiving antennas, and PC for RFID signal data processing and three-dimensional color ultrasound reconstruction;RFID reader transmits radio frequency signal and receives RFID passive tag reflection signal, after correcting the phase deviation of receiving signal, the distance between phase determination antenna and RFID passive tag is used, the position and elevation of RFID passive tag are estimated, the moving track of ultrasonic probe is determined, finally the position and angle information extracted by two-dimensional ultrasonic probe are input into three-dimensional ultrasound reconstruction model together with the obtained two-dimensional ultrasonic image, and accurate fetal heart three-dimensional ultrasound is obtained.The application can realize lower cost and higher accuracy, and is especially suitable for fetal heart detection.

1. The name of this design product: Fetal heart rate probe. 2. Purpose of the product of this design: The fetal heart probe is used to form a fetal Doppler with other components of the fetal Doppler to detect the fetal heart rate. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The utility model provides a parturient delivery skirt which comprises an upper garment and a skirt body, a first zipper is sewn in the middle of the front face of the upper garment, second zippers are sewn on the two sides of the upper garment, a plurality of button holes are formed in the bottom of the upper garment, buttons are sewn on the inner top of the skirt body, the skirt body is connected with the upper garment through the buttons and the button holes, and an opening is formed in the position, located at the chest, of the front face of the upper garment. The first zipper is a two-way zipper, so that the first zipper is pulled upwards to facilitate fetal heart detection of a pregnant woman, the first zipper is pulled downwards to facilitate contact between a newborn and a mother in the early stage, the newborn can be directly fed by the pregnant woman through the formed opening, and the opening can be blocked by the blocking piece, so that the pregnant woman feels comfortable. By means of the second zipper, the pregnant woman can be anesthetized without taking off the delivery clothes, during delivery, the pregnant woman can pull up the first zipper upwards or directly lift up the skirt body for delivery, the pregnant woman does not need to take off the delivery skirt, and the risk that the pregnant woman gets frozen and gets ill due to clothes taking off is reduced.

The application provides an inductive fetal heart monitor, which comprises a fetal heart monitor body and a probe, wherein the fetal heart monitor body comprises a power module, a main control module, a signal source switching module, a grounding signal module and a fetal heart output module; the main control module is connected with the signal source switching module; the grounding signal module and the fetal heart output module are both connected with the signal source switching module; the probe comprises a probe body, a sensor one and a sensor two; the probe body is connected with the signal source switching module; the sensor one and the sensor two are arranged on the probe body; and the sensor one and the sensor two are both connected with the main control module. The two sensors and the signal source switching circuit are added to the probe, the signal source switching circuit is controlled by an MCU, the two signal sources of the signal source switching circuit, i.e. the fetal heart signal or the grounding signal, are switched and output, and the two sensors are used to solve the problems of reading and loud noise during the fetal heart searching process when the fetal heart monitor is not used correctly.

The invention discloses a double-fetal monitoring method based on abdominal electric signal state dynamic conversion analysis, which comprises the following steps of: acquiring abdominal electric signal data of a double-fetal pregnant woman containing all age groups and physical conditions in a clinical environment, and constructing a double-fetal monitoring reference data set through preprocessing and signal separation; and designing an abdominal electric signal state dynamic conversion analysis algorithm suitable for double-fetal monitoring. And constructing a state dynamic conversion template of the source signal in state dynamic conversion analysis, and generating a double-tire position marking data set. And obtaining state labeling time sequence data of state dynamic conversion based on the state dynamic conversion template, constructing a double-fetal electrocardiosignal extraction scheme, and calculating the fetal heart rate. And visualizing a state dynamic conversion time sequence chart and marking real-time fetal heart rate and mother heart rate to realize dynamic monitoring of the state of the double fetuses. According to the invention, the limitation that the fetal heart signals are confused and the independent electrocardiosignals of the two fetuses are difficult to accurately extract during the existing monitoring of the two fetuses is solved, and the real-time performance and the reliability of the clinical monitoring of the two fetuses are improved.

