142 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 uterine contraction automatic interpretation system based on network characterization, which comprises a network model, and the network model comprises a CV module, an AT module, an SW module and a TC module which are connected in sequence; the CV module encodes low-level spatio-temporal information in the CTG signal through three convolutional layers; the AT module comprises a plurality of self-attention layers; the TC module uses a time convolution network to extract advanced time features in a time sequence, and inputs the advanced time features into a full connection layer with a Sigmoid classifier. According to the method, a multi-representation and multi-model integration thought is adopted, related features beneficial to CTG interpretation are fully extracted, the problem that a manual feature extraction algorithm is insufficient in capturing a long-time-span dependency relationship is solved, subsequent various analysis and research on CTG signals are facilitated, and the method is suitable for popularization and application. More accurate and reliable diagnosis information is provided for doctors and applied to the field of medical signal processing, and a more accurate and reliable auxiliary diagnosis tool is provided for clinicians.

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 medical ultrasonic image processing, in particular to a fetal heart standard section detection method based on multiple tasks, which comprises the following steps: acquiring a to-be-detected fetal heart section image, inputting a target detection model, outputting types, quantity and positions of anatomical structures in the to-be-detected fetal heart section image, and classifying the to-be-detected fetal heart section image based on a classification algorithm. And obtaining a judgment and classification result of the fetal heart section image to be detected. On the basis of a target detection model path fusion feature pyramid, a self-adaptive spatial feature fusion module containing sampling operation and small-size feature map fusion branches and an interleaving path are added, and a path interleaving pyramid network based on the self-adaptive spatial feature fusion module is constructed as a neck network of a target detection model. And meanwhile, a detail fusion enhancement module introducing an optimized feature fusion strategy is utilized, so that the recognition precision of a key anatomical structure in the fetal heart ultrasonic image and the accuracy of section judgment and classification results are improved.

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.

A method and apparatus of examining fetal hearts includes obtaining ultrasound data of a fetal heart to generate fetal heart images, displaying different marked sample images of a fetal heart and at respective cardiac phases, each marking a different feature of a fetal heart at a respective marked location, and sequentially presenting one of the different marked sample images to a user along with one of the fetal heart images that is a similar view. As each of the different marked sample images is sequentially presented, a user is prompted to mark the different feature in a respective fetal heart image based on its marked location to create a plurality of marked fetal heart images, and a geometric model of the heart is generated based on the marked fetal heart images, from which fetal echocardiography views can be extracted. The fetal echocardiography views of the fetal heart are displayed.

The utility model discloses an FD-600 low-noise fetus-voice meter filter circuit. The FD-600 low-noise fetus-voice meter filter circuit comprises a passive band-pass filter circuit, three groups of active low-pass filter circuits, a logarithmic amplifier circuit, two groups of proportional amplifier circuits and an audio signal amplifying and shaping circuit. According to the utility model, the input audio signal is filtered and amplified for many times, so that clutter interference in the audio signal is filtered, and the signal is amplified between the filter circuits, so that the condition that the filtered signal is weak and difficult to capture and further causes distortion is avoided, noise is effectively reduced, and meanwhile, the noise is reduced. The clarity of fetal heart audio signals is improved, and medical staff can conveniently recognize and judge the audio signals detected by the fetal heart meter.

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 invention provides a fetal heart rate signal classification method based on a space-time diagram convolutional network. The method comprises the following steps: acquiring and preprocessing a fetal heart rate signal; node feature attributes are extracted from the fetal heart rate signals, and an initial static graph is generated; constructing a dynamic graph based on the initial static graph and the self-adaptive adjacency matrix, updating information propagation of nodes in the dynamic graph on each time step, and generating an updated dynamic graph; and constructing an optimized space-time diagram convolutional network model, inputting the updated dynamic diagram into the optimized space-time diagram convolutional network model, and outputting a classification result of the fetal heart rate signals. According to the method, a dynamic graph mechanism is introduced, a traditional space-time graph convolutional network is optimized, space dependence and time dependence between nodes are effectively captured, cross-time-dimension information transmission is achieved, the pathological causality of FHR data is deduced, and the accuracy of FHR signal classification is improved.