The application provides a 3D fetal heart standard section identification method and system, electronic equipment and a storage medium. The method comprises the following steps: generating a Gaussian noise image based on Gaussian distribution, inputting the Gaussian noise image and a 3D fetal heart image into an image conditional latent U-Net network model for feature fusion; inputting the fused features into a variational autoencoder (VAE) to generate a 2D standard section of the 3D fetal heart image; and inputting the 2D standard section into a regression model to obtain a 2D standard section of the 3D fetal heart image. The application generates a Gaussian noise image based on Gaussian distribution, performs noise processing on the photographed 3D fetal heart image, and generates a 2D standard section. The generated 2D standard section is generated based on the 3D fetal heart image and has a certain correlation with the 3D fetal heart image. The 2D standard section generated in this way is used as a reference section, and the finally obtained 2D fetal heart section is more standard.

The utility model discloses a fetus ECG monitor probe fixing device which comprises a probe, a data line and a fixing device body. The probe is connected with the fetal heart monitor through a data line and installed on the fixing device. The fixing device comprises a waist protection pad, an elastic band I and an elastic band II; the number of the first elastic bands and the number of the second elastic bands are both two. One ends of the two elastic belts I and the two elastic belts II are respectively mounted on the left side and the right side of the waist protection pad in a scissor-shaped manner; the other ends of the two first elastic bands are provided with hook-and-loop fastener female faces. The other ends of the two second elastic bands are provided with hook-and-loop fastener son faces corresponding to the hook-and-loop fastener mother faces. Sliding grooves are formed in the two elastic belts I; the probe is installed on the sliding groove in a sliding mode. A positioning device is arranged on the probe; the positioning device is used for fixing the relative position of the probe on the chute; according to the utility model, a monitored object can wear independently and comfortably, and cannot slide due to sweating and movement, so that the monitoring result is accurate.

The invention discloses a fetal heart ultrasound image segmentation method and system based on deep learning, and belongs to the technical field of medical images. Channel-level intelligent weighting is achieved by means of an independent SE module, and semantic conflicts are solved; a pixel-level spatial weight map is generated, spatial-level accurate fusion is realized, and the spatial dimension accuracy is improved. According to the method, morphological semantic features are modeled in a unified mode by means of a region-level feature aggregation framework, the dynamic change rule of heart tissue is learned through region-level shared feature extraction and a pixel-level residual refinement cooperation mechanism, and compared with a previous method, boundary segmentation is clearer, training is accelerated by several times, and clinical diagnosis efficiency and accuracy are improved. The two-dimensional intelligent fusion module and the multi-scale double attention residual feature enhancement module work cooperatively, the PFFM accurately positions edge feature points, the SAFF intelligently adapts and fuses semantic features, and the multi-scale double attention residual feature enhancement module analyzes and captures morphological semantic information hierarchically.

Disclosed is a blood oxygenator for a premature fetus comprising a housing defining a cavity therein, wherein the housing comprises a blood inlet, a blood outlet, a gas inlet, and a gas outlet; and a gas exchanger disposed within the cavity, wherein the gas exchanger comprises a plurality of hollow fibers arranged in a pattern allowing blood to flow past the hollow fibers when in contact with the hollow fibers, wherein the hollow fibers comprise a receiving end in communication with the gas inlet for receiving a sweep gas and an emitting end in communication with the gas outlet from which the sweep gas exits, wherein the oxygenator is designed to rely on the fetal heart as sole blood pump.

A mechanism for automatically generating and ranking M-mode lines for generating or defining M-mode data usable to assess fetal heart activity, e.g. determine a fetal heart rate. A region of interest, containing a fetal heart, in a sequence of ultrasound images is identified. The region of interest is used to define the position of each of a plurality of M-mode lines, e.g. anatomical M-mode lines. A quality measure of each M-mode line is determined based on M-mode data generated for each M-mode line, and the quality measures are then used to rank the M-mode lines.

The invention discloses a maternal and child internet-of-things equipment data transmission safety protection and abnormity monitoring method and system, and belongs to the technical field of medical internet-of-things safety, and the method comprises the steps: S10, carrying out risk monitoring on related data, including fetal heart monitoring data, dividing the fetal heart monitoring data into fetal movement data, uterine contraction pressure data and fetal heart rate variation data; and S20, risk monitoring is carried out, related information corresponding to the fetal movement data, the uterine contraction pressure data and the fetal heart rate variation data is obtained, the related information comprises text features and byte capacity, and the fetal movement data, the uterine contraction pressure data and the fetal heart rate variation data tampered by an attacker are collected from the text features and the byte capacity. According to the method, data tampering attacks can be effectively identified and defended, meanwhile, the integrity of high-level importance data is guaranteed, the safety of data transmission and the accuracy of anomaly monitoring are improved, and effective safety protection is provided for data protection in the field of medical Internet of Things.

The invention relates to the technical field of fetal movement simulation, and discloses a portable fetal movement simulation and health assessment integrated system which comprises a wearing part fixed to the body of a user through a connecting hasp, and a simulation part arranged on the side away from the body and internally provided with a fetal movement and fetal heart simulation module and a voice analysis module. The system generates fetal movement simulation actions through a predetermined instruction, collects environment voice, and extracts voice semantics and action languages. The simulation unit evaluates the health state of the fetal movement simulation on the basis of the relationship between semantics and actions. And the early warning module is arranged in the simulation part, generates early warning notification information according to the health assessment result, and gives an alarm in a sound mode when an abnormality is diagnosed, so that the integration of simulation, analysis and early warning is realized. According to the system and the method, fetal movement simulation, voice semantic analysis, health early warning and health assessment are integrated, and the intelligent interactivity of fetal movement simulation and the real-time performance and accuracy of health monitoring can be improved.

The utility model provides a household fetus-voice meter with a blood oxygen detection function, which comprises a fetus-voice meter main body, the fetus-voice meter main body is provided with a fetal heart detection piece, a controller assembly and a blood oxygen detection assembly, and the blood oxygen detection assembly comprises a pressing elastic connecting piece and a blood oxygen detection assembly. A shell connecting part and a detection pressing connecting part are arranged on the pressing elastic connecting piece, the blood oxygen detection assembly is fixedly connected with the pressing elastic connecting piece, the blood oxygen detection assembly is arranged in the detection pressing connecting part, the shell connecting part is fixedly connected with the fetus-voice meter body, and a pressing window is formed in the fetus-voice meter body. The fetal-voice meter has the advantages that the problem that in the prior art, a fetal-voice meter detects blood oxygen in a reflection mode and is poor in accuracy can be solved, by means of the product structure, when the blood oxygen detection assembly is pressed, the blood oxygen detection assembly can be sunken downwards, the elasticity of pressing the elastic connecting piece counteracts overlarge pressing force, the accuracy of a measurement result is guaranteed, and the measurement accuracy is improved. And the use experience of the user and the competitiveness of the product are improved.

The invention relates to the technical field of medical diagnosis, in particular to a multi-mode Doppler fetal monitor which comprises a TYPE-C connecting line, a uterine contraction probe and a plurality of Doppler fetal heart replaceable probes, and the Doppler fetal heart replaceable probes have different ultrasonic working frequencies. A plurality of Doppler fetal heart replaceable probes with different ultrasonic working frequencies are provided, so that the problem that a single fixed-frequency probe is difficult to adapt to all clinical scenes due to individual differences of pregnant women is fundamentally solved. Specifically, a user or a medical worker can flexibly select and replace the Doppler fetal heart replaceable probe most suitable for the current penetration depth requirement according to the real-time monitoring condition or the judgment of individual differences, so that the optimal penetration depth and signal quality can be obtained under different clinical conditions; and the defects of monitoring blind areas and unstable signals caused by fixed frequency are effectively overcome.

An exemplary embodiment of the present disclosure provides a method for producing a resorbable heart valve, the method comprising: providing a first solution comprising a polymeric material; coating at least a portion of a mold with the first solution for form a coating; forming a plurality of valve leaflets on a first end of the coating; placing a frame around at least a portion of the coating; and attaching the frame to the coating.