The invention provides a fetal hypoxia early warning method and device combining health conditions and fetal heart rates, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing a fetal heart rate signal of a target fetus to generate target fetal heart rate signal sequence information; the target fetal heart rate signal sequence information is processed on the basis of a Gaussian second wavelet function, a target wavelet transformation diagram is generated, and the target wavelet transformation diagram is used for representing time-frequency information of the fetal heart rate signal of the target fetus; processing the physiological parameter information of the pregnant woman and the PH value information of the target fetus based on a target fetal hypoxia diagnosis model to generate a target physiological state feature; processing the target wavelet transform diagram based on the target fetal hypoxia diagnosis model to generate target image features; and processing the target physiological status features and the target image features based on the target fetal hypoxia diagnosis model to generate fetal hypoxia information.

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.

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 relates to the technical field of fetal heart detection, and particularly discloses an antenatal fetal heart detection method and system for simulation teaching. The method comprises the following steps of: establishing a movement track model comprising a plurality of nodes through a preset standard curve, discretizing student operation into simulated fetal heart searching nodes, and jumping walking to generate a first weight through a node matching detection step; secondly, when a preset frequency signal is detected, recording timestamps of a simulation capture area and a final detection point, judging the positioning efficiency through duration analysis, and if the positioning efficiency exceeds a preset range, generating a second weight; and finally, combining the basic proficiency score and the dual weight to comprehensively calculate a detection result. According to the method, visual tracking of operation steps is achieved through node discretization, a step integrity and operation timeliness dual verification mechanism (identifying leapfrogging violation and positioning time consumption abnormity) is utilized, operation normalization is quantitatively evaluated in combination with timestamp data, and the operation proficiency of a user can be comprehensively, objectively and accurately evaluated.

The invention provides a fetal heart monitoring probe detection device which comprises a base, stand columns are fixedly connected to the four ends of the upper portion of the base, a top plate is fixedly connected to the tops of the stand columns, a movable frame is movably connected to the stand columns, a rack is fixedly connected to one end of the outer side of the movable frame, and a fixed frame is fixedly connected to one end of the bottom of the base. The top of the fixing frame is fixedly connected with a third motor, and the output end of the third motor penetrates outwards and is fixedly connected with a third gear. The clamping assembly is arranged on the movable frame so that fetal heart monitoring probes of different sizes can be positioned and clamped, and follow-up detection work can be conveniently carried out; and a plurality of groups of detection assemblies are arranged at the outer end of the base and comprise sound wave detection, pressure detection and appearance detection, so that the fetal heart monitoring probe is detected from multiple aspects, and an authoritative, comprehensive and accurate detection result is achieved.

1. The name of this design product: integrated fetal heart rate monitor. 2. Purpose of this product design: used for monitoring fetal heart rate. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the key points of the design: main view.

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.

1. Name of the Design Product: Fetal Heart Monitor (Obstetrics and Gynecology). 2. Use of the Design Product: Used to measure the fetal heart rate of pregnant women. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective View 1.

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.

1. Name of the Design Product: Fetal Heart Monitor (Wireless Split). 2. Use of the Design Product: This design product is used to monitor the fetal heart rate. 3. Design Highlights of the Design Product: The combination of shape and pattern. 4. Picture or Photograph that Best Illustrates the Design Highlights: Perspective view.

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 embodiment of the invention provides a prenatal fetal monitoring method and device, equipment, a storage medium and a program product. The method comprises the steps that a prenatal fetal monitoring device extracts corresponding class diagram areas from historical monitoring data according to an abnormal list and forms a class diagram initial list; and the antenatal fetal monitoring device traverses the class diagram initial list and combines the class diagram areas with the incidence relation to generate a class diagram final list. And the antenatal fetal monitoring device sorts and outputs the class diagram areas according to the class diagram weights. The method is used for achieving the effects of improving the comprehensiveness and the automation level of fetal heart monitoring and the accuracy of abnormal recognition.