The invention relates to the field of medical image processing, in particular to a congenital heart disease four-dimensional echocardiogram dynamic segmentation system, which acquires four-dimensional echocardiogram data of a fetal heart through an image acquisition module, corrects fetal movement artifacts by an optical flow field correction module by using an optical flow field time sequence analysis technology, improves the image quality, and improves the segmentation accuracy of the fetal movement artifacts. The heart structure time sequence alignment module is used for aligning a heart structure and extracting a ventricular septal defect area through a multi-scale similarity transformation strategy, the defect analysis module is used for calculating the edge line diameter and the shunt direction of a defect part and generating defect parameter information, and the result output module is used for generating a congenital heart disease screening report. And a foundation is laid for subsequent analysis, and the defect part is kept at a stable position in a time sequence by accurately aligning the heart structure, so that accurate analysis is facilitated.

The application discloses a fetal distress diagnosis system based on multi-modal feature fusion of fetal heart rate signals, comprising a signal preprocessing module, a data enhancement module, an expert feature extraction module, a signal feature extraction module, a multi-modal fusion module and an output module. The application fuses expert features and signal features, effectively reduces the misdiagnosis rate of fetal distress, and provides an important basis for fetal monitoring, disease diagnosis and clinical decision-making. A SE-TCN backbone model is designed based on dilated convolution and causal convolution, SEBlock is added in the residual connection channel of TCN, the receptive field of the whole network is effectively increased, and the gradient disappearance phenomenon in the long time sequence classification task is relieved. The application proposes a cross-modal feature fusion mechanism CMFF, which utilizes a multi-head self-attention mechanism and a cross-modal attention score to measure the similarity between different modalities and realizes cross-modal feature fusion according to an adaptive weight factor.

The present invention relates to the field of image recognition technology, and particularly to a method and device for identifying left heart development features in fetal heart ultrasound. The method comprises acquiring historical ultrasound images and manually annotating them, dividing the historical images into salient images and non-salient images; constructing a deep neural network, constructing 4D ultrasound images as input to the deep neural network, and training the deep neural network using the historical images to obtain a binary classification deep neural network model for identifying whether an image is a salient image or a non-salient image; constructing image recognizers for salient images and non-salient images respectively, obtaining a focus area of the image through weakly supervised positioning in the image detector, and obtaining multiple detection frames through image detection; and using the detection frame closest to the focus area as the left heart development feature position of the image. The present invention can realize low-cost, fast, simple, and accurate identification and positioning of the left heart lesion area specified by fetal heart ultrasound.

The present application relates to the technical field of medical instruments, and discloses a display method of monitoring waveforms, a fetal monitor and a storage medium, the display method comprising: drawing a preset background grid on a display screen according to a configured paper type specification, the configured paper type specification being a paper type specification of thermal paper; obtaining fetal heart rate data from collected fetal heart data; and drawing a current fetal heart rate waveform on the display screen according to the fetal heart rate data, the current fetal heart rate waveform being drawn on the preset background grid; wherein the display result of the current fetal heart rate waveform is the same as a fetal heart rate waveform printed on the thermal paper according to the fetal heart rate data. The present application can achieve the effect of reproducing the fetal heart rate waveform on the thermal paper on the display screen, thereby improving user satisfaction.

The application discloses a fetal heart rate signal data enhancement method based on a conditional diffusion model. First, a fetal heart rate signal is acquired and preprocessed to obtain a fetal heart rate signal containing noise. Then, a noise prediction model based on conditional diffusion is constructed, the noise prediction model based on conditional diffusion comprises a conditional auxiliary information coding module and a noise prediction network module, and the noise prediction model based on conditional diffusion is trained by using fetal heart rate signal data containing noise. Finally, the trained noise prediction model based on conditional diffusion is used to realize denoising of the fetal heart rate signal. The application effectively overcomes the deficiencies of a traditional generation model in training stability, pattern diversity and signal quality, provides high-quality and high-fidelity data support for fetal health condition evaluation, and provides a referenceable pattern for a data generation task of a time series signal.

The invention relates to a delivery early warning method and system based on uterine contraction and fetal heart joint analysis, and belongs to the technical field of medical data processing. The early warning method comprises the following steps: arranging a plurality of electrode type A-type sensors on the abdomen of a pregnant woman, collecting uterine electromyographic signals, extracting a plurality of uterine activity indexes, and constructing uterine contraction behavior vectors under a time sequence; at least one magnetic intensity type class B sensor is arranged, a plurality of reference positioners are supplemented, weak magnetic field signals generated by fetal heart activity are collected and analyzed, a plurality of fetal response indexes are obtained, and a fetal response vector is constructed. The system further constructs a joint state vector based on stimulation (uterine contraction) and response (fetal heart) signals, compares the joint state vector with a pre-established normal sample reference model, calculates a deviation degree score, assesses whether the fetus has abnormal response in each time window, and outputs premature delivery or delivery risk levels in a grading manner.

The utility model relates to the technical field of fetal heart measuring equipment, and discloses a split type ultrasonic fetus-voice meter which comprises a host unit and a display screen unit which are arranged in a split mode, and the host unit is connected with the display screen unit in a wireless communication mode. And the host unit and the display screen unit are fixedly positioned and connected with each other in a magnetic attraction manner. A display screen unit carrying a display screen and a host unit carrying other parts are arranged in a split mode, the display screen unit and the host unit are combined together through magnetic force, and fetal heart signals of the host unit are transmitted to the display screen unit to be displayed in a wireless transmission mode. A user can hold the display screen unit or place the display screen unit at any position to check real-time data, fatigue caused by long-time head-lowering data checking of a pregnant woman user and inconvenience in operation in the middle and late pregnancy are avoided, user experience and monitoring efficiency are improved, and the problems that an ultrasonic fetus-voice meter in the prior art is prone to causing cervical vertebra fatigue, cable constraint and monitoring interruption are solved.

This invention provides a maternal-fetal dual-body critical value early warning bracelet based on multi-physiological parameter fusion, belonging to the field of wearable monitoring device technology. It includes a bracelet body, an elastic floating sealed maternal physiological data acquisition component, an integrated split abdominal binder acquisition assembly, a main control circuit board, a data processing module, a multi-parameter fusion critical value early warning module, a wireless communication module, and a power supply module. The bracelet and the split abdominal binder collaboratively collect multi-dimensional physiological parameters such as maternal electrocardiogram, blood oxygen, body temperature, uterine contraction pressure, and fetal heart rate. A multi-parameter fusion algorithm is used for comprehensive judgment of critical values, solving the problems of single-device monitoring and delayed early warning in traditional devices, and significantly improving the accuracy of maternal and infant risk identification.

The utility model provides a wearable pregnant woman health monitoring bracelet, belongs to the technical field of pregnant woman health monitoring, and aims to solve the problems that when an existing wearable pregnant woman health monitoring bracelet is used for fetal heart detection of a pregnant woman, the monitoring bracelet needs to be removed or a display needs to be independently detached to be tightly attached to the abdomen, and the display is small and is easily lost after being detached, so that the monitoring is inconvenient. And the dismounting process is tedious. Comprising a bracelet belt, the mounting shell is fixedly connected to the interior of the bracelet belt; the fixed block is fixedly mounted at the upper part of the mounting shell; one end of the connecting line is fixedly connected to the interior of the fixed block; the take-up device is arranged in the connecting wire; the monitor main body is fixedly mounted at one end of the connecting line; the mounting assembly is arranged in the fixing block; and the winding assembly is arranged in the take-up device. The utility model has the advantages of quick disassembly and assembly, loss prevention, convenience in use and the like.

The present application relates to the technical field of teaching training, in particular to a four-step palpation and fetal heart auscultation training method and system with adjustable fetal position; the method of the present application involves basic training and advanced training; the basic training adjusts the fetal position in a fixed order, and the single step is qualified to be locked or unqualified to be reset; the operation process is standardized, the accumulated deviation of the position and posture is eliminated; the four-step palpation is step-by-step practical operation and instant error correction, which conforms to the clinical standard; the data of the fetal position adjustment, palpation and auscultation are quantified and weighted, the operation ability is scientifically evaluated, and the pain points of the lack of standardization and the unscientific evaluation in the traditional training are solved; the advanced training constructs a dynamic fetal position model based on the clinical labor data, simulates the progressive change of the fetal position in the labor, fills in the blank of the dynamic scene, adjusts the position and posture without pre-warning, disposes the error in stages, strengthens the dynamic judgment and emergency ability of the trainee, trains in different difficulty gradients, generates a node evaluation report, and realizes the ability advancement from the basic standardization to the clinical practice.